{ "metadata": { "generated_at": "2025-12-26T10:00:00.000Z", "version": "1.0", "total_posts": 232, "migrated_posts": 108, "fresh_posts": 26, "posts_with_images": 44, "posts_with_videos": 7, "youtube_extraction_date": "2025-12-10T09:22:58Z", "posts_with_youtube": 1 }, "posts": [ { "id": "trump-trades-reports-oct2026", "slug": "trump-trades-reports-oct2026", "title": "What Has Trump Been Buying and Selling? His Latest Trade Reports, Extracted and Downloadable", "date_published": "2026-10-05T14:55:00.000Z", "date_updated": null, "last_modified": "2026-10-05T14:55:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/trump-trades-reports-oct2026", "thumbnail": "/images/blog/trumpTradesOct2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "database-ai" ], "summary": "President Trump's OGE trade reports through the one posted 22 September 2026, extracted into an interactive tool and CSV download on TREMOR: 19 reports with 10,100 trades worth $948 million to $3.12 billion, a shift from mostly municipal bonds to 95% stocks since May 2026, the biggest 2026 trading days in Microsoft, Amazon and Meta, and the 2025 annual disclosure with 21,285 trades. Notes on value ranges and how the scanned reports were read.", "twitter_status": "posted", "tweet_id": "2107123320290517295", "tweet_url": "https://x.com/amarHarolikar/status/2107123320290517295" }, { "id": "tigzig-agent-data-surface-oct2026", "slug": "tigzig-agent-data-surface-oct2026", "title": "Explore the Tigzig Data Surface With Your AI: What Is New in October 2026", "date_published": "2026-10-05T06:30:00.000Z", "date_updated": null, "last_modified": "2026-10-05T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-agent-data-surface-oct2026", "thumbnail": "/images/blog/tigzigAgentDataSurfaceOct2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "mutual-funds", "vigil", "portfolio-quants" ], "summary": "October 2026 update on the Tigzig data surface for AI agents, reachable at api.tigzig.com. New: mutual fund scheme details (60 fields for about 2,100 funds), 26 new global macro indicators including US jobs, Japan 10-year yield and India IIP and WPI on the new base, 29 more cricket SQL functions, and FY26 related party transactions. Notes on fund_dedup_flag, AUM versus AAUM, TER matching, the October 2025 shutdown gap and links to API error guides.", "twitter_status": "posted", "tweet_id": "2106988501707440297", "tweet_url": "https://x.com/amarHarolikar/status/2106988501707440297" }, { "id": "us-jobs-september-2026-update-oct2026", "slug": "us-jobs-september-2026-update-oct2026", "title": "US Jobs, September 2026: Fewer New Jobs, Longer Searches, Pay Barely Keeping Up", "date_published": "2026-10-04T14:30:00.000Z", "date_updated": null, "last_modified": "2026-10-04T14:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-jobs-september-2026-update-oct2026", "thumbnail": "/files/US_JOBS_SEPTEMBER_2026_UPDATE/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "The US added 29,000 jobs in September 2026, and the 12 months to August were revised down from 827,000 to 543,000, a fourth straight year of downward first-report revisions, the longest run in records back to 1956. Health care carries job growth, unemployment is 4.2 per cent, spells of unemployment are longer, more claimants exhaust benefits, and hourly pay has trailed prices since April. Eleven-page deck rating 11 measures against 2023 and 2018-19. Personal analysis.", "twitter_status": "posted", "tweet_id": "2106754041678356560", "tweet_url": "https://x.com/amarHarolikar/status/2106754041678356560" }, { "id": "mfpro-scheme-details-live-oct2026", "slug": "mfpro-scheme-details-live-oct2026", "title": "Scheme Details for Indian Mutual Funds Are Live on Tigzig: Fund Size, Fund Manager, TER and BER, Benchmark and Riskometer", "date_published": "2026-10-04T09:45:00.000Z", "date_updated": null, "last_modified": "2026-10-04T09:45:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-scheme-details-live-oct2026", "thumbnail": "/images/blog/mfproSchemeDetailsOct2026.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "database-ai" ], "summary": "Scheme details for Indian mutual funds are live on the free Tigzig MF API, MFPRO and downloads: 60 fields per scheme across about 2,100 funds and 8,600 plans, including fund size, fund manager, TER and BER, benchmark, riskometers and scheme terms, refreshed four times a day. Pull one scheme or up to 50 per call, or download Parquet or CSV. Usage notes cover fund-level values repeating on every plan, fund_dedup_flag, joining on scheme_code, AUM versus AAUM, and how TER is matched.", "twitter_status": "posted", "tweet_id": "2106680642411098307", "tweet_url": "https://x.com/amarHarolikar/status/2106680642411098307" }, { "id": "vigil-brsr-esg-fy2026-oct2026", "slug": "vigil-brsr-esg-fy2026-oct2026", "title": "FY 2025-26 BRSR Data Is Live on VIGIL: ESG Disclosures for 663 of the 750 Nifty Total Market Companies", "date_published": "2026-10-02T09:45:00.000Z", "date_updated": null, "last_modified": "2026-10-02T09:45:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-brsr-esg-fy2026-oct2026", "thumbnail": "/images/blog/vigilBrsrEsgFy26Oct2026.png", "status": "published", "deck": "release", "tags": [ "vigil" ], "summary": "VIGIL now has FY 2025-26 BRSR (Business Responsibility and Sustainability Report) data for 663 of the 750 Nifty Total Market companies, covering workforce, pay and complaints: headcount by type and gender, women on boards and in key management, median pay by gender, minimum wage compliance, and POSH and other complaints. Charts by index and sector, a Data Explorer, five-company compare, an XBRL viewer, data quality flags and a full CSV download. Explains CY and PY year views and December year-ends.", "twitter_status": "posted", "tweet_id": "2105958051434107005", "tweet_url": "https://x.com/amarHarolikar/status/2105958051434107005" }, { "id": "tremor-news-jev-rank-oct2026", "slug": "tremor-news-jev-rank-oct2026", "title": "Jev Rank on TREMOR News Scores Every Headline Against What You Want to Read", "date_published": "2026-10-01T09:40:00.000Z", "date_updated": null, "last_modified": "2026-10-01T09:40:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-news-jev-rank-oct2026", "thumbnail": "/images/blog/tremorJevRankOct2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "ai-coders" ], "summary": "TREMOR's news section now has Jev Rank, which scores about 800 headlines from 64 feeds from 0 to 100 against a reader's interests and sorts the page, in under 2 seconds. Three ways to use it: Run Jev Rank on default or saved interests, Edit interests with your own topics and example headlines, or a Focus box for a quick topic. Jev is TypeSafe's System One model; each headline is a yes or no question and the probability of yes is the score. Free, no signup.", "twitter_status": "posted", "tweet_id": "2105598778367762628", "tweet_url": "https://x.com/amarHarolikar/status/2105598778367762628" }, { "id": "anthropic-ipo-2-trillion-headwinds-sep2026", "slug": "anthropic-ipo-2-trillion-headwinds-sep2026", "title": "Anthropic IPO Could Reach $2T. The Headwinds Are Already Here", "date_published": "2026-09-30T15:00:00.000Z", "date_updated": null, "last_modified": "2026-09-30T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-ipo-2-trillion-headwinds-sep2026", "thumbnail": "/files/ANTHROPIC_IPO_FILING_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Reuters reports Anthropic's IPO could value it at more than 2 trillion dollars. A personal note on the market it would list into: a 2025 net loss of 42 billion dollars, mostly a non-cash charge, on 4.6 billion of revenue; a positive adjusted operating profit reported for Q2 2026; about 414 of 518 billion of computing commitments that must be paid; bond yields at a 2007 high, inflation, diesel, data centre opposition and a free Chinese model close behind in cyber. Nine-page deck. Personal views, not investment advice.", "twitter_status": "posted", "tweet_id": "2105312681217740840", "tweet_url": "https://x.com/amarHarolikar/status/2105312681217740840" }, { "id": "anthropic-glm53-open-weight-cyber-sep2026", "slug": "anthropic-glm53-open-weight-cyber-sep2026", "title": "GLM-5.3 From Z.ai, Like Claude Mythos Preview, Can Build Cyber Exploits on Its Own, Anthropic Finds", "date_published": "2026-09-30T05:30:00.000Z", "date_updated": "2026-09-30T17:00:00.000Z", "last_modified": "2026-09-30T05:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-glm53-open-weight-cyber-sep2026", "thumbnail": "/files/ANTHROPIC_GLM53_CYBER_CAPABILITIES_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "ai-coders", "portfolio-quants" ], "summary": "Anthropic's Frontier Red Team tested GLM-5.3, an open-weight model from Zhipu AI, known outside China as Z.ai, and found it built a working Chrome exploit in 50 of 410 attempts against 56 for Claude Mythos Preview. NIST calls it the most cyber-capable open-weight model to date, about four months behind the best US models. Anthropic's tests show its refusals give way 64% of the time to a cover story and 100% once the refusals are edited out, which cost about 4,400 dollars of computing. Ten-page deck with the key findings and a link to the full report.", "twitter_status": "posted", "tweet_id": "2105343421749825965", "tweet_url": "https://x.com/amarHarolikar/status/2105343421749825965" }, { "id": "ai-hyperscalers-debt-bond-rout-sep2026", "slug": "ai-hyperscalers-debt-bond-rout-sep2026", "title": "The Biggest Tech Build Meets the Bond Rout of 2026", "date_published": "2026-09-28T14:00:00.000Z", "date_updated": null, "last_modified": "2026-09-28T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/ai-hyperscalers-debt-bond-rout-sep2026", "thumbnail": "/files/AI_BUILD_CASH_AND_DEBT_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Goldman expects the five hyperscalers to spend $3.4 trillion on AI over the next three years, nearly 3x the prior seven, just as the 2026 bond rout pushes yields to their highest since 2007. Free cash flow has gone negative at Alphabet, Oracle and Amazon; capital spending took 99% of the five hyperscalers operating cash flow in the June quarter; their borrowing has nearly doubled since 2023 to $483 billion, with J.P. Morgan Asset Management expecting $279 billion more in bond sales this year. Twelve-page deck covers the off-balance-sheet leases and guarantees, and the wider $5 trillion data centre build through 2030.", "twitter_status": "posted" }, { "id": "foreign-buying-us-stocks-record-sep2026", "slug": "foreign-buying-us-stocks-record-sep2026", "title": "Foreign Buying of US Stocks Hit a Record. Then July Went Quiet.", "date_published": "2026-09-28T09:30:00.000Z", "date_updated": null, "last_modified": "2026-09-28T09:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/foreign-buying-us-stocks-record-sep2026", "thumbnail": "/files/FOREIGN_BUYING_US_STOCKS_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Foreigners bought a record $942 billion of US stocks in the 12 months to July, then bought only $4 billion in July as Canada alone sold $60 billion. Money tilted from Treasuries to stocks: Treasury buying fell to $247 billion from $541 billion two years earlier. Private money did 85% of the stock buying, and official money (governments, central banks, sovereign funds) holds only 11% of foreign-held US stocks, with its share of long-term Treasuries down from 61% in 2020 to 44%. The 10-year yield then closed at 5.17% on 25 September, last seen in 2007. Fifteen-page deck with full country tables, sourced from US Treasury TIC files.", "twitter_status": "posted" }, { "id": "cricket-five-million-balls-teams-sep2026", "slug": "cricket-five-million-balls-teams-sep2026", "title": "Five Million Balls of Cricket, Available as a Database. Free to Use.", "date_published": "2026-09-27T09:00:00.000Z", "date_updated": null, "last_modified": "2026-09-27T09:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-five-million-balls-teams-sep2026", "thumbnail": "/images/blog/cricketTeamsSep2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "Five million balls of cricket, free to use as a database: ball by ball, match info and a player master with cross reference IDs, on Postgres and DuckDB, over MCP for AI agents, or as a full download. Test, ODI, T20 and the IPL, men and women, refreshed twice a day. Data source is Cricsheet, licensed ODC-BY 1.0.", "twitter_status": "posted" }, { "id": "diesel-above-six-dollars-sep2026", "slug": "diesel-above-six-dollars-sep2026", "title": "US Diesel Is Above $6 a Gallon for the First Time, and the Cost Is Already Moving Through the Economy", "date_published": "2026-09-26T14:30:00.000Z", "date_updated": null, "last_modified": "2026-09-26T14:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/diesel-above-six-dollars-sep2026", "thumbnail": "/images/blog/dieselAboveSixSep2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "US diesel hit $6.53 a gallon on 21 September, up 74% on a year ago, and the cost is already moving through the economy: truck freight rates up 14%, airfares up 23%, heating oil forecast up to 31% higher this winter. The squeeze is in refining, with diesel about $3.60 a gallon above Brent crude, wider than at any point in 2022, as Russian refining stays about 30% below its pre-war level for the next 18 months. Washington is weighing a ban on diesel exports, with no decision yet.", "twitter_status": "posted" }, { "id": "us-credit-data-q2-2026-tremor-sep2026", "slug": "us-credit-data-q2-2026-tremor-sep2026", "title": "All Five US Credit Datasets on TREMOR Now Run to Q2 2026", "date_published": "2026-09-26T07:45:00.000Z", "date_updated": null, "last_modified": "2026-09-26T07:45:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-credit-data-q2-2026-tremor-sep2026", "thumbnail": "/images/blog/usCreditDataQ2Sep2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "database-ai" ], "summary": "All five US credit datasets on TREMOR now run to Q2 2026: US banks (FDIC), credit unions (NCUA), household debt (NY Fed), insurers' investments (Federal Reserve) and bank loans to non-banks (FFIEC), with up to 106 quarters of history. Covers the interactive tool's tree view, cross-section comparisons across up to four quarters, and how each source's own delinquency basis differs. Plus how each dataset is built and reconciled, and Q2 2026 notes: FDIC charge-offs down to 0.56 percent from a 0.70 percent peak, NY Fed's 90+ flow down to 2.57 percent, credit union charge-offs at 0.76 percent above the pre-crisis level, and 2.8 trillion dollars in bank commitments to non-bank lenders.", "twitter_status": "posted" }, { "id": "tremor-bond-data-macro-fallout-sep2026", "slug": "tremor-bond-data-macro-fallout-sep2026", "title": "All the Macro Data You Need to Track the Bond Market Rout, in One Place on TREMOR", "date_published": "2026-09-25T15:15:00.000Z", "date_updated": null, "last_modified": "2026-09-25T15:15:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-bond-data-macro-fallout-sep2026", "thumbnail": "/images/blog/tremorBondDataSep2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "database-ai" ], "summary": "TREMOR carries 340+ macro and rate series from 25 sources, updated three times a day, covering bond yields, credit stress, jobs, inflation, oil, GDP and housing, as the US 10-year closes at 5.18 per cent and touches 5.21 intraday. Three ways in: the interactive charting tool with overlays, YoY and MoM views and moving averages, the API and MCP server for an AI agent or a script, and full database downloads as CSV, Parquet, DuckDB or SQLite. Links to the day's bond rout post and four earlier pieces on bonds, jobs and AI valuations.", "twitter_status": "posted" }, { "id": "bond-market-rout-sep2026", "slug": "bond-market-rout-sep2026", "title": "Bond Markets Have Been Warning for Months. Now They Have Started Acting.", "date_published": "2026-09-25T07:00:00.000Z", "date_updated": null, "last_modified": "2026-09-25T07:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/bond-market-rout-sep2026", "thumbnail": "/images/blog/bondRoutSep2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "A follow-on to the 6 September bond markets post. The US 10-year closed above 5 per cent for the first time since 2007, a level seen since 2000 only in the dot-com years and the run-up to 2008. Almost all of the rise is the real yield, the TIPS measure of the return lenders want above inflation: over one year the 10-year rose 0.99 points while expected inflation fell 0.02. Japan's 10-year is at its highest since August 1996. Links to earlier analysis on the Fed, banks and AI valuations.", "twitter_status": "posted" }, { "id": "us-jobs-calm-before-storm-sep2026", "slug": "us-jobs-calm-before-storm-sep2026", "title": "On the Headline Numbers the US Job Market Looks Fine. The Story Changes When You Dig Deeper.", "date_published": "2026-09-24T13:50:00.000Z", "date_updated": null, "last_modified": "2026-09-24T13:50:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-jobs-calm-before-storm-sep2026", "thumbnail": "/files/LABOUR_HEADLINE_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "A 16-page deck on the US job market as of September 2026. The August jobs report beat expectations and the Fed called the economy solid, but payroll growth has slowed from 3.2 per cent a year in January 2023 to 0.4 per cent, the August 2024 and 2025 counts were each revised down by about a million, hiring is near its lowest on record and the median time out of work rose from 9.6 to 11.4 weeks. Hourly pay has trailed prices five months running and the saving rate is 3.0 per cent, while layoffs stay low. Plus the survey quirks that trip up labour data.", "twitter_status": "posted" }, { "id": "anthropic-threat-intelligence-cyber-sep2026", "slug": "anthropic-threat-intelligence-cyber-sep2026", "title": "Sophisticated Attacks No Longer Require Sophisticated Attackers. Anthropic's September Threat Intelligence Report, and What I See in My Own Logs.", "date_published": "2026-09-21T07:45:00.000Z", "date_updated": null, "last_modified": "2026-09-21T07:45:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-threat-intelligence-cyber-sep2026", "thumbnail": "/files/ANTHROPIC_THREAT_INTELLIGENCE_CYBER_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "security", "ai-coders" ], "summary": "A 13-page deck on the cyber operations section of Anthropic's September 2026 threat intelligence report. A Russian espionage group left its AI rewriting malware until no security product could detect it, one operator downloaded 1.8 million phone apps to read the credentials developers had left inside them, and a third built a loop that reads a security product's code and keeps writing attacks until one works. The ways in are the familiar ones, stolen credentials, unpatched gateways, exposed services, SQL injection and phishing, and the difference is speed and scale: one break-in ran from a stolen developer login to full administrative control of a cloud in about three hours. Plus hotel guest wifi turned into a malware delivery system, and what has changed in my own edge logs since January.", "twitter_status": "posted" }, { "id": "qdesk-one-app-three-ways-in-sep2026", "slug": "qdesk-one-app-three-ways-in-sep2026", "title": "Everything I Built for Market Reports Now Sits in One App. QDESK, and Three Ways In.", "date_published": "2026-09-20T14:30:00.000Z", "date_updated": null, "last_modified": "2026-09-20T14:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/qdesk-one-app-three-ways-in-sep2026", "thumbnail": "/images/blog/qdeskOnePlaceSep2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "mcp-servers" ], "summary": "The quant and portfolio reports that used to live in three separate apps now sit in one, called QDESK. Three ways in: ask the agent in plain English, fill a form and hit generate, or point your own code or your own agent at the API and MCP, which is what the first two call underneath. The agent now looks up the symbol live, having previously recalled tickers from memory. Covers the report set and the Indian mutual fund NAV history route through MFPRO.", "twitter_status": "posted" }, { "id": "mfpro-composition-august-portfolios-sep2026", "slug": "mfpro-composition-august-portfolios-sep2026", "title": "August 2026 Portfolios Are Live on MFPRO Composition. Eleven Month-End Snapshots Across 24 of India's Larger Equity Funds.", "date_published": "2026-09-20T14:15:00.000Z", "date_updated": null, "last_modified": "2026-09-20T14:15:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-composition-august-portfolios-sep2026", "thumbnail": "/images/blog/mfproCompositionSep2026.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "portfolio-quants" ], "summary": "August 2026 portfolios are live on the MFPRO composition tool, taking it to eleven month-end snapshots across 24 of India's larger equity funds. Covers reading the change in market value and the change in quantity together, the ISIN remapping, why money market lines are grouped, and the four rows holding everything that is not a listed Indian share. Validation is published: 251 of 264 fund-months match the AMC's own printed grand total, the other 13 listed with the amount and the reason.", "twitter_status": "posted" }, { "id": "cricket-point-your-ai-ball-by-ball-sep2026", "slug": "cricket-point-your-ai-ball-by-ball-sep2026", "title": "Point Your AI at a Free Ball-by-Ball Cricket Database. Five Million Deliveries, One Paste.", "date_published": "2026-09-20T06:20:00.000Z", "date_updated": null, "last_modified": "2026-09-20T06:20:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-point-your-ai-ball-by-ball-sep2026", "thumbnail": "/images/blog/cricketSundayAgentSep2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "A free ball-by-ball cricket database an AI agent can find and use on its own, from one pasted line. Every delivery in international cricket since December 2001, Test, ODI and T20 for men and women plus the IPL, about five million balls across roughly 11,000 matches, refreshed twice a day. Two ways in: read-only SQL over Postgres and DuckDB through the API, or the whole database as a DuckDB file under 20 MB zipped to run on your own laptop with no rate limits. Covers the three data traps that return a wrong number without throwing an error, which agents can do this today, the four tables, and the OpenAPI spec an agent reads to work the rest out.", "twitter_status": "posted", "tweet_id": "2101557974078791692", "tweet_url": "https://x.com/amarHarolikar/status/2101557974078791692" }, { "id": "india-sectoral-credit-jul2026-nbfc-sep2026", "slug": "india-sectoral-credit-jul2026-nbfc-sep2026", "title": "India Bank Credit Is Up 19 Percent, and the Biggest Single Contributor Is Lending to NBFCs", "date_published": "2026-09-19T14:25:00.000Z", "date_updated": null, "last_modified": "2026-09-19T14:25:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/india-sectoral-credit-jul2026-nbfc-sep2026", "thumbnail": "/images/blog/indiaCreditJul2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants", "database-ai" ], "summary": "India's July 2026 sectoral credit data is live on TREMOR. Non-food bank credit is Rs 219.6 lakh crore, up 19.1 percent over the year, and bank lending to NBFCs is the single biggest contributor, at Rs 21.3 lakh crore and Rs 5.6 lakh crore of the Rs 35.2 lakh crore the book added. RBI's extra 25 point risk weight on that lending came off in April 2025 and growth went 11, then 3, then 36 percent across the three Julys. Also covers why the Rs 5.5 lakh crore gold jewellery line is banks only against ICRA's Rs 18 lakh crore organised market, the credit deposit ratio coming off its 83.38 high, and the FCNR(B) window and record reserves rise that land in the fortnights after the last deposit reading.", "twitter_status": "posted", "tweet_id": "2101318146145849470", "tweet_url": "https://x.com/amarHarolikar/status/2101318146145849470" }, { "id": "credit-union-q2-2026-losses-delinquency-sep2026", "slug": "credit-union-q2-2026-losses-delinquency-sep2026", "title": "Credit Union Q2 2026: Losses Above Pre-Crisis, and Delinquency Above Pre-COVID in Every Segment", "date_published": "2026-09-19T10:30:00.000Z", "date_updated": null, "last_modified": "2026-09-19T10:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/credit-union-q2-2026-losses-delinquency-sep2026", "thumbnail": "/images/blog/ncuaQ2ReleaseSep2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants", "database-ai" ], "summary": "NCUA published Q2 2026 credit union data on 14 September and it is now on the TREMOR interactive tool and downloadable as a file. Charge-offs across all lending are 0.76 percent on a for-the-quarter annualised basis, above the 0.49 percent of Q3 2007 and the 0.56 percent pre-COVID level, against a 1.33 percent crisis peak. Sixty plus day delinquency is above its pre-COVID level in every segment. Consumer is doing most of the loss, at 1.74 percent against 1.09 before COVID, while property delinquency has risen from 0.58 to 0.83 with charge-offs unmoved at 0.02 percent. Also covers the changing shape of the 1.76 trillion dollar book, with property and commercial growing while auto shrinks in dollars, and the measurement basis behind each comparison.", "twitter_status": "posted", "tweet_id": "2101251252726456591", "tweet_url": "https://x.com/amarHarolikar/status/2101251252726456591" }, { "id": "mf-nav-isin-checker-404-sep2026", "slug": "mf-nav-isin-checker-404-sep2026", "title": "Getting a 404 From the MF NAV API? Check Whether the ISIN Itself Is Wrong", "date_published": "2026-09-18T14:05:00.000Z", "date_updated": null, "last_modified": "2026-09-18T14:05:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mf-nav-isin-checker-404-sep2026", "thumbnail": "/images/blog/mf_nav_api.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "converters-tools" ], "summary": "Malformed ISINs are a common cause of a 404 from the Tigzig MF NAV API, and the free checker at isin.tigzig.com separates the two cases: an ISIN that is itself wrong, against a valid one whose data is simply not there. Also a reminder of why the identifiers are awkward, since what most people call one fund can carry up to six ISINs and ten identifiers including scheme codes, some IDCW schemes have no payout ISIN at all, and SIF identifiers are valid while returning nothing from any mutual fund source. Links through to the earlier write-up for the detail, plus the mutual fund hub, the MFPro tool and the NAV API docs.", "twitter_status": "posted", "tweet_id": "2100960805760254448", "tweet_url": "https://x.com/amarHarolikar/status/2100960805760254448" }, { "id": "tigzig-agent-data-surface-sep2026", "slug": "tigzig-agent-data-surface-sep2026", "title": "Point Your AI Agent at api.tigzig.com. 40 Million Rows of Public Data, and Half the Bytes Are One File", "date_published": "2026-09-18T13:22:24.000Z", "date_updated": null, "last_modified": "2026-09-18T13:22:24.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-agent-data-surface-sep2026", "thumbnail": "/images/blog/tigzigAgentDataSurfaceSep2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "mutual-funds", "vigil", "portfolio-quants" ], "summary": "The agent data surface at api.tigzig.com now spans over 40 million rows across Indian mutual fund NAVs, ball-by-ball cricket, global macro indicators and India company filings, refreshed daily and free with no signup or key. Cricket is the addition since the August post, covering Tests, ODIs, T20s and the IPL for men and women from December 2001. Over seven days the surface served more than 110,000 API calls and more than 1,700 file downloads, about 40 GB in all, and one file alone, the complete NAV history in Parquet, accounts for more than half those bytes. Also covers the four routes in, the downloads manifest with its row counts and generated_at stamp, conditional requests returning 304, and how to pick between CSV, Parquet, SQLite and DuckDB.", "twitter_status": "posted", "tweet_id": "2100942536466636858", "tweet_url": "https://x.com/amarHarolikar/status/2100942536466636858" }, { "id": "qrep-excel-webservice-81-metrics-sep2026", "slug": "qrep-excel-webservice-81-metrics-sep2026", "title": "Pull 81 Risk and Performance Metrics Into Excel With One Formula", "date_published": "2026-09-17T10:00:00.000Z", "date_updated": null, "last_modified": "2026-09-17T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/qrep-excel-webservice-81-metrics-sep2026", "thumbnail": "/images/blog/qrepExcelSep2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "excel" ], "summary": "One Excel formula pulls 81 risk and performance metrics for any Yahoo Finance ticker straight into a sheet, using WEBSERVICE against the QREP API and TEXTSPLIT to lay the tab-separated response out across cells. Covers the working formula, the two troubleshooting steps, and how to read the same data in a browser as TSV or JSON. Also sets out the limits: WEBSERVICE fires every cell at once so a few hundred symbols runs into the host rate limit, and Power Query or Python in Excel is the route for that. Parameters explained: the risk free rate is a decimal fraction so 0.05 means 5 percent and 5 is rejected, six symbols is the maximum per call, and the benchmark sits outside that count. Backend is the Python QuantStats library with documented changes to the calculations.", "twitter_status": "posted", "tweet_id": "2100590672290144555", "tweet_url": "https://x.com/amarHarolikar/status/2100590672290144555" }, { "id": "fed-caught-rate-decision-sep2026", "slug": "fed-caught-rate-decision-sep2026", "title": "The Fed Is Caught. The Market Has Already Made Most of the Decision for It.", "date_published": "2026-09-14T12:00:00.000Z", "date_updated": null, "last_modified": "2026-09-14T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/fed-caught-rate-decision-sep2026", "thumbnail": "/files/FED_IS_CAUGHT_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "The FOMC decides on Wednesday 16 September and the market has largely priced it: a quarter point rise sits at 86 per cent on CME FedWatch against 33 per cent a month ago, and the three month Treasury bill is already at 4.00 per cent, exactly where that rise would put the top of the range. Over the past year the ten year Treasury rose 91 basis points and ten year TIPS rose 86, so the gap the market reads as expected inflation moved just 5 basis points and almost the whole rise is the real cost of money. That cost is paid on mortgages, refinancing and every government bond rolled over, whether the Fed moves or holds. A rate rise also does not reach the supply side pushing prices up, with diesel up 59.8 per cent over the year and petrol 30.8. Also covers the political campaign around the decision, Fed Chairman Warsh's breadth and duration case from his Jackson Hole keynote, and the two things in that same speech that cut the other way.", "twitter_status": "posted", "tweet_id": "2099435867165327552", "tweet_url": "https://x.com/amarHarolikar/status/2099435867165327552" }, { "id": "vigil-flags-filter-by-signal-sep2026", "slug": "vigil-flags-filter-by-signal-sep2026", "title": "Releasing VIGIL Flags. Filter Companies by One or More Signals Instead of Going Company by Company.", "date_published": "2026-09-14T10:00:00.000Z", "date_updated": null, "last_modified": "2026-09-14T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-flags-filter-by-signal-sep2026", "thumbnail": "/images/blog/vigilFlagsSep2026.png", "status": "published", "deck": "release", "tags": [ "vigil" ], "summary": "VIGIL Flags lets you filter listed Indian companies by one or more red-flag conditions at once, instead of moving across tabs or checking one company at a time. Twelve conditions cover credit ratings, promoters buying and selling, outsiders crossing five per cent, pledges and lenders invoking them, encumbrance, insolvency and the exchange surveillance flags. Tick several and choose any, which behaves like an OR, or all, which behaves like an AND. Also explains the split that changes how the period filter behaves: eight of the conditions are dated events, while four are the position a company is in right now and read the same whatever period you pick. Plus why promoter sold down and promoter sold five per cent or more are kept apart, since one reads the insider filings at a one point cut and the other reads the takeover filings at five per cent.", "twitter_status": "posted", "tweet_id": "2099392969057165776", "tweet_url": "https://x.com/amarHarolikar/status/2099392969057165776" }, { "id": "qdesk-reporting-desk-sep2026", "slug": "qdesk-reporting-desk-sep2026", "title": "QDESK: A Reporting Desk for Portfolio Reports and Financial Data. Just Ask in Plain Language.", "date_published": "2026-09-13T09:45:00.000Z", "last_modified": "2026-09-13T09:45:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/qdesk-reporting-desk-sep2026", "thumbnail": "/images/blog/qdeskSep2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "mcp" ], "summary": "QDESK is an agent-driven reporting desk: ask in plain language and it looks up the symbol then runs the report, so you never need the ticker. It produces a security performance report, daily and weekly technical analysis, a QPulse tearsheet against a benchmark, and QREP metrics with around eighty risk and return figures for up to six securities. Also pulls company financials, prices and Indian mutual fund NAV history, with REST and MCP endpoints behind each report.", "twitter_status": "posted" }, { "id": "sif-august-2026-biggest-month-sep2026", "slug": "sif-august-2026-biggest-month-sep2026", "title": "August Was the Biggest Month Indian SIFs Have Had. Net Assets Rs 31,175 Crore, Net Inflows Rs 7,699 Crore.", "date_published": "2026-09-12T10:40:00.000Z", "last_modified": "2026-09-12T10:40:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sif-august-2026-biggest-month-sep2026", "thumbnail": "/images/blog/sifPageSep2026.png", "status": "published", "deck": "analysis", "tags": [ "mutual-funds", "database-ai" ], "summary": "August 2026 was the largest month Indian Specialized Investment Funds have recorded: net assets rose from Rs 23,177 crore to Rs 31,175 crore, net inflows were Rs 7,699 crore and folios went from 94,000 to over 125,000. Equity Ex-Top 100 Long-Short grew about 66% and now spans seven fund houses, Hybrid Long-Short holds about 63% of assets, and the two debt strategies SEBI permits still have no funds. Free download in CSV and Parquet.", "twitter_status": "posted" }, { "id": "cricket-sql-stats-functions-and-limits-sep2026", "slug": "cricket-sql-stats-functions-and-limits-sep2026", "title": "Twelve More Stats Functions on the Cricket SQL Endpoints, and the Two Limits That Are Easy to Hit", "date_published": "2026-09-12T09:20:00.000Z", "last_modified": "2026-09-12T09:20:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-sql-stats-functions-and-limits-sep2026", "thumbnail": "/images/blog/dbmcpAPI.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "Twelve statistical functions added to the cricket SQL allow list after callers were refused on percentiles, quartiles and ranking functions, on top of 17 added earlier in the week. Covers the two request limits that are easy to hit without knowing them: 3,500 characters measured after URL decoding, and ten SELECT keywords per statement, with 1,000 rows returned. Plus which engine to use and which percentile spelling is portable.", "twitter_status": "posted" }, { "id": "yfin-api-csv-format-multiple-tickers-sep2026", "slug": "yfin-api-csv-format-multiple-tickers-sep2026", "title": "YFIN API Now Returns Plain CSV on Most Endpoints. Add format=csv to the Same URL.", "date_published": "2026-09-11T13:40:00.000Z", "last_modified": "2026-09-11T13:40:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/yfin-api-csv-format-multiple-tickers-sep2026", "thumbnail": "/images/blog/yfinCsvSep2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "excel" ], "summary": "The free YFIN Yahoo Finance API now answers with a plain CSV table on most endpoints: add format=csv to the same URL and skip unwrapping JSON. Every endpoint also takes a comma-separated list of up to 25 symbols, so a whole watchlist comes back in one request. Includes the data pull agent at quants.tigzig.com, what it will and will not do, and its separate AI technical analysis report.", "twitter_status": "posted" }, { "id": "mf-nav-run-your-own-service-parquet-sep2026", "slug": "mf-nav-run-your-own-service-parquet-sep2026", "title": "Heavy User of the Mutual Fund NAV API? Run Your Own NAV Service Inside Your Own Setup", "date_published": "2026-09-11T08:10:00.000Z", "last_modified": "2026-09-11T08:10:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mf-nav-run-your-own-service-parquet-sep2026", "thumbnail": "/images/blog/mfNavAPISep20236.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "database-ai" ], "summary": "The whole Indian mutual fund NAV dataset is published as a single file, and the Parquet copy is about 169 MB covering every scheme and every NAV date back to April 2006. On a Python backend duckdb queries the Parquet file where it sits, with no database server to install, and Polars reads the same file. Keep it current by pointing a cron at the manifest and re-pulling when the build time moves.", "twitter_status": "posted" }, { "id": "cricket-database-14-things-that-trip-people-up-sep2026", "slug": "cricket-database-14-things-that-trip-people-up-sep2026", "title": "For Analysts Using the Tigzig Cricket Database: 14 Things That Trip People Up", "date_published": "2026-09-11T06:40:00.000Z", "last_modified": "2026-09-11T06:40:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-database-14-things-that-trip-people-up-sep2026", "thumbnail": "/images/blog/dbmcpAPI.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "Fourteen schema and query traps in the Tigzig cricket database, each one measured against the live data. Covers winner_innings as a yes/no flag, team1 and team2 not being batting order, run outs inflating bowler wicket counts, blank rather than zero extras columns, over_no starting at zero, the wides column holding runs, target_runs already including the winning run, name-string joins, and a plus sign in a GET query arriving as a space.", "twitter_status": "posted" }, { "id": "macro-signals-back-at-2007-levels-sep2026", "slug": "macro-signals-back-at-2007-levels-sep2026", "title": "Many of These Macro Charts Are Showing the Same Stress They Showed Before 2007. The AI Layer Is What Is New.", "date_published": "2026-09-10T15:10:00.000Z", "last_modified": "2026-09-10T15:10:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/macro-signals-back-at-2007-levels-sep2026", "thumbnail": "/images/blog/macroSignalsSep2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Twelve macro charts pulled from TREMOR, read against 2007. The US 30 year is back at July 2007 levels, the UK 10 year gilt at July 2008, the German Bund at May 2011. Consumer debt 90 days late is 3.31% and bank C&I noncurrent loans sit above every quarter of 2007, while core PCE at 3.34% leaves the Fed no easy move. With Warsh at Jackson Hole, and what the AI layer adds on top.", "twitter_status": "posted" }, { "id": "four-warnings-ai-cyber-risk-sep2026", "slug": "four-warnings-ai-cyber-risk-sep2026", "title": "Four Warnings About AI and Cyber Risk Arrived Inside Three Weeks. I Am Now Spending More Time Securing Than Building.", "date_published": "2026-09-10T12:00:00.000Z", "last_modified": "2026-09-10T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/four-warnings-ai-cyber-risk-sep2026", "thumbnail": "/files/AI_CYBER_RISK_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "security", "ai-coders" ], "summary": "Four warnings on AI and cyber risk landed within three weeks: Google Threat Intelligence Group, a joint advisory from the NSA, CISA, FBI, Department of Energy and EPA, the Financial Stability Board chair's letter to the G20, and a departing OpenAI and Anthropic researcher. Set against nine months of running 40+ no-auth public tools behind Cloudflare, where volume and sophistication both rose. Nine-slide deck, each warning quoted from the document itself.", "twitter_status": "posted" }, { "id": "cricket-match-players-people-tables-sep2026", "slug": "cricket-match-players-people-tables-sep2026", "title": "Two New Tables and 17 New SQL Functions Are Live on the Tigzig Cricket Database. Playing XIs, and a Player Registry Mapped to ESPN Cricinfo.", "date_published": "2026-09-10T06:00:00.000Z", "last_modified": "2026-09-10T06:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-match-players-people-tables-sep2026", "thumbnail": "/images/blog/cricketNewTablesSep2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "Two new tables land on the free Tigzig cricket SQL database: match_players, with playing XIs, teams and match officials across 292,000+ rows, and people, an 18,500+ player registry mapping identifiers to ESPN Cricinfo IDs at a 99.8% match rate. Both join match_info on match_id. Also 17 new SQL functions on Postgres and DuckDB, including dense_rank, lag, lead, string_agg and array_agg. 11,000+ matches, 5.3 million+ records, refreshed twice daily.", "twitter_status": "posted" }, { "id": "tigzig-api-error-hints-read-the-body-sep2026", "slug": "tigzig-api-error-hints-read-the-body-sep2026", "title": "Tigzig APIs Tell You What Went Wrong. If Your Code Reads Only the Status Code, You Never See It.", "date_published": "2026-09-07T18:00:00.000Z", "last_modified": "2026-09-07T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-api-error-hints-read-the-body-sep2026", "thumbnail": "/images/blog/apiErrorHintsSep2026.png", "status": "published", "tags": [ "database-ai", "ai-coders" ], "summary": "Tigzig's free APIs return a tailored hint in the error body explaining what went wrong and how to fix it, but a client that reads only the status code never sees it. Shows how to pull the error block in Python, JavaScript and curl, why requests, httpx, aiohttp, urllib and raise_for_status all hide the body by default, and links the five API debugging guides you can hand to an AI agent.", "twitter_status": "posted", "deck": "release" }, { "id": "fsb-chair-bailey-warning-sep2026", "slug": "fsb-chair-bailey-warning-sep2026", "title": "The FSB Chair Is Not Hedging. Andrew Bailey's Blunt Warning Note to the G20.", "date_published": "2026-09-07T14:00:00.000Z", "last_modified": "2026-09-07T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/fsb-chair-bailey-warning-sep2026", "thumbnail": "/files/FSB_CHAIR_WARNING_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Andrew Bailey, chair of the Financial Stability Board and Governor of the Bank of England, warned the G20 on 28 August 2026 that markets remain vulnerable to a potentially disorderly correction that could spread across borders. Covers the factors he names - sovereign debt, private credit, stretched AI valuations, rising equity leverage - his section on frontier AI and cyber risk, and the July collapse of a leveraged AI fund. Ten-slide deck attached.", "twitter_status": "posted" }, { "id": "mf-nav-api-how-people-use-it-sep2026", "slug": "mf-nav-api-how-people-use-it-sep2026", "title": "MF NAV in the Wild. A Review of the API Logs, and How Widely the Mutual Fund NAV Data Is Being Pulled.", "date_published": "2026-09-07T06:00:00.000Z", "last_modified": "2026-09-07T06:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mf-nav-api-how-people-use-it-sep2026", "thumbnail": "/images/blog/mfNavInTheWildSep2026.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "database-ai" ], "summary": "What a routine security, error and latency review of the free Indian mutual fund NAV API showed about how the data is being used: AI agents, Google Sheets and Apps Script, Excel and Power Query, web apps, an iPhone app, Zoho and n8n, plain Python, and DuckDB querying the published Parquet in place. Also what the logs changed in the service, and an overview of the search, NAV and download surfaces.", "twitter_status": "posted" }, { "id": "bond-markets-alarm-sep2026", "slug": "bond-markets-alarm-sep2026", "title": "Bond Markets Are Sounding an Alarm. The US Thirty Year Is at 5.25 Per Cent and the Fed Has Been Cutting Through All of It.", "date_published": "2026-09-06T13:00:00.000Z", "last_modified": "2026-09-06T13:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/bond-markets-alarm-sep2026", "thumbnail": "/files/BOND_MARKETS_ALARM_SEP2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Long-term borrowing costs are rising across the developed world while central banks cut. The US thirty year is at 5.25 per cent, above 5.2 for the first time since 2007, and since the Fed began cutting the ten year is up 106 basis points and the thirty year 122. Sets out why the long rate is what prices a mortgage, a refinancing and a long-payback project, and the combination of pressures around it. Sixteen-slide deck attached.", "twitter_status": "posted" }, { "id": "cricket-ipl-ball-by-ball-live-sep2026", "slug": "cricket-ipl-ball-by-ball-live-sep2026", "title": "IPL Ball by Ball Data Is Live on the Tigzig Cricket API and Download. 1,200+ Matches, Every Season Since 2008.", "date_published": "2026-09-06T04:00:00.000Z", "last_modified": "2026-09-06T04:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-ipl-ball-by-ball-live-sep2026", "thumbnail": "/images/blog/cricketIplLiveSep2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "IPL ball by ball data is now live on the free Tigzig cricket SQL API, the bulk download and MCP: 1,200+ matches covering every season since 2008, alongside Test, ODI and T20 internationals for men and women back to 2001, for 11,000+ matches and 5 million+ deliveries in all. Explains the layout - one ball_by_ball table plus views by format, gender and IPL, joined to match_info on match_id, identical on Postgres and DuckDB.", "twitter_status": "posted" }, { "id": "mfpro-data-api-manual-download-sep2026", "slug": "mfpro-data-api-manual-download-sep2026", "title": "The API Is Not the Only Way In. Download the Whole India Mutual Fund NAV Database from the App, or Pick Just the Funds You Want.", "date_published": "2026-09-05T09:30:00.000Z", "last_modified": "2026-09-05T09:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-data-api-manual-download-sep2026", "thumbnail": "/images/blog/mfproManualDownloadSep2026.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "database-ai" ], "summary": "Indian mutual fund NAV data can be taken from the MFPro app directly, without the API. Covers the three routes on its Data and API tab: the full history since April 2006, about 37 million rows (parquet 169 MB, csv and tsv 219 MB gzipped, SQLite too), the 1.1 MB latest snapshot that doubles as a 22-column scheme master, and a picker for up to 20 funds with a period selector. Plus the pipeline status page.", "twitter_status": "posted" }, { "id": "mf-nav-bulk-download-one-file-sep2026", "slug": "mf-nav-bulk-download-one-file-sep2026", "title": "India MF Tool Builders: You Do Not Need a Thousand API Calls a Day. The Whole NAV Dataset Is One File.", "date_published": "2026-09-04T15:30:00.000Z", "last_modified": "2026-09-04T15:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mf-nav-bulk-download-one-file-sep2026", "thumbnail": "/images/blog/mfNavAPISep20236.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "database-ai" ], "summary": "For developers pulling Indian mutual fund NAV per scheme from the free Tigzig MF API: the whole dataset is published as one file instead. Parquet is about 168 MB and holds every scheme and every NAV date. Covers the refresh pattern - poll the sub-5 KB manifest for its build time, or send its ETag back for a 304 with no transfer - and the roughly 1 MB daily snapshot that doubles as a scheme master.", "twitter_status": "posted" }, { "id": "cricket-ipl-ball-by-ball-coming-sep2026", "slug": "cricket-ipl-ball-by-ball-coming-sep2026", "title": "IPL Ball by Ball Data Arrives on the Tigzig Cricket API This Sunday, 6 September, at 9 am IST. About 1,200 Matches and Every Season Since 2008.", "date_published": "2026-09-04T13:30:00.000Z", "last_modified": "2026-09-04T13:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-ipl-ball-by-ball-coming-sep2026", "thumbnail": "/images/blog/cricketIplComingSep2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "Announces that IPL ball by ball data lands on the free Tigzig cricket SQL API and bulk download on 6 September 2026, adding about 1,200 matches from 2008 onward to the existing Test, ODI and T20 coverage for men and women. Describes the two-table shape: per-delivery ball by ball tables and a 45-field match_info table, joined on match_id. Postgres and DuckDB, no signup, no API key. Data from Cricsheet under ODC-BY 1.0.", "twitter_status": "posted" }, { "id": "us-bank-loan-data-q2-2026-tremor-sep2026", "slug": "us-bank-loan-data-q2-2026-tremor-sep2026", "title": "US Bank Loan Data Is Now Updated Through Q2 2026 on TREMOR. NY Fed, FDIC and the FFIEC Call Reports, All Three in One Place.", "date_published": "2026-09-04T11:30:00.000Z", "last_modified": "2026-09-04T11:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-bank-loan-data-q2-2026-tremor-sep2026", "thumbnail": "/images/blog/usBankLoanDataQ2Sep2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "database-ai" ], "summary": "All three US bank and household credit datasets on TREMOR are now current to Q2 2026: FDIC industry aggregates back to 2001, FFIEC Call Report data for 754 individual banks with their capital and non-bank lending, and the NY Fed Equifax consumer credit panel back to 1999. Covers the Q2 delinquency moves on both sides, and why FDIC and NY Fed delinquency rates are not comparable.", "twitter_status": "posted" }, { "id": "anthropic-data-analytics-sep2026", "slug": "anthropic-data-analytics-sep2026", "title": "In Analytics the SQL Was Always the Easy Part. Anthropic's Data Team Put a Figure on It.", "date_published": "2026-09-04T08:00:00.000Z", "date_updated": null, "last_modified": "2026-09-04T08:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-data-analytics-sep2026", "thumbnail": "/files/ANTHROPIC_DATA_ANALYTICS/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "ai-coders", "database-ai" ], "summary": "A read of two Anthropic posts on running self-service analytics with Claude, put into one deck with a practitioner's note at the end. Their headline numbers: without skills, accuracy on their own evals did not exceed 21 percent, and with skills it sits above 95 percent in aggregate on the same model and the same warehouse. Their offline accuracy then drifted from about 95 percent at launch to about 65 percent within a month, because the data model changed and the skill docs did not follow, which they fixed by colocating the markdown with the transformation models. The closing section covers what has worked in the author's own client work: a semantic layer holding the governed definitions, a separate skill per kind of work carrying its own judgment and tolerances, progressive disclosure so the agent does not read everything to answer one question, and continuous update as the thing doing most of the work.", "twitter_status": "posted" }, { "id": "portfolio-agent-symbol-lookup-sep2026", "slug": "portfolio-agent-symbol-lookup-sep2026", "title": "The Portfolio Agent Now Looks Up Live Symbols for Stocks and Securities, Instead of Guessing From Memory.", "date_published": "2026-09-03T16:00:00.000Z", "date_updated": null, "last_modified": "2026-09-03T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/portfolio-agent-symbol-lookup-sep2026", "thumbnail": "/images/blog/portfolioAgentSep2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "mutual-funds" ], "summary": "The Tigzig portfolio agent at quants.tigzig.com now resolves stock and security symbols against the live Yahoo Finance lookup rather than recalling them, the same pattern already used for Indian mutual fund schemes. Prompted by log review, which showed symbol guessing as a point of friction and users asking the agent to pull NAVs. Five reports are available: a QRep performance and risk report for one security against a benchmark, a chart-based technical analysis report, a multi-security performance comparison with HTML and CSV output, a Yahoo Finance data pull covering prices, profiles, financials, holders, estimates, dividends, splits, actions and ticker lookup, and Indian mutual fund scheme lookup with NAV history.", "twitter_status": "posted" }, { "id": "sif-data-universe-download-sep2026", "slug": "sif-data-universe-download-sep2026", "title": "SIF Is India's Newest Fund Category and It Nearly Doubled in Three Months. Every NAV AMFI Has Published Is Now Downloadable.", "date_published": "2026-09-03T11:00:00.000Z", "date_updated": null, "last_modified": "2026-09-03T11:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sif-data-universe-download-sep2026", "thumbnail": "/images/blog/sifUniverseSep2026.png", "status": "published", "deck": "analysis", "tags": [ "mutual-funds", "database-ai" ], "summary": "Specialized Investment Funds, India's newest fund category, grew from Rs 12.3K crore of AUM at end-April to Rs 23.2K crore at end-July, and every SIF NAV AMFI has published is now downloadable as CSV and parquet at sif.tigzig.com, refreshed twice a day. Explains why none of the 17 fund house names is recognisable, since SEBI clause 21.2.1 requires a SIF to carry a brand distinct from its AMC, and how to identify the sponsor anyway: the SIF code is a running number, but each scheme's ISIN carries the AMC's issuer prefix, so INF174K is Kotak, INF109K is ICICI Prudential and INF200K is SBI. Covers the SEBI Master Circular chapter 21 clauses on branding, one strategy per category and the Rs 10 lakh PAN-level minimum, plus the concentration of money in Hybrid Long-Short and the two debt strategies that have never had a fund.", "twitter_status": "posted" }, { "id": "cricket-womens-data-added-sep2026", "slug": "cricket-womens-data-added-sep2026", "title": "Women's Cricket Is Now in the Cricket Database. Four New Tables, 2,774 Matches and 850K+ Deliveries Back to 2003.", "date_published": "2026-09-03T09:00:00.000Z", "date_updated": null, "last_modified": "2026-09-03T09:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-womens-data-added-sep2026", "thumbnail": "/images/blog/cricketWomensSep2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "Women's international cricket is now in the Tigzig open cricket database, as four new tables covering Test, ODI and T20 ball by ball. 2,774 women's matches and about 852,000 deliveries going back to 2003, made up of 2,141 T20s from 2009, 609 ODIs from 2007 and 24 Tests from 2003. That takes the whole database to 9,769 matches and 4.73 million deliveries across eight tables, refreshed twice daily and queryable live over the REST API and the MCP server on both Postgres and DuckDB. Also downloadable in bulk, about 12 MB as DuckDB or 83 MB as SQLite, or one table at a time. Covers the manifest and ETag conditional requests for scheduled jobs, why the women's data sits in separate tables, and the absence of a stable player id. Data source is Cricsheet under ODC-BY 1.0.", "twitter_status": "posted" }, { "id": "vigil-whole-database-download-sep2026", "slug": "vigil-whole-database-download-sep2026", "title": "The Whole VIGIL India Red Flag Database Is Now Published for Download. One API Call Takes the Lot.", "date_published": "2026-09-02T10:00:00.000Z", "date_updated": null, "last_modified": "2026-09-02T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-whole-database-download-sep2026", "thumbnail": "/images/blog/vigilWholeDbSep2026.png", "status": "published", "deck": "release", "tags": [ "vigil", "database-ai" ], "summary": "The VIGIL India red-flag database is now downloadable in full, so a screen across a few hundred companies can be one call instead of hundreds. Covers seven tables: related party transactions, insider trading, credit ratings, promoter pledge, encumbrance events, takeover and SAST filings, and surveillance flags, spanning around 2,500 listed companies and about 426,000 rows. One SQLite file holds all seven at roughly 18 MB, or take a single table as parquet, CSV or TSV, gzipped or zipped, 51 files in total. A manifest publishes row counts, file sizes and a generated_at stamp, and the files answer If-None-Match with a 304 so a scheduled job stops re-downloading unchanged bytes. Rebuilt four times a day. Also explains which tables are event streams and which are current snapshots, and why related party rows are filed on a consolidated basis.", "twitter_status": "posted" }, { "id": "ai-valuation-correction-aug2026", "slug": "ai-valuation-correction-aug2026", "title": "A Correction in AI Valuations Is Looking More Likely, and That View Now Comes From a Central Bank, an Asset Manager and a Bond Investor.", "date_published": "2026-08-31T16:30:00.000Z", "date_updated": null, "last_modified": "2026-08-31T16:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/ai-valuation-correction-aug2026", "thumbnail": "/files/AI_VALUATION_BUBBLE_AUG2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "The case that AI valuations are due a correction, now argued in print by the European Central Bank, and two weeks later by Andrew Bailey to the G20 in his capacity as chair of the Financial Stability Board, who points at leverage and the cross-investment between AI companies and hyperscalers as amplifiers. The whole structure rests on free cash flow arriving from 2028 while the current direction is the opposite, with Alphabet posting its first negative free cash flow quarter as a public company. Sets that alongside the wider pressure: employment, delinquencies and inflation, US banks with $2.8 trillion committed to non-bank lenders, and Fitch's private credit default rate at a record. Includes a twenty-slide deck and links to earlier pieces on valuations, private credit and household debt.", "twitter_status": "posted" }, { "id": "tigzig-whole-database-downloads-aug2026", "slug": "tigzig-whole-database-downloads-aug2026", "title": "Pulling Tigzig Data Through the API? There Is a Second Way In. The Whole Database Is Published for Download.", "date_published": "2026-08-31T10:30:00.000Z", "date_updated": null, "last_modified": "2026-08-31T10:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-whole-database-downloads-aug2026", "thumbnail": "/images/blog/tigzigWholeDbAug2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "infrastructure" ], "summary": "Every Tigzig dataset is downloadable in full, alongside the live API, so a tool builder can put their own micro-service in front of the data or analyze it offline without an API dependency. Covers the mutual fund NAV master with 37 million records back to 2006, ball-by-ball cricket for men's Tests, ODIs and T20s with 6,900+ matches and 3.8 million deliveries, 300+ macro indicator series across the US, India, Europe and the OECD, and the India red-flag datasets covering related party transactions, insider trading, pledges, credit ratings and surveillance. Take one table as parquet or CSV or the whole thing as a single DuckDB or SQLite file. Each dataset publishes a manifest at /downloads/manifest carrying the generation timestamp, file sizes and row counts, so a scheduled job can skip a download when nothing has changed.", "twitter_status": "posted" }, { "id": "tigzig-api-status-page-aug2026", "slug": "tigzig-api-status-page-aug2026", "title": "Getting Errors on the Tigzig APIs? Is It You, or Is It Me? Now You Can Just Go and Look.", "date_published": "2026-08-30T15:00:00.000Z", "date_updated": null, "last_modified": "2026-08-30T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-api-status-page-aug2026", "thumbnail": "/images/blog/status_page.png", "status": "published", "deck": "release", "tags": [ "infrastructure", "database-ai" ], "summary": "A public status page for the Tigzig APIs is now live at status.tigzig.com, so a caller hitting errors can tell whether the fault is theirs or the service's. It covers the MF NAV API, the Yahoo Finance API, the Vigil and Tremor APIs and the Cricket Database MCP on both Postgres and DuckDB, with 90 days of uptime history per service, a banner for incidents and a note at the top during an outage. Alerts reach the operator within a minute or two. The page is served from object storage through a Cloudflare Worker, so nothing on the public path touches the server it monitors. Also links the API hub, the machine-readable catalog for agents, the bulk downloads and the error-pattern guides.", "twitter_status": "posted" }, { "id": "mf-nav-api-429-rate-limit-aug2026", "slug": "mf-nav-api-429-rate-limit-aug2026", "title": "Getting a 429 From the MF NAV API? It Is Almost Never the Total. Two Changes Fix Nearly Every Case.", "date_published": "2026-08-30T12:00:00.000Z", "date_updated": null, "last_modified": "2026-08-30T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mf-nav-api-429-rate-limit-aug2026", "thumbnail": "/images/blog/mf_nav_api.png", "status": "published", "tags": [ "mutual-funds", "database-ai" ], "summary": "Why callers hit a 429 on the India mutual fund NAV API and how to stop it. The per-IP limit is 300 requests a minute and most people who trip it are nowhere near that total; the cause is many unbounded calls firing at the same instant, since a fifty-fund request with no date range returns over 100,000 rows. Two fixes cover nearly every case: batch up to 50 scheme codes into one call using schemes=, and bound the dates with latest=true or since=. Includes tool-specific notes for Excel WEBSERVICE, n8n, Python scripts and Power Query, what the 429 body tells you, and how to hand the error to an AI coder to fix the calls.", "twitter_status": "posted" }, { "id": "us-banks-nonbank-lending-q2-2026", "slug": "us-banks-nonbank-lending-q2-2026", "title": "US Banks Had $2.8 Trillion Committed to Non-Bank Lenders at the End of Q2 2026, and $1.7 Trillion of It Was Already Drawn.", "date_published": "2026-08-30T09:30:00.000Z", "date_updated": null, "last_modified": "2026-08-30T09:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-banks-nonbank-lending-q2-2026", "thumbnail": "/files/US_NONBANK_LENDING_Q2_2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants", "database-ai" ], "summary": "Q2 2026 FFIEC Call Report data on US bank lending to non-bank financial institutions, now live on TREMOR bank by bank. Banks had $2.8 trillion committed at the end of the quarter with $1.7 trillion drawn, equal to 14.3 percent of the lending banks' own loan book, up from 11.1 percent seven quarters earlier and higher in every quarter since separate reporting began in Q4 2024. 754 banks are named across the seven quarters, 633 reporting in the latest, with 37 fields each covering drawn balances, undrawn commitments, the five lending categories, capital ratios and credit quality. The 633 are about one in seven US banks by count but hold 88.7 percent of banking assets. Includes notes on the $10 billion reporting threshold that leaves $32 billion unallocated, blank capital fields under the CBLR framework, and the FDIC cross-check.", "twitter_status": "posted" }, { "id": "amfi-name-and-isin-changes-aug2026", "slug": "amfi-name-and-isin-changes-aug2026", "title": "August Was a Heavy Month in the AMFI Data. 1,234 Schemes Renamed and 37 ISINs Swapped. If You Join on Scheme Code You Are Fine.", "date_published": "2026-08-29T13:00:00.000Z", "date_updated": null, "last_modified": "2026-08-29T13:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/amfi-name-and-isin-changes-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": "analysis", "tags": [ "mutual-funds", "database-ai" ], "summary": "What moved in the AMFI mutual fund data during August 2026 and what it breaks. 1,234 schemes were renamed over the month, 942 on 26 August alone, the SEBI deadline for re-filing under the new categorisation, so Low Duration became Ultra Short to Short Term, Savings became Money Market and Equity and Debt became Aggressive Hybrid. Separately 37 schemes had their ISIN swapped, at UTI, DSP, Kotak, Invesco and LIC, fourteen of them ETFs, and none of them were renamed. Also covers spelling defects in the file, including five spellings of children's fund and SBI's hybrid fund filed as AGGRESIVE across four plans, and the LIC gold ETF case where the replaced ISIN still resolves to the old IDBI fund with a full price history. Anything joined on AMFI scheme code is unaffected.", "twitter_status": "posted" }, { "id": "cricket-downloads-plain-path-urls-aug2026", "slug": "cricket-downloads-plain-path-urls-aug2026", "title": "The Cricket Download Files Now Sit on a Plain Path. Append the File Name and That Is the Whole URL.", "date_published": "2026-08-29T10:00:00.000Z", "date_updated": null, "last_modified": "2026-08-29T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-downloads-plain-path-urls-aug2026", "thumbnail": "/images/blog/cricketPlainPathAug2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "The Tigzig cricket bulk downloads now work off a plain path, so the URL ends in the file name you get and no query parameter is needed. The whole DuckDB database is one GET at about 9 MB compressed, covering ODI, T20 and Test ball by ball, 6,900+ matches from 2001 and 3.8 million deliveries. The short form on db-mcp.tigzig.com is a convenience redirect to the real home at api.tigzig.com/cricket/v1/download, which curl and wget follow with no extra flags. The older query parameter form stays live, so existing scripts keep working, and direct SQL access over Postgres and DuckDB is unchanged. 17 files in all, refreshed twice a day. Data source is Cricsheet under ODC-BY 1.0.", "twitter_status": "posted" }, { "id": "mfpro-api-roadmap-risk-metrics-aug2026", "slug": "mfpro-api-roadmap-risk-metrics-aug2026", "title": "What Is Next on the MF NAV API. Risk Metrics, Peers, Scheme Details and Holdings, and Where Each One Stands.", "date_published": "2026-08-28T12:00:00.000Z", "date_updated": null, "last_modified": "2026-08-28T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-api-roadmap-risk-metrics-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "database-ai" ], "summary": "A roadmap note on the India mutual fund NAV API, covering the four most requested additions and where each stands. Risk metrics are top of the list: alpha, beta, Sharpe, Sortino and drawdown are already computed and live in the Performance tab, so the remaining work is exposing them on the API under load. Peers will not get a separate API path, because peer grouping is a judgment call, but they can be built from the NAV master file or the search path, and the frontend offers them through the Category and Performance tabs. Scheme details are a known gap, with launch date, fund manager and scheme-document fields missing today. Holdings across the whole universe are not on the radar; composition exists on the frontend for 24 large equity funds, about a third of all equity funds.", "twitter_status": "posted" }, { "id": "cricket-full-data-download-live-aug2026", "slug": "cricket-full-data-download-live-aug2026", "title": "The Full Cricket Database Is Now Downloadable. Test, ODI and T20 Ball by Ball, in Parquet, CSV, DuckDB or SQLite.", "date_published": "2026-08-28T09:00:00.000Z", "date_updated": null, "last_modified": "2026-08-28T09:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-full-data-download-live-aug2026", "thumbnail": "/images/blog/cricketDownloadsLiveAug2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "The Tigzig open cricket database is now available as bulk downloads. Test, ODI and T20 ball-by-ball data covering 6,900+ matches from 2001 onwards and 3.8 million+ deliveries, refreshed twice daily. Take one table at a time as Parquet or CSV, or the whole database as a single DuckDB or SQLite file, either by clicking on the page or through one API call. Covers the schema for anyone going straight to the files, including the two added integer columns over_no and delivery_in_over and why the original ball column collides on the tenth delivery of an over, and why actual_delivery is not a unique key. Also the atomic-swap refresh, R2 edge delivery, the SQL API limits, and how the ODC-BY licence stays attached in the parquet footer and a README. Data source is Cricsheet under ODC-BY 1.0.", "twitter_status": "posted" }, { "id": "anthropic-field-marketer-claude-code-aug2026", "slug": "anthropic-field-marketer-claude-code-aug2026", "title": "AI Coders Are Not Just for Developers. An Anthropic Field Marketer Built His Own Weekly Sales Digest With Claude Code.", "date_published": "2026-08-27T16:00:00.000Z", "date_updated": null, "last_modified": "2026-08-27T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-field-marketer-claude-code-aug2026", "thumbnail": "/files/ANTHROPIC_MKTG_CLAUDE_CODE/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "ai-coders" ], "summary": "A read of Anthropic's post on Adam Ward, a field marketer with no engineering background who used Claude Code to build a Slack digest that reaches every sales rep on Monday morning, wired to BigQuery over MCP as the marketing team's source of truth. Covers the working habits that made it possible: briefing Claude like a new colleague, recording yourself thinking out loud and handing over the transcript, writing a fake version of the output first so there is a template to aim at, catching Claude's mistakes, starting small and piloting with a few users. Includes a ten-slide deck, plus pointers to Anthropic's marketing-ops and business-development write-ups, Boris Cherny on roles melting into five archetypes, and which AI coder you may already be paying for through a Google, ChatGPT or Claude subscription.", "twitter_status": "posted" }, { "id": "portfolio-agent-mutual-fund-nav-aug2026", "slug": "portfolio-agent-mutual-fund-nav-aug2026", "title": "My Portfolio Agent Could Not Look Up an Indian Mutual Fund NAV. Now It Can.", "date_published": "2026-08-27T15:00:00.000Z", "date_updated": null, "last_modified": "2026-08-27T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/portfolio-agent-mutual-fund-nav-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "mutual-funds" ], "summary": "The Tigzig portfolio agent at quants.tigzig.com can now resolve and fetch Indian mutual fund NAVs. Give it a fund name roughly as you would say it and it searches the AMFI scheme master, resolves the real scheme code, and where a name matches more than one scheme it lists the candidates and asks before fetching, because a fund typically exists as Direct and Regular plans with Growth and IDCW options on separate series. Short windows come back in the chat and longer histories as a CSV link. The agent also pulls prices, company profiles and financial statements for Yahoo Finance symbols, finds tickers by name, runs quant reports against a benchmark, produces technical analysis reports as HTML and PDF, and compares securities on return and risk. It does not give investment advice.", "twitter_status": "posted" }, { "id": "fdic-q2-2026-bank-credit-tremor-aug2026", "slug": "fdic-q2-2026-bank-credit-tremor-aug2026", "title": "FDIC Q2 2026 Numbers Are Live on TREMOR. 102 Quarters of Bank Balances, Delinquency and Charge-Offs.", "date_published": "2026-08-27T14:00:00.000Z", "date_updated": null, "last_modified": "2026-08-27T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/fdic-q2-2026-bank-credit-tremor-aug2026", "thumbnail": "/images/blog/fdicQ2_2026Aug2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants", "database-ai" ], "summary": "FDIC Q2 2026 Quarterly Banking Profile data is now on TREMOR, 102 quarters of balances, delinquency and charge-offs for every FDIC-insured bank back to Q1 2001, queryable in the browser or downloadable in full. Net charge-offs peaked at 0.70% in Q4 2024 and are now 0.56%, almost exactly the Q3 2007 level and still above 2019's 0.50%. Lending to other financial firms is 18.6% of the book at $2.6 trillion against 3.3% before the last crisis, and supplied 54% of loan growth over the year. Sector detail on C&I, real estate, auto and cards, plus an explanation of the noncurrent measure and how it splits between 90+ days past due and nonaccrual.", "twitter_status": "posted" }, { "id": "markdown-to-pdf-api-typesetting-aug2026", "slug": "markdown-to-pdf-api-typesetting-aug2026", "title": "A Free Markdown to PDF Converter Over API, With Typesetting Options. No Signup, No Key.", "date_published": "2026-08-25T15:30:00.000Z", "date_updated": null, "last_modified": "2026-08-25T15:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/markdown-to-pdf-api-typesetting-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "tags": [ "converters-tools", "ai-coders" ], "summary": "A rebuilt markdown to PDF converter on Tigzig, free with no signup, no API key and no auth. Markdown can be pasted into the browser, POSTed to the API as JSON, or uploaded as a text or md file. Typesetting additions include explicit page breaks via a pagebreak directive, tables, nested lists, task lists and fenced code blocks, Greek letters, maths operators and arrows, currency signs from any script so a taka or rupee renders instead of an empty box, and emoji mapped to monochrome symbols with a small dot as the fallback. Also covers why an AI agent is usually better off rendering PDFs natively, and three tips for doing that at volume.", "twitter_status": "posted" }, { "id": "cricket-db-new-fields-sql-docs-aug2026", "slug": "cricket-db-new-fields-sql-docs-aug2026", "title": "Three New Things on the Tigzig Cricket Database. New match_info Fields, SQL Laid Out in the Docs, and a Quicker Postgres.", "date_published": "2026-08-25T15:45:00.000Z", "date_updated": null, "last_modified": "2026-08-25T15:45:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-db-new-fields-sql-docs-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "Three changes to the Tigzig open cricket database. match_info gains 14 fields taken straight from Cricsheet, including outcome, eliminator, winner_innings, method, target_runs and declared, which removes several common workarounds. The API docs now state what SQL the endpoint accepts so a caller can write the query without probing, covering CTEs, window functions, correlated subqueries and set operations. Postgres has been tuned and is faster than before, with DuckDB still the quicker of the two engines. The database refreshes twice a day and recent Test and T20 fixtures are already in. Data source is Cricsheet under ODC-BY 1.0.", "twitter_status": "posted" }, { "id": "mfpro-nav-batch-calls-and-guides-aug2026", "slug": "mfpro-nav-batch-calls-and-guides-aug2026", "title": "Running Hundreds of API Calls to Pull NAV History, One Scheme Per Call? Batch 50 Into One.", "date_published": "2026-08-25T16:00:00.000Z", "date_updated": null, "last_modified": "2026-08-25T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-nav-batch-calls-and-guides-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "tags": [ "mutual-funds", "database-ai" ], "summary": "How to cut the call count when pulling India mutual fund NAV history. The NAV endpoint accepts up to 50 scheme codes in a single call, turning a two hundred fund watchlist into four calls, and the full database from April 2006 onward is available as a single download. Collects the surrounding references in one place: the API FAQ, a plain guide to what each error code means across all three Tigzig APIs, the MFPRO data dictionary, the scheme master file with every scheme and its latest NAV, an ISIN checker that distinguishes a bad identifier from genuinely absent data, and where SIF data lives.", "twitter_status": "posted" }, { "id": "cricket-full-data-download-coming-aug2026", "slug": "cricket-full-data-download-coming-aug2026", "title": "Full Data Download of the Cricket Database, Coming This Week. T20, ODI and Test, Ball by Ball.", "date_published": "2026-08-24T17:30:00.000Z", "date_updated": null, "last_modified": "2026-08-24T17:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-full-data-download-coming-aug2026", "thumbnail": "/images/blog/cricketDownloadsComingAug2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "duckdb" ], "summary": "Advance notice that the Tigzig open cricket database is getting a full bulk download, covering T20, ODI and Test ball-by-ball data, 6,900+ matches from 2001 onwards and about 3.8 million deliveries. Four tables ship as individual files in parquet, gzipped CSV and zipped CSV, with DuckDB and SQLite offered as an all-tables option. Three of the tables are ball-by-ball, one per format, and match_info holds the two team names, venue, dates, toss, result, the match officials where Cricsheet records them, and the player of the match. Retrievable in a single API call or by hand from the web page, on the same pattern as the mutual fund NAV downloads. Existing live SQL access over Postgres, DuckDB, the REST API and the MCP server is unchanged. Free, no signup, no key. Data source is Cricsheet under ODC-BY 1.0.", "twitter_status": "posted" }, { "id": "mf-nav-api-three-upgrades-aug2026", "slug": "mf-nav-api-three-upgrades-aug2026", "title": "Three Upgrades on the India Mutual Fund NAV API. Easier to Pull, Whatever You Already Use.", "date_published": "2026-08-24T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-24T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mf-nav-api-three-upgrades-aug2026", "thumbnail": "/images/blog/mfproEasierToPullAug2026.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "excel" ], "summary": "Three changes to the free India mutual fund NAV API aimed at Power Query, Excel WEBSERVICE and browser-based trackers. The all-scheme snapshot is now gzipped on the wire, dropping from 9.3 MB to about 1.1 MB on the same link. Any NAV call accepts format=csv and returns plain rows instead of JSON. The bulk download files now send a CORS header so they can be fetched from a web page. Includes a paste-ready Excel LET formula that pulls ten funds in one call using the batch schemes= parameter, which takes up to 50 codes, plus links to the MFPRO docs, FAQ and ISIN checker.", "twitter_status": "posted" }, { "id": "tremor-ask-claude-agent-aug2026", "slug": "tremor-ask-claude-agent-aug2026", "title": "Reading Macro Charts on TREMOR? Send Them to the AI Agent Sitting There and Get a Read With the Latest News.", "date_published": "2026-08-24T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-24T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-ask-claude-agent-aug2026", "thumbnail": "/images/blog/tremorAskAiAug2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "ai-coders" ], "summary": "An AI agent now sits inside the TREMOR macro dashboard and will read the charts in front of you, with three ways to ask. What changed this week sends every chart on the dashboard as one briefing, Analyze selected charts takes up to five and looks for trends, inflection points and correlations between them, and Deep research does the same while going out to newswires, ratings agencies and central bank releases and citing what it finds. The prompt is editable before it sends, output copies as markdown or prints to PDF, and past runs persist in browser storage. Each chart reaches the agent as its series, values, dates and source label, so it knows the provenance and how much history it has. Also the honest caveats: this is commentary rather than a research note, and the read is heavily shaped by the prompt, which is why the prompt is left customizable.", "twitter_status": "posted" }, { "id": "yfin-integrate-your-tools-aug2026", "slug": "yfin-integrate-your-tools-aug2026", "title": "Pulling Yahoo Finance Data Into Your Own Trackers. Excel, Sheets, a Web Tool, or Straight From an AI Agent.", "date_published": "2026-08-24T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-24T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/yfin-integrate-your-tools-aug2026", "thumbnail": "/images/blog/yfinIntegrateAug2026.png", "status": "published", "deck": null, "tags": [ "excel", "portfolio-quants" ], "summary": "Getting Yahoo Finance prices, calendars, financials and recommendations into whatever you already work in, through the free keyless Tigzig YFIN API. In Excel that means Power Query, Python-in-Excel via xlwings Lite, VBA or a WEBSERVICE formula, each with its own trade-off; Google Sheets needs a few lines of Apps Script; a web tool needs one JavaScript call; and an AI agent needs no code at all because it is on MCP. The practical route for most people is to hand the API docs to an agent with a desktop app, which edits the workbook directly. Also four things worth knowing: Yahoo wants the exchange ticker rather than the company name, there is now a search endpoint aimed at agents that stops them guessing symbols from memory, the /v1/excel/ statement endpoints return sheet-ready rows while the plain ones are pipe delimited, and WEBSERVICE should batch 25 tickers a call rather than firing one cell each.", "twitter_status": "posted" }, { "id": "api-error-hints-aug2026", "slug": "api-error-hints-aug2026", "title": "If a Tigzig API Keeps Returning the Same Error, Read the Response Body. There Is Almost Always a Hint in It.", "date_published": "2026-08-23T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-23T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/api-error-hints-aug2026", "thumbnail": "/images/blog/apiErrorHintsAug2026.png", "status": "published", "deck": null, "tags": [ "database-ai", "ai-coders" ], "summary": "Tigzig's APIs now try to work out what actually broke and put the answer in the error message, usually naming the exact character or the exact table, across the cricket database, the mutual fund and Yahoo Finance endpoints and others. The commonest failure on GET endpoints is not the SQL but the URL mangling it in transit, and the message will say so directly. The catch is that most tooling hides response bodies on an error: curl shows it unless -f is used, and exits 0 on a 400 so a shell chain carries on regardless; urllib raises an exception carrying nothing but the status; requests does not raise at all and leaves the hint sitting in r.text; and fetch never throws on a 400, so a try/catch around it catches nothing. Includes what to call in each case to actually see the hint.", "twitter_status": "posted" }, { "id": "db-mcp-test-cricket-aug2026", "slug": "db-mcp-test-cricket-aug2026", "title": "Test Cricket Is Now Live in the Tigzig Open Cricket Database, Alongside T20 and ODI. 3.8 Million Deliveries.", "date_published": "2026-08-23T12:00:00.000Z", "date_updated": null, "last_modified": "2026-08-23T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/db-mcp-test-cricket-aug2026", "thumbnail": "/images/blog/dbMcpTestCricketAug2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "mcp-servers" ], "summary": "Test cricket joins T20 and ODI in the free Tigzig cricket database, ball by ball on both Postgres and DuckDB, reachable as a REST API or an MCP server with no key and no auth. About 6,900 matches and 3.8 million deliveries spanning twenty five years, Test back to 2001, ODI to 2002 and T20 to 2005, refreshed twice a day and sourced from Cricsheet. Three ball-by-ball tables, one per format, join to a match_info table carrying venue, dates, teams, toss and result. Shipped alongside Test are over_no and delivery_in_over columns, added because the older decimal ball column collides on long overs, where the tenth delivery is written 1.10 and sorts identically to the first at 1.1, putting rows out of order in exactly the overs where a wide or no-ball has pushed past six balls.", "twitter_status": "posted" }, { "id": "trump-trades-filings-aug2026", "slug": "trump-trades-filings-aug2026", "title": "What Trump Has Been Buying, Selling and Holding. The Filings, Extracted and Searchable.", "date_published": "2026-08-24T12:00:00.000Z", "date_updated": null, "last_modified": "2026-08-24T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/trump-trades-filings-aug2026", "thumbnail": "/images/blog/trumpTradesAug2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants", "database-ai" ], "summary": "Trump's public financial filings, extracted and made searchable: trades, assets, income, liabilities and gifts. Two different documents sit behind it. The periodic trade reports, filed within 30 to 45 days of each trade and covering securities only, run to 15 filings and 5,368 trades, though the distribution is lopsided, with fourteen filings holding 1,726 trades between them and a single 14 May filing carrying 3,642, almost all equities. The annual disclosure is a separate 927 page document with 28,007 records including 21,285 trades, and because OGE rules let it omit trades already reported, neither document alone is the full year. Also the extraction method for each, which differed sharply because one arrives as a scan and the other as a digital document, and the caveats that matter before analysis, including that form values are ranges rather than exact figures.", "twitter_status": "posted" }, { "id": "tremor-chart-layout-aug2026", "slug": "tremor-chart-layout-aug2026", "title": "TREMOR Opened With Sixteen Charts in a Fixed Grid. That Was My View. Now You Can Change All of It.", "date_published": "2026-08-24T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-24T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-chart-layout-aug2026", "thumbnail": "/images/blog/tremorChartsAug2026.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "database-ai" ], "summary": "The TREMOR macro dashboard used to open on a fixed grid of sixteen charts chosen by its author, and that grid is now fully editable: delete a chart, drag two side by side, swap one series for another, resize or expand, overlay two series on one axis, switch to year-on-year or month-on-month, apply a moving average of any period, extend the window to thirty years, hide labels, copy the underlying data, export a PNG for a deck, or add something new from around three hundred indicators. Behind it sit roughly 330 series, about 250 US and 50 India, weighted heavily towards credit through FDIC bank aggregates, NCUA credit union data and NY Fed household debt, alongside FRED macro, MoSPI and RBI for India, and odds and ends like Shiller, Manheim and European bond yields. Free, no signup, and reachable by an AI agent through the API or MCP.", "twitter_status": "posted" }, { "id": "yfin-three-fixes-aug2026", "slug": "yfin-three-fixes-aug2026", "title": "Three Fixes on the Yahoo Finance API This Week, All of Them Found by Reading My Own Error Logs.", "date_published": "2026-08-23T12:00:00.000Z", "date_updated": null, "last_modified": "2026-08-23T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/yfin-three-fixes-aug2026", "thumbnail": "/images/blog/yfinFixesAug2026.png", "status": "published", "deck": null, "tags": [ "portfolio-quants", "excel" ], "summary": "Three fixes to the free Tigzig Yahoo Finance API, each one found by reading the server's own error logs rather than waiting for a report. The worst of them had price history failing and then blaming the caller's ticker symbol or date range when both were fine and the real fault was upstream, so someone at the other end would have sat re-typing a symbol that was correct all along. Detailed company info could also run long and return nothing, throwing away the tickers that had already come back cleanly, and it now returns whatever it managed to fetch. A single ISIN dropped into a symbol list used to fail the entire request, and the rest of the list now comes back regardless. Also why a WEBSERVICE-based Excel tracker trips rate limits, the xlwings Lite workbook that avoids it, and why Indian tickers need the exchange symbol.", "twitter_status": "posted" }, { "id": "tremor-monthly-update-aug2026", "slug": "tremor-monthly-update-aug2026", "title": "Shiller CAPE Is at 41.2. In 145 Years the Only Period Higher Was the Dot-Com Peak.", "date_published": "2026-08-23T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-23T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-monthly-update-aug2026", "thumbnail": "/images/blog/tremorMonthlyAug2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants", "database-ai" ], "summary": "This month's Tremor update, with the valuation reading leading it: Shiller CAPE at 41.2, a level exceeded only during the dot-com peak across 145 years of history. Alongside it, the US 30-year Treasury at 5.23% makes 2026 the first year since 2007 that the long bond has traded above 5.2%, US auto loans are running a 90+ day delinquency flow of 3.00% which is the highest since the first quarter of 2010, and Brent has recovered to the mid nineties after a spike above 138 in April and a fall to around 70 by late June. On the data side, India bank credit history extends back to February 2017 giving 113 months across 33 sectors, NY Fed household debt for Q2 2026 is loaded across 64 series, and BSE Sensex daily prices now reach back to 1997. Free, no signup, usable as an interactive tool, an API, over MCP or as a full database download.", "twitter_status": "posted" }, { "id": "cricket-ask-your-ai-aug2026", "slug": "cricket-ask-your-ai-aug2026", "title": "Have Your AI Analyze the Cricket Data on Tigzig. 24+ Years of Ball by Ball, ODI and T20, Across 6000+ Matches.", "date_published": "2026-08-22T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-22T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cricket-ask-your-ai-aug2026", "thumbnail": "/images/blog/cricketAskYourAiAug2026.png", "status": "published", "deck": null, "tags": [ "database-ai", "ai-coders" ], "summary": "One pasted instruction is enough to put an AI agent onto 24 years of ball-by-ball cricket, ODI and T20 across more than six thousand matches, free and without signing up. Pointed at the machine-readable catalog at api.tigzig.com, the agent works out which service holds the cricket data, opens its OpenAPI spec, discovers the tables and columns, and writes the SQL itself. Three tables per engine joined on match_id, with match_info carrying venue, dates, teams, toss and result so a venue or toss cut needs no second source, and because the data is deliveries rather than pre-aggregated totals anything expressible in SQL is available. Works with Claude chat and Cowork, ChatGPT Work, and coding agents including Claude Code, Antigravity and Codex. Also the new cross-engine validation suite, which on its first run caught a single Test match present on one engine and missing from the other.", "twitter_status": "posted" }, { "id": "nyfed-household-debt-q2-2026", "slug": "nyfed-household-debt-q2-2026", "title": "NY Fed Q2 2026 Household Debt. The Headline Delinquency Flow Fell, but Auto and Mortgage Went the Other Way.", "date_published": "2026-08-21T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-21T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/nyfed-household-debt-q2-2026", "thumbnail": "/images/blog/nyfedHouseholdAug2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants", "database-ai" ], "summary": "The Q2 2026 NY Fed household debt release read better at the headline and worse underneath. The overall flow into 90+ day delinquency fell from 3.26% two quarters ago to 2.57%, but almost all of that improvement is student loans unwinding after the reporting restart. Mortgage, which is about 70% of what households owe, has seen its 90+ flow rise in six of the last seven quarters to 1.52%, back around 2005-06 levels even though the stock at 0.99% is far below the 2.23% of Q3 2007. Auto is the stretched one: a 90+ stock of 5.49% is the second highest reading since the series began in 2003, and its flow at 3.00% now exceeds the 2.34% of Q3 2007. Cards sit above their 2007 flow too. Full then-and-now comparison against the quarter before the GFC cycle turned, plus why the NY Fed credit-file panel, FDIC and NCUA do not reconcile with each other.", "twitter_status": "posted" }, { "id": "tigzig-agent-data-surface-aug2026", "slug": "tigzig-agent-data-surface-aug2026", "title": "Point Your AI Agent at api.tigzig.com and It Works Out Which Dataset It Needs. 38 Million Rows Across India and the US, Free.", "date_published": "2026-08-20T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-20T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-agent-data-surface-aug2026", "thumbnail": "/images/blog/tigzigAgentDataSurfaceAug2026.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "vigil", "portfolio-quants" ], "summary": "One address an AI agent can be pointed at, api.tigzig.com, from which it works out which dataset a question needs and pulls the file itself. Close to 38 million rows across India and the US, curated and refreshed daily, free with no signup or key: twenty years of Indian mutual fund NAVs, around 330 macro, credit and valuation indicators across eight groups, and seven filing datasets for Indian listed companies covering credit ratings, insider trading, promoter pledge, encumbrance, takeover, surveillance flags and related party transactions. Usable three ways, by hand in the apps, through the API from code, or as whole-table downloads in CSV, Parquet, SQLite or DuckDB. Includes a downloads manifest carrying row counts and a generated_at stamp so a script can tell whether anything changed before re-pulling, and practical notes on renamed schemes still resolving from an old ISIN.", "twitter_status": "posted" }, { "id": "mfpro-nav-into-excel-aug2026", "slug": "mfpro-nav-into-excel-aug2026", "title": "Pull India Mutual Fund NAVs Straight Into Excel. 50, 100 or More Schemes, in a Few Clicks.", "date_published": "2026-08-19T12:00:00.000Z", "date_updated": null, "last_modified": "2026-08-19T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-nav-into-excel-aug2026", "thumbnail": "/images/blog/mfproNavDataAug2026.png", "status": "published", "deck": null, "tags": [ "mutual-funds", "excel" ], "summary": "Getting India mutual fund NAV history into Excel for fifty or a hundred schemes at a time, free and without signing in, from the MFPRO Data and API screen. Comes out as a plain CSV that opens as ordinary rows, ready for the returns, CAGR and drawdown work you would normally do with formulas, and an AI assistant sitting alongside Excel will write those formulas or fetch and compute the whole thing itself. Also the three things that break an analyst's join: SEBI's February categorisation rework means the same fund can appear under a new category or scheme name between two files, so match on the AMFI scheme code rather than the name; ISIN coverage is not complete, with roughly one in fourteen live schemes carrying none; and Direct, Regular, Growth and IDCW are separate scheme codes, so one fund is usually four rows. Full history is 37 million rows from April 2006, too large for Excel.", "twitter_status": "posted" }, { "id": "db-mcp-both-formats-both-engines-aug2026", "slug": "db-mcp-both-formats-both-engines-aug2026", "title": "T20 and ODI Ball by Ball Are Now on Both Engines, Postgres and DuckDB. Run Your SQL Directly on My Server.", "date_published": "2026-08-19T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-19T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/db-mcp-both-formats-both-engines-aug2026", "thumbnail": "/images/blog/dbMcpBothEnginesAug2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "mcp-servers" ], "summary": "Both formats now sit on both engines: T20 and ODI ball-by-ball are queryable on Postgres and on DuckDB, so a query no longer has to follow the format to a particular engine. Free, no auth and no key, refreshed twice a day, and open to the kind of work the data is actually for, strike rates against pace versus spin, run rates through the death overs, head-to-head records across a decade, dismissal patterns by bowler type and home-away splits by venue. Postgres has moved off a managed instance onto self-hosted hardware and is much faster for it. Also what the guards refuse and why schema discovery is deliberately left open, and a note that any Claude, ChatGPT or Gemini subscription already includes a desktop agent that can fetch and analyse this over plain HTTP without MCP.", "twitter_status": "posted" }, { "id": "db-mcp-url-encoding-errors-aug2026", "slug": "db-mcp-url-encoding-errors-aug2026", "title": "Getting Errors Running SQL Against the Tigzig Cricket Database? The Most Common Failure Is the URL, Not Your SQL.", "date_published": "2026-08-17T15:00:00.000Z", "date_updated": null, "last_modified": "2026-08-17T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/db-mcp-url-encoding-errors-aug2026", "thumbnail": "/images/blog/dbMcpDocsUpdateAug2026.png", "status": "published", "deck": null, "tags": [ "database-ai", "mcp-servers" ], "summary": "The commonest error on the free Tigzig cricket SQL endpoint is not the SQL, it is the URL it travelled in. A query sent as a GET has to be percent encoded first, and that step goes wrong in predictable ways: a plus sign already means a space inside a URL so an addition silently loses its operator, line breaks get eaten and words fuse together, and some clients stop encoding at the first bracket and send a truncated query. The SQL on screen is right and a different query arrives. Error responses now name the cause where they can and otherwise quote the SQL back exactly as received. GET stays because it is all a plain browser, an agent-driven browser or a no-code HTTP node can do. Also the engine and dialect split between Postgres and DuckDB, the thousand-row cap and its truncated flag, and schema discovery.", "twitter_status": "posted" }, { "id": "mfpro-composition-analytics-aug2026", "slug": "mfpro-composition-analytics-aug2026", "title": "MFPRO Composition Analytics Is Live. Portfolios for 24 of India's Largest Equity Funds, Reconciled Against the AMC's Own Printed Totals.", "date_published": "2026-08-17T09:00:00.000Z", "date_updated": null, "last_modified": "2026-08-17T09:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-composition-analytics-aug2026", "thumbnail": "/images/blog/mfproCompositionAug2026.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "portfolio-quants" ], "summary": "Portfolio composition analytics for 24 of India's largest equity funds across large cap, mid cap, small cap, flexi cap, focused and value, spanning ten snapshots from June 2025 to July 2026. Compare one fund over time, several against each other, or against a benchmark, with copy-to-Excel, the original disclosures, ISIN remaps and the reconciliation notes alongside. The work that makes months comparable is the interesting part: a face-value split moves a company to a new ISIN and looks like a position appearing from nowhere unless the two are stitched, and money market instruments arrive as dozens of separate lines that need grouping. Cash is the real divergence between fund houses, some folding it into the printed grand total and others listing it below, which is worth several per cent of a fund. Every fund-month is reconciled against the AMC's own printed total, 228 of 240 to the rupee with the remainder published with reasons.", "twitter_status": "posted" }, { "id": "db-mcp-cricket-sql-queries-aug2026", "slug": "db-mcp-cricket-sql-queries-aug2026", "title": "Run Your Own SQL on My Cricket Database. T20 and ODI Ball by Ball Since 2002, Free, No Signup and No Key.", "date_published": "2026-08-17T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-17T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/db-mcp-cricket-sql-queries-aug2026", "thumbnail": "/images/blog/dbMcpQuestionsAug2026.png", "status": "published", "deck": null, "tags": [ "database-ai", "mcp-servers" ], "summary": "An open SQL endpoint over men's international cricket ball-by-ball data, free with no signup or key, covering T20 and ODI since 2002 across more than two million deliveries and six thousand matches, reachable as a REST API or as an MCP connector. Match-level detail sits in a second table joined on match_id, so questions like whether a ground favours batting first, or how a batter fares against one bowler, are a single query. Includes the questions the two tables can answer beyond the obvious, and the data cautions that decide whether an answer is right: match_info spans all three formats so the denominator misleads unless filtered, the ball tables are one format per engine, Associate nations are pooled in unless narrowed, and results cap at a thousand rows behind a truncated flag. Also a note on latency while the service moves to dedicated infrastructure.", "twitter_status": "posted" }, { "id": "vigil-related-party-transactions-aug2026", "slug": "vigil-related-party-transactions-aug2026", "title": "Related Party Transactions for Around 750 Listed Indian Companies Are Now on VIGIL, With the March Half-Yearly Filings In.", "date_published": "2026-08-16T12:00:00.000Z", "date_updated": null, "last_modified": "2026-08-16T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-related-party-transactions-aug2026", "thumbnail": "/images/blog/vigilRptAug2026.png", "status": "published", "deck": null, "tags": [ "vigil" ], "summary": "Related party transaction filings for around 750 listed Indian companies on the free VIGIL app, updated to 12 August 2026 with the March half-yearly filings in. About 360,000 rows covering roughly 44,000 counter parties across three half-year periods, browsable in the app, queryable through an open no-key API, or downloadable whole as CSV, Parquet or SQLite. How much a company files turns out to track how many legal entities its group runs rather than how much business it does: the median company files 195 rows while nine file more than 5,000 each, and about 69% of all rows are transactions with group companies. Includes the data nuances that matter before analysis, among them a catch-all transaction type covering 56% of rows, counter parties stored as names rather than identifiers, dates held as text that sort wrongly, and deliberate negative balances meaning payable.", "twitter_status": "posted" }, { "id": "anthropic-bd-claude-cowork-aug2026", "slug": "anthropic-bd-claude-cowork-aug2026", "title": "Anthropic's Business Development Team Runs Inbound and Outbound With Claude Cowork. What the Post Actually Says.", "date_published": "2026-08-16T09:00:00.000Z", "date_updated": null, "last_modified": "2026-08-16T09:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-bd-claude-cowork-aug2026", "thumbnail": "/files/ANTHROPIC_BD_AUG2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "ai-coders", "chatgpt-integrations" ], "summary": "A summary deck and read of Anthropic's own post on how its business development team runs inbound and outbound with Claude Cowork, where roughly five hours a day of manual inbox and research work now goes into strategic work, and over a hundred accounts get researched overnight rather than one at a time. The four things the post calls out: build the knowledge base before the workflows, give Claude worked examples including each rep's own writing style, keep a person on every send, and write corrections back into the skill so a mistake is recorded once. Plus what actually comes with a Claude or ChatGPT subscription, when to use Claude in Excel over Cowork, and links to Anthropic's other practical guides including the argument for HTML over Markdown as an output format.", "twitter_status": "posted" }, { "id": "vigil-takeover-sast-filings-aug2026", "slug": "vigil-takeover-sast-filings-aug2026", "title": "Is a Large Investor Building a Position or Selling Down in Your Stock? Takeover Filings on VIGIL, Refreshed Four Times a Day.", "date_published": "2026-08-15T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-15T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-takeover-sast-filings-aug2026", "thumbnail": "/images/blog/vigilTakeoverAug2026.png", "status": "published", "deck": null, "tags": [ "vigil" ], "summary": "SEBI takeover disclosures for Indian listed companies on the free VIGIL app, refreshed four times a day, showing who has been building or cutting a large stake. Anyone crossing 5% of a company has to tell the exchange, and every 2% move after that. Pull up a company to see who has been buying into it, or search an acquirer to see everywhere they have been active. About 12,000 filings across 1,500 companies from January 2024, of which around 3,600 come from outside the promoter group, where mutual funds, insurers and private equity show up. Includes the caution that debenture trustees also sit in that non-promoter group holding shares as security rather than as a view on the company. Downloadable as CSV, Parquet or SQLite, or hand the open no-key API straight to an AI agent with the prompt included.", "twitter_status": "posted" }, { "id": "md-to-pdf-rebuild-aug2026", "slug": "md-to-pdf-rebuild-aug2026", "title": "I Rebuilt the Markdown to PDF Converter on tigzig.com. Paste Text, Get a Properly Typeset PDF, or Have Your Agent Do It Over MCP.", "date_published": "2026-08-14T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-14T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/md-to-pdf-rebuild-aug2026", "thumbnail": "/images/blog/mdToPdfRebuildAug2026.png", "status": "published", "deck": "release", "tags": [ "converters-tools", "mcp-servers" ], "summary": "The free Markdown to PDF converter on tigzig.com has been rebuilt after a rise in traffic showed how much of the input was not rendering: headings below a certain level were dropped, nested lists came out flat and code blocks lost their formatting. It now handles page breaks on demand with a :::pagebreak::: marker, tables, nested lists, task lists and fenced code, Greek letters, maths symbols and arrows, and it will not fall over on emoji. Plain .txt keeps its line breaks. Paste into the browser tool, or drive it from an agent over MCP or a plain no-key REST call. Also where it earns its place in practice, which is automation, as a step at the end of a pipeline that needs a PDF.", "twitter_status": "posted" }, { "id": "vigil-insider-trading-disclosures-aug2026", "slug": "vigil-insider-trading-disclosures-aug2026", "title": "Insider Trading Disclosures for Indian Listed Companies, on Vigil, Updated Four Times a Day.", "date_published": "2026-08-13T09:00:00.000Z", "date_updated": null, "last_modified": "2026-08-13T09:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-insider-trading-disclosures-aug2026", "thumbnail": "/images/blog/vigilInsiderAug2026.png", "status": "published", "deck": null, "tags": [ "vigil" ], "summary": "Insider trading disclosures for Indian listed companies on the free VIGIL app, refreshed four times a day, showing whether the people inside a company have been buying or selling, when, how much, and the holding before and after. These are filings mandated by SEBI's Prohibition of Insider Trading regulations, covering promoters, the promoter group, directors, key managerial personnel and designated employees once a quarter's trades cross a value threshold, so every row carries a named person and a date. Around 38,000 disclosures across about 1,500 companies going back to early 2024, downloadable whole as CSV, Parquet or SQLite or queryable through an open API with no key. Also a caution for anyone grouping by role: the category is free text, with 18 spellings for roughly six roles and one in six rows blank, published as filed rather than recoded.", "twitter_status": "posted" }, { "id": "mfpro-nav-fewer-calls-batch-files-parquet-aug2026", "slug": "mfpro-nav-fewer-calls-batch-files-parquet-aug2026", "title": "Pulling Indian Mutual Fund NAVs One Scheme at a Time? Three Ways to Get the Same Data in Far Fewer Calls.", "date_published": "2026-08-13T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-13T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-nav-fewer-calls-batch-files-parquet-aug2026", "thumbnail": "/images/blog/mfproFewerCallsAug2026.png", "status": "published", "deck": null, "tags": [ "mutual-funds", "duckdb" ], "summary": "Three routes already in the free MFPro India mutual fund NAV API for callers fetching one scheme per request: batch up to 50 schemes in a single call with a date range attached, take the single latest-NAV file covering all ~38,000 schemes including matured ones left in deliberately, or take the full daily history as one file of a little over 37 million records back to April 2006, all refreshed three times a day. A fourth route skips the download entirely because the history file is parquet and supports range requests, so DuckDB or Polars query it straight off the URL. Worked numbers for a daily pull of every active fund, how active is derived from a 45-day NAV window, and how conditional requests return a 304 with no body on days nothing changed.", "twitter_status": "posted" }, { "id": "db-mcp-four-days-ssrf-query-guards-aug2026", "slug": "db-mcp-four-days-ssrf-query-guards-aug2026", "title": "Four Days of an Open SQL Endpoint. What Real Cricket Queries Needed, and the SSRF Someone Found in the First Couple of Hours.", "date_published": "2026-08-12T16:00:00.000Z", "date_updated": null, "last_modified": "2026-08-12T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/db-mcp-four-days-ssrf-query-guards-aug2026", "thumbnail": "/images/blog/dbMcpFourDaysAug2026.png", "status": "published", "deck": null, "tags": [ "database-ai", "security" ], "summary": "Four days after opening a keyless SQL endpoint over ball-by-ball cricket data, 650+ successful calls split into two kinds of traffic: real cricket analysis and exploitation attempts. Reading what callers were actually refused showed two guard rules were turning away about 62% of legitimate queries, with the join limit set so low that ordinary two-table work broke and schema discovery blocked outright, so the guards were reworked and the errors now say what to fix. Separately, an SSRF found within the first couple of hours succeeded because read-only database connections had external access left on; caught in monitoring about ten minutes later and closed inside the hour, with the engine hardened since. Also what analysts are doing with it, the documented query limits, the two schema traps that make counts fail to reconcile, and a fuller account of how the SSRF ran.", "twitter_status": "posted" }, { "id": "mf-nav-api-faq-aug2026", "slug": "mf-nav-api-faq-aug2026", "title": "MF NAV API FAQ. Why Am I Getting an Error, Why Is There No SIF NAV, and Why Does the AUM Look Wrong?", "date_published": "2026-08-12T14:00:00.000Z", "date_updated": null, "last_modified": "2026-08-12T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mf-nav-api-faq-aug2026", "thumbnail": "/images/blog/mfFaqAug2026.png", "status": "published", "deck": null, "tags": [ "mutual-funds" ], "summary": "Twenty-six questions the free MFPRO India mutual fund NAV API actually got from callers over seven days, 25,000+ calls and 450+ file downloads. Why a SIF identifier 404s and where SIF data really lives, why the AUM looks too low (it is per scheme and a quarterly average, not per fund and not month-end), why one fund returns several rows, why a valid ISIN can return nothing and how reinvestment ISINs and matured schemes change that, why an IDCW plan shows near-zero NAV return, why the SEBI category rename means passing two names, why dates and unknown parameters fail loudly instead of being guessed, how to assemble a history that spans a manager change, and the aaum_cr column rename with the schema_version to watch. Plus the anomalies published as filed rather than cleaned.", "twitter_status": "posted" }, { "id": "chatgpt-work-ai-worker-analytics-aug2026", "slug": "chatgpt-work-ai-worker-analytics-aug2026", "title": "ChatGPT Work Comes With Your ChatGPT Go or Pro Subscription. Here Is What It Can Do.", "date_published": "2026-08-12T12:00:00.000Z", "date_updated": null, "last_modified": "2026-08-12T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/chatgpt-work-ai-worker-analytics-aug2026", "thumbnail": "/images/blog/chatGPTWork.png", "status": "published", "deck": null, "tags": [ "ai-coders", "chatgpt-integrations" ], "summary": "ChatGPT Work is included with a ChatGPT Go or Pro subscription and plays the same role as Claude Cowork, reading CSVs and parquet files, running analysis, building models, tools, automations, reports and presentations, and calling external APIs like FRED or the free Tigzig ones to pull data. A bit clunky next to Cowork, but only weeks old. Three copy-and-run prompts to try it with: a FRED macro report rendered to portrait HTML pages and converted to PDF, an instruction to go find the QRep comparison API unaided and build an interactive HTML risk tool from 81 metrics across six large-cap names against SPY, and a 168 MB parquet of live India mutual fund NAV data with 37 million records to explore from scratch.", "twitter_status": "posted" }, { "id": "db-mcp-cricket-ball-by-ball-api-aug2026", "slug": "db-mcp-cricket-ball-by-ball-api-aug2026", "title": "A Database Server With API and MCP You Can Point an AI Agent At, With Ball-by-Ball Cricket Behind It.", "date_published": "2026-08-09T17:00:00.000Z", "date_updated": null, "last_modified": "2026-08-09T17:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/db-mcp-cricket-ball-by-ball-api-aug2026", "thumbnail": "/images/blog/dbMcpCricketAug2026.png", "status": "published", "deck": "release", "tags": [ "database-ai", "mcp-servers" ], "summary": "A free database server on TigZig exposing read-only SQL over two engines through both a REST API and an MCP connector, with men's international cricket ball-by-ball data behind it, refreshed daily. ODI runs on Postgres and T20 on DuckDB under the same column names, covering about 1.36 million ODI deliveries from 2002 and 786 thousand T20 deliveries from the first men's T20 international in February 2005, across roughly 6,000 matches, with a wider match table that also carries Tests. No sign-up, auth or key. Notes on the security work that open SQL demands, why the limits are per IP, and why the error responses hand back the real schema and a worked call so an agent can correct itself. Data from Cricsheet, pipeline in live testing so treat it as provisional.", "twitter_status": "posted" }, { "id": "google-antigravity-ai-coding-agent-aug2026", "slug": "google-antigravity-ai-coding-agent-aug2026", "title": "If You Pay Rs 1,950 a Month to Google, You Already Have Antigravity, a Full AI Coding Agent.", "date_published": "2026-08-09T06:30:00.000Z", "date_updated": null, "last_modified": "2026-08-09T06:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/google-antigravity-ai-coding-agent-aug2026", "thumbnail": "/images/blog/antigravityAug2026.png", "status": "published", "deck": null, "tags": [ "ai-coders" ], "summary": "Antigravity is Google's agentic coding tool, and anyone on the Rs 1,950 a month Google subscription already has access to it at raised limits, with a free tier available for trying it first. Because it is a full coding agent rather than a chat window, it manages local files, pulls data straight from feeds like Yahoo Finance or FRED, builds custom HTML dashboards, and helps with Excel automation through VBA, Power Query M code or Python in Excel via xlwings Lite. The developer-style interface can be softened into something closer to a chat app by hiding the file panels and switching to a light theme. It runs on your own machine with terminal access, so the per-command approval prompt should stay on. A note on where it sits against Claude Code, which remains the primary tool here on quality.", "twitter_status": "posted" }, { "id": "nse-sebi-surveillance-667-stocks-aug2026", "slug": "nse-sebi-surveillance-667-stocks-aug2026", "title": "667 Stocks Are Under NSE and SEBI Surveillance. Almost All of Them Sit Outside the Nifty 500.", "date_published": "2026-08-08T18:30:00.000Z", "date_updated": null, "last_modified": "2026-08-08T18:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/nse-sebi-surveillance-667-stocks-aug2026", "thumbnail": "/images/blog/vigilSurvAug2026.png", "status": "published", "deck": null, "tags": [ "vigil" ], "summary": "As of 7 August 2026 there are 667 Indian listed companies carrying an NSE or SEBI surveillance flag, and the breakdown shows it is overwhelmingly a small and micro cap story. Only 20 sit in the Nifty 500, two of those in the Nifty 100 and none in the Nifty 50. Another 24 are in the Microcap 250. The remaining 623 fall outside the Nifty Total Market entirely, including roughly 171 on the SME platform and 41 in the suspended or Z group. Flags also stack: 62 companies carry two, 27 carry three and 4 carry four. ASM, GSM and ESM cover price and volume surveillance, with separate fundamental flags. Checkable per stock in the VIGIL app, updated daily, and downloadable via a free API.", "twitter_status": "posted" }, { "id": "sp500-excel-tracker-xlwings-yfin-aug2026", "slug": "sp500-excel-tracker-xlwings-yfin-aug2026", "title": "An Excel Tracker for Hundreds of Yahoo Finance Tickers, Built with xlwings Lite. For When WEBSERVICE Keeps Failing.", "date_published": "2026-08-07T16:10:00.000Z", "date_updated": null, "last_modified": "2026-08-07T16:10:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sp500-excel-tracker-xlwings-yfin-aug2026", "thumbnail": "/images/blog/sp500TrackerXlwings.png", "status": "published", "deck": "release", "tags": [ "excel", "xlwings-lite", "python-in-excel" ], "summary": "A downloadable Excel workbook that pulls dividend and earnings calendar data and prices for hundreds of Yahoo Finance tickers through the free Tigzig YFIN API, built for the case where a WEBSERVICE formula keeps failing because it is volatile and fires every cell at once. Built with xlwings Lite, which runs Python inside Excel as an add-in you install once. It uses the API's batching at up to 25 tickers per call with a configurable batch size, and each batch writes back to the sheet as it arrives with a spinner and a running status cell so the load is visible in progress. An important data caveat is documented: all dates are UTC calendar dates in YYYY-MM-DD with no time of day, because Yahoo timestamps each event to an exact instant and the UTC date of that instant is reported, so a US company reporting after roughly 8 PM Eastern crosses midnight UTC and lands on the next date, meaning readers should convert from UTC rather than assume the US trading date. Ships configured for S&P 500 tickers and takes about nine minutes for a full calendar pull and about two minutes for four trading days of prices, with the ticker list swappable and the Python source editable in place, so any AI assistant can modify it. Also points to the API error guide and notes the same pattern works against the India mutual fund APIs, including adding risk, return and benchmark calculations.", "twitter_status": "posted", "tweet_id": "2085762930898059525", "tweet_url": "https://x.com/amarHarolikar/status/2085762930898059525" }, { "id": "mfpro-range-requests-duckdb-polars-aug2026", "slug": "mfpro-range-requests-duckdb-polars-aug2026", "title": "Range Requests on the MFPRO NAV File Are Fixed. DuckDB and Polars Queries No Longer Get Refused Midway.", "date_published": "2026-08-07T06:00:00.000Z", "date_updated": null, "last_modified": "2026-08-07T06:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-range-requests-duckdb-polars-aug2026", "thumbnail": "/images/blog/mf_range_requests_onepager.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "duckdb", "infrastructure" ], "summary": "HTTP range requests against the free MFPRO India mutual fund NAV parquet now have their own pathway and budget, fixing queries from duckdb and polars that were being refused partway through. Cause: a single analytical query issues close to three hundred byte-range reads within seconds, and the rate limiter was counting those in the same bucket as whole-file downloads, so a legitimate query registered as a burst. The range pathway now allows 3,000 requests per minute and 25,000 per day per public IP, with remaining-budget details returned in response headers. The file remains about 175 MB covering close to 37 million NAV records and can still be downloaded whole, alongside single and batch NAV pulls, scheme search and the snapshot endpoint that returns the latest NAV for every scheme in one file. Includes measured tips for querying it remotely: duckdb and polars are fastest with polars marginally ahead, pyarrow and pandas-with-fsspec move roughly ten times more data for the same query, and pandas handed a bare URL is the case to avoid (372 MB and 37 seconds against 17 MB and 3.5 seconds when the URL is opened through fsspec first). Because the file is sorted by scheme_code, a WHERE on scheme_code costs about five requests while a date filter or unfiltered scan costs about three hundred, so the rule of thumb is to filter on scheme_code and query remotely and otherwise just download the file. Also covers two JavaScript options, hyparquet and the native duckdb npm package.", "twitter_status": "posted", "tweet_id": "2085762844285661657", "tweet_url": "https://x.com/amarHarolikar/status/2085762844285661657" }, { "id": "isin-checker-mutual-fund-identifiers-aug2026", "slug": "isin-checker-mutual-fund-identifiers-aug2026", "title": "An ISIN Checker for When a Mutual Fund Lookup Fails. And Why One Fund Carries Ten Identifiers.", "date_published": "2026-08-06T15:55:00.000Z", "date_updated": null, "last_modified": "2026-08-06T15:55:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/isin-checker-mutual-fund-identifiers-aug2026", "thumbnail": "/images/blog/isin_checker_onepager.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "converters-tools" ], "summary": "A free checker at isin.tigzig.com that separates a malformed ISIN from a valid one whose data is simply absent. It validates against the ISO 6166 check digit, explains what is wrong when validation fails, and where a character has been dropped will often suggest the intended ISIN. For Indian mutual funds it also returns the fund name, the AMFI scheme code and the reinvestment ISIN, accepts a pasted list for bulk checking, and resolves a fund name back to its ISIN. Explains the identifier sprawl behind most failed lookups, worked through Axis ELSS Tax Saver Fund: one fund exists as four variants (Direct and Regular plans, each with Growth and IDCW options), each with its own AMFI scheme code and NAV, and the two IDCW variants carry two ISINs each because payout and reinvestment are different instruments, giving four scheme codes and six ISINs, ten identifiers for what a reader would call one fund. Also covers three traps: IDCW schemes with no payout ISIN at all (Parag Parikh Flexi Cap Direct IDCW, scheme code 153964, where only the reinvestment identifier exists, so code reading the primary field alone reports a live fund as missing), SIF ISINs that are valid but absent from AMFI's mutual fund feed because SIF data is published separately, and scheme names shifting under the SEBI recategorisation while identifiers stay put.", "twitter_status": "posted", "tweet_id": "2085394238305652794", "tweet_url": "https://x.com/amarHarolikar/status/2085394238305652794" }, { "id": "yfin-excel-webservice-429s-aug2026", "slug": "yfin-excel-webservice-429s-aug2026", "title": "Getting 429s on the YFIN API from an Excel WEBSERVICE Formula? Here Is What Is Happening.", "date_published": "2026-08-06T16:00:00.000Z", "date_updated": null, "last_modified": "2026-08-06T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/yfin-excel-webservice-429s-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": null, "tags": [ "infrastructure", "excel" ], "summary": "Why Excel WEBSERVICE formulas hitting the free TigZig Yahoo Finance API return 429s, and what to do instead. WEBSERVICE is a volatile function, so it recalculates on any change anywhere in the workbook, meaning a few hundred such cells all fire simultaneously and breach the per-IP rate limit at once. Two mitigations: the endpoint accepts up to 25 tickers in a single call, turning a few hundred names into a few dozen calls, though hundreds of symbols will still take minutes because calls are paced server-side to manage concurrency and stay polite to Yahoo Finance. For anything at that scale the recommendation is Power Query with M, or Python in Excel via xlwings Lite, where requests run in sequence and refresh on demand rather than on every recalculation. Notes that any AI assistant can set up either option, points to the xlwings Lite hub for free guides and templates, and confirms the limits have been raised and tracker templates are in progress.", "twitter_status": "posted", "tweet_id": "2085394336750141866", "tweet_url": "https://x.com/amarHarolikar/status/2085394336750141866" }, { "id": "tigzig-technical-analysis-ask-your-ai-aug2026", "slug": "tigzig-technical-analysis-ask-your-ai-aug2026", "title": "Technical Analysis on Any Ticker Without Visiting My Site. Just Ask Your AI Agent.", "date_published": "2026-08-05T16:25:00.000Z", "date_updated": null, "last_modified": "2026-08-05T16:25:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-technical-analysis-ask-your-ai-aug2026", "thumbnail": "/images/blog/ta_ask_your_ai_onepager.png", "status": "published", "deck": "release", "tags": [ "technical-analysis", "mcp", "ai-coders" ], "summary": "The TigZig technical analysis API produces a formatted PDF and web report with charts, indicator readings and an outlook for any Yahoo Finance ticker, covering stocks, indices, FX, crypto and commodities, and it can be reached entirely through an AI agent without opening the site: paste a plain-English request naming the ticker and the tigzig API, and the agent discovers the API catalog, picks the right endpoint and reads the rate limits itself. Model choice is available on request. How it is built: prices from Yahoo Finance on daily and weekly timeframes, indicators computed with finta (EMAs, MACD, RSI, Bollinger Bands), two charts drawn with matplotlib, then both the numbers and the chart images passed to a vision model that reads the chart and writes the interpretation, with the returned markdown converted to PDF and HTML by a second ReportLab-based service. One FastAPI app serves the web front end, the REST endpoint and the MCP endpoint, so a browser and an agent execute the same code, and the whole thing is open source for anyone wanting to self-host or customise it. Closes with an explicit scope statement: this is a reporting tool rather than investment advice or research, models can misread a chart confidently, and the author's own practice is to use AI for the heavy lift and alternative angles while validating every deterministic output, the numbers and formulas, against more than one source.", "twitter_status": "posted", "tweet_id": "2085039599123108248", "tweet_url": "https://x.com/amarHarolikar/status/2085039599123108248" }, { "id": "sif-data-release-nav-scheme-master-aum-aug2026", "slug": "sif-data-release-nav-scheme-master-aum-aug2026", "title": "SIF Data Is Live. The Full Published NAV History, the Scheme Master and the Quarterly AUM.", "date_published": "2026-08-05T12:55:00.000Z", "date_updated": null, "last_modified": "2026-08-05T12:55:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sif-data-release-nav-scheme-master-aum-aug2026", "thumbnail": "/images/blog/sif_data_release_onepager.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "infrastructure" ], "summary": "A free, keyless dataset of every NAV AMFI has published for Indian Specialized Investment Funds since the category began, live at sif.tigzig.com as plain CSV updated twice a day, alongside the scheme master and quarterly average AUM. Six files: sif_nav.csv (about 10,000 rows over 200+ trading dates), sif_meta.csv, sif_aaum.csv, summary.json, manifest.json with row counts, byte sizes and SHA-256 per file, and a README covering every column. Explicitly observational, part of pipeline beta testing, with no uptime commitment and a file shape that may still change. Explains why fund counts and scheme-variant counts differ: AMFI counts 27 funds as at 30 June 2026 across 75,032 folios and Rs 17,858 crore net AUM, while the files hold 111 scheme variants from 17 fund houses, 63 open ended and 48 interval, with the first NAV on 8 October 2025. Documents the traps for anyone building on it: the temp_ column prefix that disappears on merge, upload_ts being IST despite a trailing Z, isin and isin2 being one scheme, and column names deliberately matching the mutual fund dataset. Sets out the open design questions before SIF joins MFPRO, including a different SEBI strategy taxonomy, long-only benchmarks that misprice long-short funds, interval-fund liquidity semantics, and a three-option comparison for reconciling AMFI's string SIF codes with integer mutual fund scheme codes. Closes with the validation regime: dual-source comparison, five-day lookback for late filings and restatements, holiday versus gap detection, byte-level reparse against archived originals, AUM reconciliation, a strategy-by-strategy roll-up matching AMFI's monthly report, and the single inferred field with its NAV-ordering test.", "twitter_status": "posted", "tweet_id": "2084998716117975308", "tweet_url": "https://x.com/amarHarolikar/status/2084998716117975308" }, { "id": "quantstats-four-routes-qrep-agent-aug2026", "slug": "quantstats-four-routes-qrep-agent-aug2026", "title": "Comparing Securities on Returns, Risk and Drawdowns. Four Routes into QuantStats, Depending on What Your AI Can Do.", "date_published": "2026-08-04T13:20:00.000Z", "date_updated": "2026-08-04T14:05:00.000Z", "last_modified": "2026-08-04T14:05:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/quantstats-four-routes-qrep-agent-aug2026", "thumbnail": "/images/blog/qrep_mag7_routes_onepager.png", "status": "published", "deck": "release", "tags": [ "portfolio-analytics", "mcp", "ai-coders" ], "summary": "Four ways to get portfolio and security analytics out of QuantStats, the open-source library by Ran Aroussi who also wrote yfinance, with the right route depending on what your AI can actually do. An AI chat such as ChatGPT cannot install Python, so it connects through an MCP server, and the open-source QREP MCP server is one option. An AI agent such as Claude Cowork, Claude in Excel or PowerPoint, or ChatGPT Work, installs the package itself, runs the numbers and formats the output or builds a dashboard, and can equally call the QREP API directly for a fixed set of 81 metrics with no setup, or connect over MCP. For interactive work on screen, the QREP browser tool runs the cuts and generates a QuantStats-format tear sheet. Tool and API are both QuantStats-powered and both open source, with a few QREP implementation choices that shift a handful of numbers slightly, documented in the methodology page. Also notes that an agent with Python can be asked to write the metric functions from scratch, which is useful for validation and for metrics such as rolling returns. Adds a clarification on what a chat app can actually run in its Python sandbox, where ChatGPT is more restricted than Claude.ai and the desktop cowork apps are more capable again, with the practical advice being to ask the assistant what it can do and test it. Includes copy-paste agent prompts for both the run-it-yourself and the API route, using the Mag 7 against the S&P 500 over five years, plus practical notes for the browser tool: ticker search rather than typing, CSV export of the metrics table and raw prices, adjustable omega threshold and var/cvar confidence and tail cutoff, and browser-computed EMA, RSI and MACD technicals with the formulas in the docs.", "twitter_status": "posted", "tweet_id": "2084630357740302797", "tweet_url": "https://x.com/amarHarolikar/status/2084630357740302797" }, { "id": "tigzig-api-zero-rate-limited-aug2026", "slug": "tigzig-api-zero-rate-limited-aug2026", "title": "Zero Callers Rate Limited in 24 Hours. 6,500+ Calls and 9.7 GB Served, After the TigZig API Limits Went Up.", "date_published": "2026-08-03T11:40:00.000Z", "date_updated": null, "last_modified": "2026-08-03T11:40:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-api-zero-rate-limited-aug2026", "thumbnail": "/images/blog/tigzig_zero_ratelimited_onepager.png", "status": "published", "deck": "release", "tags": [ "infrastructure", "mutual-funds" ], "summary": "Result of the rate-limit and concurrency rework on the free, keyless TigZig public APIs: zero callers refused in a 24-hour window across 6,500+ calls and 9.7 GB served, against a prior peak where roughly one in four calls was being refused because old limit and concurrency settings had never been updated. Fixed across the whole path, edge, proxy and app. Also breaks down how the three busiest API surfaces are actually used, which differs sharply between them. The mutual fund API (MFPRO) leads on call volume and splits two ways, callers pulling NAVs scheme by scheme or in batches, and callers taking the entire database as one file alongside the scheme master, which is regenerated three times a day from AMFI data, with format demand split roughly evenly between parquet and CSV; the NAV path traffic looks like scheduled scripts maintaining trackers. The Yahoo Finance API (YFIN) is second by volume with get-all-prices the most used path, and is a thin open-source wrapper over the yfinance package. Tremor is almost entirely bulk, callers taking the full macro database of a few hundred global and India indicators. Downloads carry a tighter limit than queries because they are heavy, and that limit is being revised upwards as range requests appear. Includes a copy-paste agent prompt for wiring up a tracker end to end, and the free no-login front ends: MFPRO, the Quants Report Agent and QREP.", "twitter_status": "posted", "tweet_id": "2084243009236078693", "tweet_url": "https://x.com/amarHarolikar/status/2084243009236078693" }, { "id": "mfpro-sif-analytics-where-it-stands-aug2026", "slug": "mfpro-sif-analytics-where-it-stands-aug2026", "title": "Is SIF Analytics Coming to MFPRO? On the List, No Date.", "date_published": "2026-08-02T17:10:00.000Z", "date_updated": null, "last_modified": "2026-08-02T17:10:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-sif-analytics-where-it-stands-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": null, "tags": [ "mutual-funds" ], "summary": "Where SIF analytics stands on the MFPRO roadmap: planned, with no date. SIF is SEBI's Specialised Investment Fund category, about a year old and growing, but AMFI publishes SIF data on a separate portal and the daily AMFI NAV feed that MFPRO syncs three times a day carries no SIF schemes at all, so none are present in the mutual fund dataset or API today. A complicating detail for anyone matching identifiers: SIF ISINs are formatted exactly like mutual fund ISINs, INF prefix included, because SIF units sit under the AMC's mutual fund umbrella at the depository, so the identifier format alone does not distinguish the two. Same identifier shape, different regulatory category, different source, which means a separate ingestion pipeline with its own validations rather than an extension of the existing one. The stated reason for holding off is sequencing rather than difficulty: MFPRO has only been live a few months, app and API usage has risen sharply in recent weeks, and the preference is to let that settle and learn from it before adding a new data domain.", "twitter_status": "posted", "tweet_id": "2083962835550560737", "tweet_url": "https://x.com/amarHarolikar/status/2083962835550560737" }, { "id": "claude-cowork-python-sandbox-mf-data-aug2026", "slug": "claude-cowork-python-sandbox-mf-data-aug2026", "title": "Claude Cowork Runs a Real Python Sandbox. I Put 37 Million Rows of Mutual Fund NAV Data Through It.", "date_published": "2026-08-02T16:15:00.000Z", "date_updated": null, "last_modified": "2026-08-02T16:15:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-cowork-python-sandbox-mf-data-aug2026", "thumbnail": "/images/blog/cowork_python_sandbox_onepager.png", "status": "published", "deck": "analysis", "tags": [ "ai-coders", "mutual-funds", "duckdb" ], "summary": "What Claude Cowork's Python sandbox can actually do for analytics work, tested on a real dataset. It ships with pandas, numpy, scipy, duckdb, matplotlib and yfinance, installs scikit-learn and statsmodels in under a minute, and takes polars and pyarrow too, though packages needing compilation can time out on install (xgboost did). An approximate benchmark run inside the sandbox reports two cores of an AMD Ryzen 7 7730U, about 4 GB of memory and 10 GB of SSD-class disk. The worked case: downloading the free India mutual fund NAV dataset published on tigzig.com, a 175 MB parquet of 37 million-plus rows going back to April 2006 plus the scheme master, and running category-level risk and return analytics with results back in under a minute per iteration. The same Python sandbox is available in Claude for Excel and PowerPoint. Includes a copy-paste prompt for pointing any agent at the same live datasets, with the four things that trip people up on this data: matured and merged schemes are retained so it is survivorship-bias free, a row is a plan-and-option variant rather than a fund (about 8,500 codes publishing daily, roughly 1,950 funds as AMFI counts them), and scheme names and categories shift under the SEBI recategorisation so joins should key on scheme code or ISIN. Closes with why DuckDB and Parquet are the working default: the same NAV data is 3.8 GB as plain CSV against about 175 MB as parquet, columnar storage means a query reads only the columns it touches, and DuckDB's statistical functions run in SQL without dropping into pandas.", "twitter_status": "posted", "tweet_id": "2083949890972107206", "tweet_url": "https://x.com/amarHarolikar/status/2083949890972107206" }, { "id": "tigzig-api-rate-limits-raised-aug2026", "slug": "tigzig-api-rate-limits-raised-aug2026", "title": "TigZig API Rate Limits Raised Substantially, Across the Edge, the Proxy and the Apps. And a 429 Now Tells You What to Do About It.", "date_published": "2026-08-02T08:45:00.000Z", "date_updated": null, "last_modified": "2026-08-02T08:45:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-api-rate-limits-raised-aug2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": "release", "tags": [ "infrastructure", "mutual-funds" ], "summary": "Rate limits on the free, keyless TigZig APIs have been raised substantially across every layer of the path, the Cloudflare edge, the proxy in front of the apps and the apps themselves, with concurrency limits raised alongside. The old numbers were set when traffic was a fraction of current volume and had accumulated across layers added at different times, so ordinary use, particularly Excel and Google Sheets trackers and automated scripts firing one call per row, was being refused. The 429 response is now actionable: every refusal carries Retry-After, CORS headers go out on it so a browser fetch can read the status rather than throwing a generic network error, and where a bulk download exists the response body names the file that would replace the loop. A Cloudflare-edge refusal also returns X-RateLimit-Limit, Remaining, Reset and Domain, with Reset approximate because the window rolls per edge location, while an app-side refusal carries Retry-After alone. Per-API limits are published in the human docs and the machine-readable catalogs. Also covers the batch endpoints and the full-database downloads available for the mutual fund, Vigil and Tremor APIs as the right answer for heavy use, Python in Excel via xlwings Lite for spreadsheet automation, and an explanation of what per-IP counting means behind carrier-grade NAT or a corporate gateway, along with why these APIs stay keyless by deliberate choice.", "twitter_status": "posted", "tweet_id": "2083836774598803748", "tweet_url": "https://x.com/amarHarolikar/status/2083836774598803748" }, { "id": "tigzig-agent-first-ask-your-ai-aug2026", "slug": "tigzig-agent-first-ask-your-ai-aug2026", "title": "Tigzig is Built AI-Agent First. 300+ Assets, and the Fastest Way In Is to Ask Your AI.", "date_published": "2026-08-01T15:00:00.000Z", "date_updated": null, "last_modified": "2026-08-01T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-agent-first-ask-your-ai-aug2026", "thumbnail": "/images/blog/agent_first_onepager_aug2026.png", "status": "published", "deck": "release", "tags": [ "ai-coders", "infrastructure" ], "summary": "Tigzig is built AI-agent first, so the quickest route into the site is to ask an assistant rather than browse it. All 300+ assets, guides, live apps, repos, open APIs and MCP servers, are organised to be machine readable and agent accessible, with worked example prompts for ChatGPT, Claude, Gemini or Meta AI such as asking what the site has for building an Excel tool, whether it carries analysis on Donald Trump's trades, or to pull the author's posts on economy and markets. An AI coder (Claude Cowork, ChatGPT Work, Claude Code, Codex) can go further and fetch the code, call the APIs and deploy and run something for you. Also covers two changes in traffic: a fair portion of API calls now arrive from agents and automated tools, so some callers are meeting rate limits they would not have hit before, with limits under active adjustment and the limits themselves being documented across response headers, 429 bodies, the machine-readable API roots and the human docs, starting with the MFPRO API and extending to other high-usage APIs including the Yahoo Finance and FFN portfolio tools. Suggested workarounds are batching identifiers into a single call where an API supports multiple tickers or scheme codes, or taking the whole database as a file for TREMOR and the mutual fund API. Ends on where the traffic now comes from: ChatGPT is the number one referrer after Google, and API callers arriving as ChatGPT and Claude send standard headers and behave in a predictable, systematic way.", "twitter_status": "posted", "tweet_id": "2083569922534432904", "tweet_url": "https://x.com/amarHarolikar/status/2083569922534432904" }, { "id": "mfpro-scheme-master-download-aug2026", "slug": "mfpro-scheme-master-download-aug2026", "title": "MFPRO Scheme Master: Every India Mutual Fund Scheme in One Small File, Free", "date_published": "2026-08-01T09:30:00.000Z", "date_updated": null, "last_modified": "2026-08-01T09:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-scheme-master-download-aug2026", "thumbnail": "/images/blog/mfpro_scheme_master_onepager.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "portfolio-analytics" ], "summary": "A free, keyless one-row-per-scheme master file covering every India mutual fund scheme in AMFI's public records from April 2006 onward, about 38,000 rows and 22 columns at roughly 9 MB as CSV, 1.1 MB gzipped and 1.6 MB as parquet, downloadable in one click from the MFPRO app or over HTTP at /mf/v1/download?format=latest with pandas reading straight off the URL. Columns cover identity (scheme_code, isin, isin2, scheme_name, amc, txic_code), classification (scheme_type, raw category, category_sub, category_group and the four-value category_group_clean), how it is sold (scheme_plan, scheme_option), lifespan (first_date, last_date, is_active, is_stale) and size and price (aaum_cr_quarterly_avg with its quarter label and ISO quarter-end, nav_date, nav). About 8,600 schemes are live and roughly 29,000 matured or merged are kept in deliberately, so long-run studies can be run survivorship-bias free. Includes a worked pandas filter that shortlists 39 large long-track-record Direct Growth equity schemes across three fund houses, guidance on moving from scheme codes to NAVs (single call, 50-code batch, or the 37-million-row full history file), and a join recipe pairing the master with the full NAV history to hold the whole universe locally. Documents six data quirks that produce plausible wrong answers rather than errors: the NAV column being each scheme's last NAV rather than today's, one scheme code being a plan-and-option variant rather than a fund, meaningful blanks in category_sub, aaum_cr_quarterly_avg and isin, sorting quarters on the ISO column rather than the text label, AAUM being a quarterly average rather than a balance, and about 2,300 matured schemes carrying a closing NAV of zero.", "twitter_status": "posted", "tweet_id": "2083493424632893944", "tweet_url": "https://x.com/amarHarolikar/status/2083493424632893944" }, { "id": "mfpro-search-filters-12-fields-jul2026", "slug": "mfpro-search-filters-12-fields-jul2026", "title": "MFPRO Mutual Fund API: Search Now Filters Across 12 Fields on a Scheme", "date_published": "2026-07-31T05:20:00.000Z", "date_updated": null, "last_modified": "2026-07-31T05:20:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-search-filters-12-fields-jul2026", "thumbnail": "/images/blog/mfpro_search_filters_onepager.png", "status": "published", "deck": "release", "tags": [ "mutual-funds", "infrastructure" ], "summary": "Full reference for the /search endpoint on the free, keyless MFPRO India mutual fund NAV API, which now filters on 12 scheme fields rather than matching a fund name alone: amc, category, group, type, plan, option, active, stale, the first_date and last_date windows, isin and txic, with exclude on top as the single NOT. Any filter present turns search into browse, so a call with filters and no q lists the scheme universe; only a completely bare call still returns 422. Covers the difference between group (four broad buckets after the SEBI Feb 2026 change folded Solution Oriented and Fund of Funds into Other Scheme) and category (the SEBI sub-category, contains-matched against AMFI's raw text too, which is why category=income with active=all surfaces tens of thousands of pre-2018 schemes that carry no clean sub-category). Documents the whole query language: filters AND automatically, a comma is OR within one field, exclude subtracts last, and there is nothing past that - no parentheses, no nesting, no OR across fields, and AND/OR typed into q are matched as literal words. Also the error behaviour (an unknown parameter rejects the whole call with valid_params and did_you_mean rather than silently dropping a filter, repeated q is rejected while list filters merge, limit is bounds-checked 1 to 100 with a pointer to the bulk download), worked examples with real counts, the exclude-matches-name-only and no-sort limits, offset paging, and the pagination-across-a-sync edge with the bulk file as the consistent alternative.", "twitter_status": "posted", "tweet_id": "2083070584364425382", "tweet_url": "https://x.com/amarHarolikar/status/2083070584364425382" }, { "id": "mfpro-scheme-explorer-sebi-categories-jul2026", "slug": "mfpro-scheme-explorer-sebi-categories-jul2026", "title": "MFPRO Scheme Explorer is Live. Cross-Tab 38,000+ India Mutual Fund Schemes, and Watch the SEBI Re-Categorisation Happen in the Daily Files.", "date_published": "2026-07-30T17:05:00.000Z", "date_updated": null, "last_modified": "2026-07-30T17:05:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-scheme-explorer-sebi-categories-jul2026", "thumbnail": "/images/blog/mfpro_explorer_onepager.png", "status": "published", "deck": "release", "tags": [ "mutual-funds" ], "summary": "An interactive Scheme Explorer on MFPRO that cross-tabs the whole India mutual fund universe of 38,000+ AMFI schemes, pivoting scheme counts by clean category, sub-category, AMC, plan and option with filters, under the new SEBI categories and names. Because MFPRO now tracks the latest AMFI filing rather than the first-filed name, the February 2026 SEBI re-categorisation can be watched moving through the daily NAV files: 367 schemes changed category or name in a single day, including 91 name changes as fund houses rename to the true-to-label rule. Worked examples of the renames: Groww Dynamic Bond Fund to Groww Dynamic Term Fund, ITI Ultra Short Duration Fund to ITI Ultra Short Term Fund, ICICI Prudential Equity Arbitrage to ICICI Prudential Arbitrage Fund, Canara Robeco Consumer Trends and HDFC Non-Cyclical Consumer both to Consumption Fund, ICICI Prudential Thematic Advantage Fund (FOF) to ICICI Prudential Aggressive Hybrid Active FOF, Quantum Gold Savings Fund to Quantum Gold ETF FOF, Motilal Oswal Asset Allocation FOF to Motilal Oswal Multi Asset Passive Fund of Fund, and UTI Liquid Cash Plan to UTI Liquid Fund, with the new FoF naming now signalling active versus passive underlyings. The first two Life Cycle Funds are live (Zerodha Life Cycle Fund 2036 and 2041). Solution Oriented schemes are discontinued, so 143 active children's and retirement schemes stop taking fresh subscriptions and will be merged. Of the 38,000+ schemes only about 8,600 are active, roughly three in four having matured or merged, mostly fixed maturity plans from 2006 to 2013. Also documents a transient AMFI filing error (Aditya Birla Sun Life Gold ETF filed under Equity ETF) published as filed rather than silently corrected.", "twitter_status": "posted", "tweet_id": "2082875340955975865", "tweet_url": "https://x.com/amarHarolikar/status/2082875340955975865" }, { "id": "sebi-mf-categorisation-changes-jul2026", "slug": "sebi-mf-categorisation-changes-jul2026", "title": "SEBI Has Revised Mutual Fund Categorisation and Introduced a New Fund Type. The Major Changes, and What They Mean for MFPRO Users and Analysts.", "date_published": "2026-07-30T13:15:00.000Z", "date_updated": "2026-07-31T05:25:00.000Z", "last_modified": "2026-07-31T05:25:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sebi-mf-categorisation-changes-jul2026", "thumbnail": "/images/blog/mfpro_main_screen.png", "status": "published", "deck": "release", "tags": [ "mutual-funds" ], "summary": "SEBI's 26 February 2026 circular on categorization and rationalization of mutual fund schemes, the major changes, and what they change for users of the MFPRO tool and the free MF NAV API. Circular changes covered: a new Life Cycle Funds category (target date funds such as Life Cycle Fund 2041, equity heavy at the start and shifting toward debt as the maturity year approaches, with the first two already launched by Zerodha); Solution Oriented schemes discontinued, with children's and retirement funds stopping new subscriptions and being merged; Sectoral and Thematic split into two categories with portfolio overlap limits; most debt categories renamed across the duration ladder (Short Duration to Short Term, Dynamic Bond to Dynamic Term Fund); and scheme names required to match the category, with return-indicating words no longer allowed. Existing schemes have six months to comply. The transition is already visible in AMFI's daily NAV files, with new filings carrying the new category headers from 17 July, so old and new category and sub-category names will coexist in the data for a while and can break analysis pipelines. On the MFPRO side: the top level view moved to four buckets (Equity, Debt, Hybrid, Other) with life cycle funds inside Other, Solution Oriented and Fund of Funds folded into Other as well, the raw AMFI category text stays on every scheme for filtering, search is available on both tool and API, and scheme names switch from first-filed to the current AMFI filing ahead of the rename wave. Includes a field-level note on category, category_group, sub-category and the recommended category_group_clean.", "twitter_status": "posted", "tweet_id": "2082818030992797815", "tweet_url": "https://x.com/amarHarolikar/status/2082818030992797815" }, { "id": "mfpro-api-update-three-things-jul2026", "slug": "mfpro-api-update-three-things-jul2026", "title": "MFPRO Mutual Fund API Update. Three Things Your Calls Asked For, Now Built.", "date_published": "2026-07-29T10:30:00.000Z", "date_updated": null, "last_modified": "2026-07-29T10:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-api-update-three-things-jul2026", "thumbnail": "/images/blog/api_conversation_update_onepager.png", "status": "published", "deck": "release", "tags": [ "infrastructure", "mutual-funds" ], "summary": "Three changes to the free, keyless MFPRO India mutual fund NAV API, each one built from a pattern seen repeatedly in the server logs, with the before and after for each. First, latest=true is now a real parameter: callers had been guessing it for weeks and silently receiving a fund's entire history (5,003 rows back to 2006) to show one number; it now returns one row, works on single and batch calls, combines with since/to for as-of-date queries, and accepts LATEST=TRUE, latest=1 and latest=false. Second, every response now carries per-scheme first_available_date and latest_available_date plus a Last-Modified header on empty windows, so a 200 with an empty data array explains itself and a poller running ahead of AMFI's daily publish can self-correct instead of writing empty files. Third, a 404 on an ISIN now runs the check-digit arithmetic and says in the error body when the identifier itself is corrupted, distinguishing that from an identifier that is well formed but simply absent, so an agent reading the error knows whether retrying can ever help. Also recaps the forgiving-input handling: scheme/schemes/isin/isins interchangeable, up to 50 identifiers per call, case-insensitive parameter names and values, repeated parameters merging, and one bad identifier never failing a batch.", "twitter_status": "posted", "tweet_id": "2082441812699119879", "tweet_url": "https://x.com/amarHarolikar/status/2082441812699119879" }, { "id": "api-conversation-agents-scripts-jul2026", "slug": "api-conversation-agents-scripts-jul2026", "title": "Your Agents and Scripts Have Been Talking to Me. Here Is That Conversation in Plain English.", "date_published": "2026-07-28T15:00:00.000Z", "date_updated": "2026-07-28T18:00:00.000Z", "last_modified": "2026-07-28T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/api-conversation-agents-scripts-jul2026", "thumbnail": "/images/blog/api_conversation_onepager.png", "status": "published", "deck": null, "tags": [ "infrastructure", "mcp", "mutual-funds" ], "summary": "Server logs from the free, keyless tigzig data APIs rewritten as a plain-English conversation, including who is actually calling (roughly 1 in 5 mutual fund API calls arrive with Claude or ChatGPT user agents from Cowork, Excel and Codex, rising to about half on Tremor and Yahoo Finance where MCP is mounted, with the rest scripts and AI coders behind generic or blank user agents): 23 real questions the incoming calls asked, and the patterns that got it right. Questions covered include SIF schemes (not carried, and why a q=SIF search returns 30 false matches from 'diversified'), where to get index levels rather than index funds (Tremor's 20 market series with tickers and history start dates, or the Yahoo Finance wrapper), batching up to 50 identifiers in one call with scheme/schemes/isin/isins interchangeable, what a 429 actually means and how Retry-After and X-RateLimit-Remaining work, trailing punctuation and & entities in copied URLs (the silent one that returns full history instead of your date slice), why an MCP endpoint 406s in a browser, the three causes of a 404 on a well-formed identifier, ISIN check digits and invalid ISINs circulating through AI output, empty-200 vs 404, retrying large downloads and verifying against Content-Length, plus the cricket-database GET-vs-POST fix and its Cricsheet id space. The things callers got right: whole-file parquet downloads, format choice, ChatGPT-User fetching files into its own sandbox, batch calls, search-then-fetch, since= incrementals, DuckDB HTTP range reads pulling 9MB instead of 170MB, ETag/If-None-Match and If-Modified-Since checks, and agents reading the API catalog before making a single data call. Ends with the human and agent documentation sets and how to report a problem.", "twitter_status": "posted", "tweet_id": "2082106741853139036", "tweet_url": "https://x.com/amarHarolikar/status/2082106741853139036" }, { "id": "mfpro-download-truncation-fix-jul2026", "slug": "mfpro-download-truncation-fix-jul2026", "title": "1 in 5 Bulk Downloads Were Silently Truncating. Two Cloudflare Worker Bugs, Found and Fixed. Now Up to 4x Faster.", "date_published": "2026-07-27T11:00:00.000Z", "date_updated": null, "last_modified": "2026-07-27T11:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-download-truncation-fix-jul2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": "release", "tags": [ "infrastructure", "mutual-funds" ], "summary": "Roughly 1 in 5 bulk downloads of the MFPRO India mutual fund database were silently truncating - files started fine, ran a while, then arrived incomplete, with no error shown to the user and slower connections hit hardest. Edge-level monitoring surfaced 504s that origin server logs structurally cannot see. Two separate Cloudflare Worker bugs in serving large files from R2: teeing the user's download stream into the edge cache write (the cache fill finishes first and cuts the client off mid-file), and passing a large file through JavaScript inside the Worker (the CPU limit truncates it silently). Both fixed and verified byte by byte from four countries; downloads now complete and up to 4x faster. Includes the transferable lessons for anyone serving large files at the edge, what is in the download (37,084,484 rows across 38,000+ schemes since 2006, seven formats from 167MB Parquet to 1.28GB SQLite, regenerated 2-3x daily with a manifest carrying sizes, row count and a validation flag), the one-GET no-key endpoint, the DuckDB-over-HTTP alternative, and the wider no-key API and MCP surface (Tremor macro, Vigil India red flags, Yahoo Finance, comparisons, technical analysis, markdown to PDF) with live uptime at status.tigzig.com.", "twitter_status": "posted", "tweet_id": "2081749113406968246", "tweet_url": "https://x.com/amarHarolikar/status/2081749113406968246" }, { "id": "sec-financials-three-ways-xbrl-edgartools-jul2026", "slug": "sec-financials-three-ways-xbrl-edgartools-jul2026", "title": "Pulling Company Financials Straight From SEC Filings. Free, No Vendor, No API Key. Three Routes Tested Head to Head.", "date_published": "2026-07-26T16:00:00.000Z", "date_updated": "2026-07-29T11:00:00.000Z", "last_modified": "2026-07-29T11:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sec-financials-three-ways-xbrl-edgartools-jul2026", "thumbnail": "/images/blog/xbrl_onepager_analysis.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants", "ai-coders" ], "summary": "Three routes to company financials from SEC filings, tested head to head in a Claude Cowork session on the same seven companies and six quarters: the SEC XBRL API directly, the edgartools Python library, and yfinance. All three agreed on the numbers; they differ on coverage and traceability. Verdict: edgartools first - it ships a statement API that pulls a full statement in one shot with indent level and parent, a customizable tag-standardization layer, de-cumulation, EDGAR full-text search, 13F institutional holdings, TTM calculations and segment facts, plus Claude skills and an MCP server. The raw SEC API stays for raw SEC API for cross-sectional screening of thousands of filers and as an independent second path - a check that earned its keep when the one disagreeing figure turned out to be a mishandled restatement. Links to a full 17-section write-up covering XBRL concepts/tags/facts, the four API routes as slices of a cube, frames coverage, taxonomies, saving filings, R-pages, standardisation, duration vs instant, the yfinance reconciliation, edgartools capabilities and its shipped Claude skills, and limits - with every Python script, JSON dataset and source filing published alongside so any number can be traced to the code that produced it. edgartools is by Dwight Gunning, MIT licensed, 2,000+ stars, ships Claude skills and an MCP server.", "twitter_status": "posted", "tweet_id": "2081382324676296871", "tweet_url": "https://x.com/amarHarolikar/status/2081382324676296871" }, { "id": "yahoo-finance-mcp-backend-upgrade-jul2026", "slug": "yahoo-finance-mcp-backend-upgrade-jul2026", "title": "Yahoo Finance MCP Backend Upgraded. 25 Tickers in One Request, Answers in Seconds, Clear Errors Instead of Slow Failures.", "date_published": "2026-07-23T16:00:00.000Z", "date_updated": null, "last_modified": "2026-07-23T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/yahoo-finance-mcp-backend-upgrade-jul2026", "thumbnail": "/images/og-image.png", "status": "published", "deck": "release", "tags": [ "mcp" ], "summary": "Short release note: the TigZig Yahoo Finance MCP backend has been upgraded - batch requests now take up to 25 tickers in one call, responses return in seconds instead of stalling, and a bad or slow ticker returns a short note while the rest of the batch still comes through, a clear message instead of a slow failure. Applies to any MCP client (Claude, ChatGPT, LM Studio). Errors on any TigZig app surface on the author's monitoring and get root-caused. Links: Yahoo Finance MCP docs page, api.tigzig.com agent entry point, the API-errors practical fix guide, and the Streamable HTTP migration note for anyone still on an older SSE URL.", "twitter_status": "posted", "tweet_id": "2080342365085323661", "tweet_url": "https://x.com/amarHarolikar/status/2080342365085323661" }, { "id": "tigzig-mcp-streamable-http-migration-jul2026", "slug": "tigzig-mcp-streamable-http-migration-jul2026", "title": "Every Tigzig MCP is now Streamable HTTP. Requesting existing users from LM Studio, ChatGPT, Claude etc to make a one time change if you have not already done so", "date_published": "2026-07-23T10:00:00.000Z", "date_updated": "2026-07-23T14:00:00.000Z", "last_modified": "2026-07-23T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-mcp-streamable-http-migration-jul2026", "thumbnail": "/images/blog/mcp_streamable_onepager.png", "status": "published", "deck": "release", "tags": [ "mcp" ], "summary": "Migration note: all seven Tigzig MCP servers now run Streamable HTTP, the current MCP transport standard used by Claude, ChatGPT and LM Studio - every server lives at its host plus /mcp. Existing SSE users are asked to make a one-time connection change; SSE stays mounted for now but is being deprecated, and old addresses keep working where possible. Includes a plain-words explainer on connection types (remote URL vs local command; pick Streamable HTTP or 'HTTP' in your client), the full server list with URLs (Yahoo Finance, Technical Analysis, Portfolio Review ffn, QRep Compare Securities, Tigzig Data Surface, Cricket Database, Markdown to PDF), why a browser shows 406 on /mcp addresses, the skip-the-MCP alternatives (call the open APIs directly or wrap your own server - no keys, per-IP rate limits on each docs page), and the agent path: point an AI at api.tigzig.com for the machine-readable catalog of every API and MCP. Single-image release post with the migration one-pager PNG at the bottom.", "twitter_status": "posted", "tweet_id": "2080239882207957156", "tweet_url": "https://x.com/amarHarolikar/status/2080239882207957156" }, { "id": "jamie-dimon-six-warnings-jul2026", "slug": "jamie-dimon-six-warnings-jul2026", "title": "Jamie Dimon's Six Warnings. Record JPMorgan Profits, but a Very Different Macro Outlook From the Chairman Himself.", "date_published": "2026-07-22T15:00:00.000Z", "date_updated": null, "last_modified": "2026-07-22T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/jamie-dimon-six-warnings-jul2026", "thumbnail": "/files/DIMON_STORY_JUL2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Jamie Dimon's hour-long Master Investor Podcast interview (Wilfred Frost, July 2026) distilled: JPMorgan posts its highest-ever quarterly profit on a trading rebound while the chairman's own macro outlook runs the other way. Six warnings: early AI-boom entrants may not be the winners (dot-com parallel), the real AI challenge is retooling training systems rather than job-loss panic, he would not buy long government bonds or broad indices at current prices, he distrusts official monthly inflation prints, geopolitical risk from conflicts and remilitarization is larger than markets price, and 100% debt-to-GDP with 6% deficits outside wartime will become a problem via rates and bond-market pressure. 8-slide summary deck (browsable carousel + PDF download) and the full best-effort cleaned interview transcript below the deck, with the YouTube source linked and caption-quality disclaimer stated.", "twitter_status": "posted", "tweet_id": "2079924539161825524", "tweet_url": "https://x.com/amarHarolikar/status/2079924539161825524" }, { "id": "pushover-server-down-alerts-jul2026", "slug": "pushover-server-down-alerts-jul2026", "title": "The boring $5 tool that tells me when a server goes down", "date_published": "2026-07-22T13:00:00.000Z", "date_updated": "2026-07-22T17:00:00.000Z", "last_modified": "2026-07-22T17:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/pushover-server-down-alerts-jul2026", "thumbnail": "/images/logos/pushover.png", "status": "published", "deck": null, "tags": [ "infrastructure" ], "summary": "Long-form guide to Pushover, the one-time-$5 hosted notification service that pages your phone when a server dies - as used across the tigzig.com fleet (50+ apps, two servers). Covers the whole model in plain words: your cron or error handler makes one outbound HTTPS POST, Pushover's cloud owns all delivery machinery, notification lands on your phone. Includes the 10-minute setup, complete curl and JavaScript snippets, the -2 to +2 priority dial (silent to keeps-ringing-until-acknowledged emergency with retry/expire), a real production alert taxonomy (CPU alarms, 4xx velocity auto-jail, API liveness, 5xx spikes, TLS cert expiry, login anomalies, recovery pings), the fire-once-and-recover dedup pattern with an active_alerts table, why a hosted notifier beats self-hosting for shared-fate reasons (with ntfy and Gotify as the self-host alternatives), the keep-secrets-out-of-notifications rule, and the full capability ceiling (teams, receipts, webhooks, Glances, subscriptions). Also: any AI coding agent can wire it for you - it knows the API cold.", "twitter_status": "posted", "tweet_id": "2079920235340279975", "tweet_url": "https://x.com/amarHarolikar/status/2079920235340279975" }, { "id": "mfpro-downloads-full-database-jul2026", "slug": "mfpro-downloads-full-database-jul2026", "title": "227 Downloads, 41GB in 10 Days. India MF Complete NAV Database - 38K+ Schemes, 37M+ Records, One 167MB Parquet. Free.", "date_published": "2026-07-21T10:00:00.000Z", "date_updated": null, "last_modified": "2026-07-21T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-downloads-full-database-jul2026", "thumbnail": "/images/blog/mfpro_downloads_onepager.png", "status": "published", "deck": "release", "tags": [ "mutual-funds" ], "summary": "Usage note on the MFPRO full-database download: 227 downloads totalling 41GB served in 10 days from Cloudflare's global edge (R2) - the complete India Mutual Fund NAV database as a single 167MB Parquet file covering 38K+ scheme codes and 37M+ records since 2006, including closed and defunct schemes, enabling regime-based analysis across the 2008 GFC and later shock events. Free and open, refreshed three times daily, downloadable manually from the browser (MFPRO Data & API screen) or via API - or point an AI agent at api.tigzig.com. Update process runs multi-step validation, with a data dictionary, build-process doc, scheme-type field guide and documented data anomalies in the Docs section. Also covers the scheme universe (~8,500 codes publishing daily, roughly 1,950 funds as AMFI counts them; 22% of all-time codes active) with an interactive cross-tab Explorer, and API uptime at 99.7% over the past ~15 days with a live status page at status.tigzig.com. Single-image release post with the downloads one-pager PNG at the bottom.", "twitter_status": "posted", "tweet_id": "2079591693490565534", "tweet_url": "https://x.com/amarHarolikar/status/2079591693490565534" }, { "id": "tigzig-api-errors-practical-guide-jul2026", "slug": "tigzig-api-errors-practical-guide-jul2026", "title": "Demystifying TIGZIG API Errors: A Practical Guide for MFPRO, TREMOR, and VIGIL Users", "date_published": "2026-07-20T04:00:00.000Z", "date_updated": "2026-07-26T18:00:00.000Z", "last_modified": "2026-07-26T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-api-errors-practical-guide-jul2026", "thumbnail": "/images/blog/tigzig_api_errors_onepager.png", "status": "published", "deck": null, "tags": [ "infrastructure", "mcp" ], "summary": "A troubleshooting field guide for the free tigzig.com data APIs (MFPRO mutual fund NAV, TREMOR macro/market, VIGIL India red flags, Yahoo Finance tools), built from real server-log patterns. Covers 20 common failure modes in plain English: trailing punctuation and & entities in copied URLs, over-encoded parameter joiners, missing required inputs, the JavaScript undefined trap, GET vs POST confusion, folder-style path guessing, comma-separated inputs on single-item endpoints, wrong format requests, opening MCP endpoints in a browser (406), deprecated SSE paths (405), per-API parameter naming differences, ISIN variant mismatches, model-hallucinated scheme codes, Yahoo market suffixes, and the empty-but-correct responses class: non-corporate assets without financials, dividend-free stocks, defunct schemes after the July 2026 backfill (38,000+ schemes since 2006), AMFI weekend/holiday publishing windows, and client timeouts on large batches. Ends with the copy-this-page-to-your-AI troubleshooting recipe and the full human + AI-agent documentation link set (API catalog RFC 9727, MCP server registry, raw markdown data dictionaries).", "twitter_status": "posted", "tweet_id": "2079219391800172983", "tweet_url": "https://x.com/amarHarolikar/status/2079219391800172983" }, { "id": "mfpro-data-dictionary-docs-jul2026", "slug": "mfpro-data-dictionary-docs-jul2026", "title": "Data Dictionary & Docs Live on MFPRO. Every Field Explained, 21-Metric Methodology, and How the India MF Universe Actually Looks.", "date_published": "2026-07-19T10:00:00.000Z", "date_updated": null, "last_modified": "2026-07-19T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-data-dictionary-docs-jul2026", "thumbnail": "/images/blog/mfpro_data_dictionary_onepager.png", "status": "published", "deck": "release", "tags": [ "mutual-funds" ], "summary": "Release note for the MFPRO documentation set on the India Mutual Fund NAV database. The data dictionary explains every field table by table (scheme_code, category_group_clean, where fields go blank); docs also cover Performance & Risk methodology for all 21 computed metrics with formulas and worked examples (CAGR, rolling returns, max drawdown, Calmar, beat rate, beta/R-squared, alpha/t-stat, Sharpe, Sortino, tracking error/information ratio, capture ratios, win rate, Treynor, VaR/CVaR, skewness/kurtosis, Ulcer Index, drawdown & recovery), the Scheme Universe field guide, How the Data Is Built, and composition analytics. For AI agents, https://api.tigzig.com/mf/v1/ returns the full machine-readable catalog: every endpoint with parameters, schema, stats, downloads, rate limits, error shape, plus explicit data_dictionary and methodology links. Includes a scheme-universe breakdown: of 38,000+ scheme codes since 2006, 8,538 active (22%) vs 29,466 inactive (78%, ~80% of those close-ended FMP-era), active split Debt 33% / Equity 28% / Index-ETF-FoF 27% / Hybrid 10% / Solution 2%, Direct 47% vs Regular 38%. Single-image release post with the docs one-pager PNG at the bottom.", "twitter_status": "posted", "tweet_id": "2078877219867492727", "tweet_url": "https://x.com/amarHarolikar/status/2078877219867492727" }, { "id": "mfpro-search-upgrade-full-history-jul2026", "slug": "mfpro-search-upgrade-full-history-jul2026", "title": "One Line of User Feedback, Fixed Within a Day. MFPRO Search Upgraded + Complete AMFI NAV History, 37M Rows, 2006 Onward.", "date_published": "2026-07-18T15:00:00.000Z", "date_updated": null, "last_modified": "2026-07-18T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-search-upgrade-full-history-jul2026", "thumbnail": "/images/blog/mfpro_update01_feedback_fixes.png", "status": "published", "deck": "release", "tags": [ "mutual-funds" ], "summary": "Release note on two MFPRO fixes driven by user feedback and daily log reading. One: a user reported ABSL Value and ICICI Pru Value missing - the funds were in the database, the search was the bug; search now expands 17 abbreviations and old fund names (ABSL, PPFAS, IDFC to Bandhan, Reliance to Nippon) and ranks results by relevance instead of alphabetically. Two: 404s in the logs led to a full rebuild of every NAV AMFI has ever published, 2006 onward, including closed, defunct, interval and close-ended funds - 37M rows total. Covers the staged validation: offline profiling at the pull, section-header category parsing with an explicitly reviewed legacy-taxonomy mapping, pilot-then-batched loads with cell-by-cell cross-tab reconciliation, existing-rows-win conflict policy (9 overlaps in 16.4M rows, logged), and a post-load multi-agent audit (7 finders + 7 adversarial verifiers, zero duplicate scheme-date pairs, zero orphans). Source oddities like 2006-era zero-NAV rows are published as filed, documented in the MFPRO Docs section. Single-image release post with the update one-pager PNG at the bottom.", "twitter_status": "posted", "tweet_id": "2078504827282464830", "tweet_url": "https://x.com/amarHarolikar/status/2078504827282464830" }, { "id": "tigzig-agent-first-ask-your-ai-jul2026", "slug": "tigzig-agent-first-ask-your-ai-jul2026", "title": "TigZig is Built Agent First. 300+ Assets on AI for Analytics. Just Ask Your ChatGPT, Gemini or Claude.", "date_published": "2026-07-17T17:00:00.000Z", "date_updated": null, "last_modified": "2026-07-17T17:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-agent-first-ask-your-ai-jul2026", "thumbnail": "/images/blog/tigzig_guides_ask_your_ai_onepager.png", "status": "published", "deck": "release", "tags": [ "ai-coders" ], "summary": "Release note explaining that tigzig.com is built agent-first: 300+ assets (210+ guides, 40+ live apps across 13 categories, open-source repos, a free open API and live MCP servers) on AI for analytics, and the recommended way in is to ask your own AI - ChatGPT, Gemini, Meta AI on WhatsApp, Claude, or an AI coder like Claude Code / Cursor / Codex - rather than browse. Includes example prompts (market valuations, database AI, macro data, app security, cloning and deploying the open-source repos), quick links to the main hubs (markets, claude-for-analytics, mutual-funds, mcp-landing, analysis), and pointers to the two explainers on chat apps vs cowork apps vs embedded apps vs AI coders. Single-image release post with the guides-and-tools one-pager PNG at the bottom.", "twitter_status": "posted", "tweet_id": "2078155826632782330", "tweet_url": "https://x.com/amarHarolikar/status/2078155826632782330" }, { "id": "ai-boom-warnings-jul2026", "slug": "ai-boom-warnings-jul2026", "title": "Three Warnings on the AI Valuation Boom in the Past Two Weeks: Warren Buffett, S&P Global & BIS", "date_published": "2026-07-16T18:00:00.000Z", "date_updated": null, "last_modified": "2026-07-16T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/ai-boom-warnings-jul2026", "thumbnail": "/files/AI_BOOM_WARNINGS_JUL2026/Slide1.PNG", "tweet_url": "https://x.com/amarHarolikar/status/2077797341583839269", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Three warnings on the AI valuation boom in the past two weeks - Warren Buffett, S&P Global and BIS (the bank for central banks). A legendary investor, a rater and a regulator. The BIS modelled the AI build-out and found the race over-invests 1.5 to 3 times the efficient level. Buffett is sitting on a record $397bn in cash and calls the market gambling. S&P cut Oracle to one notch above junk on the cost of its AI build-out. Nobody is questioning the technology - the parallel is dotcom (huge valuations on page views with no profit); this time it is users and revenue, better, but still no cash profit. Until the profit is real, the valuation is the exposed part. Seven-slide PDF deck attached (browsable carousel + PDF download). Also includes a one-month supervisor drills roundup - ECB doubled its private-credit probe, France ran its first system-wide non-bank stress test across Banque de France, ACPR and AMF, and the Bank of England sent 46 firms a five-year recession to model with Sarah Breeden invoking Akerlof's market-for-lemons on private credit - and an agent-first pitch for tigzig.com (dozens of analyses + several live tools; point ChatGPT / Gemini / Claude at the site with example prompts).", "twitter_status": "posted", "tweet_id": "2077797341583839269", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 } }, { "id": "ai-worker-not-ai-chatter-jul2026", "slug": "ai-worker-not-ai-chatter-jul2026", "title": "Jamie Dimon Uses Claude Code to Generate Dashboards. Are You? You Need an AI Worker or AI Coder, Not an AI Chatter.", "date_published": "2026-07-15T15:00:00.000Z", "date_updated": "2026-07-15T18:00:00.000Z", "last_modified": "2026-07-15T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/ai-worker-not-ai-chatter-jul2026", "thumbnail": "/images/blog/ai_worker_not_chatter_onepager.png", "tweet_url": "https://x.com/amarHarolikar/status/2077405551814971638", "status": "published", "deck": "analysis", "tags": [ "ai-coders" ], "summary": "Practitioner commentary framed around Jamie Dimon (JPMorgan CEO) using Claude Code to generate dashboards - and the author's argument that for serious CXO or analyst work you need an AI worker or an AI coder, not an AI chatter. Three-tier taxonomy: (1) AI Chatter = ChatGPT / Claude / Gemini in browser - fine for questions, can generate code and do Python, but no longer enough on its own; (2) AI Coworker = Claude Cowork and recently launched ChatGPT Work - can connect to external systems, pull data, run analysis and models, build decks, automate - a full working assistant that can check the work your team sends over or build rapid prototypes instead of lengthy team calls; (3) AI Coder = Claude Code / Codex / Cursor - all of the above plus complex models, scrapes, full-stack apps from build to deployment, essentially a data-science + research + tech team combined. How-to-start section: install the Claude or ChatGPT desktop app on your current plan, try things out, share the author's Cowork PDF guide with the AI itself and have it tutor you with test cases (they are great tutors, they just need to be asked), and lean on the tutorials from Anthropic / OpenAI plus YouTube. Cost economics: both Claude and ChatGPT start at $20 (light usage only), serious work is $100, heavy work is $200; ChatGPT has a free tier plus an $8 plan; team plans up to 150 users on flat per-seat pricing with usage included and the premium seat covering Claude Code and Cowork. Author's own economics as a freelancer: Claude Code is the primary tool from ML models to full-stack apps, ROI is a no-brainer, and 2 business clients moved to Claude Code with 'game-changer' impact in their own words. Enterprise tier is a different beast - a seat fee plus usage billed at API rates (not the flat team pricing), and the same heavy usage that fits inside the subsidized $200 individual plan can easily run $8K to $15K per person per month at metered API rates if pushed hard. Closes with a set of questions the AI Worker hub answers (Claude in Excel + PowerPoint, what works and what doesn't, MCP Server explained, how to connect to external data) and a link to the hub. Single-image analytical post with a one-pager PNG at the bottom.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2077405551814971638" }, { "id": "ai-is-not-just-chat-cowork-jul2026", "slug": "ai-is-not-just-chat-cowork-jul2026", "title": "AI Is Not Just Chat. Cowork Apps Are Where the Action Happens.", "date_published": "2026-07-13T15:00:00.000Z", "date_updated": null, "last_modified": "2026-07-13T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/ai-is-not-just-chat-cowork-jul2026", "thumbnail": "/images/blog/ai_not_just_chat_onepager.png", "tweet_url": "https://x.com/amarHarolikar/status/2076696214482125142", "status": "published", "deck": "analysis", "tags": [ "ai-coders" ], "summary": "Practitioner commentary on where the real AI work happens for analyst / builder workflows - the Cowork apps (Claude Cowork and now ChatGPT Work). These are the desktop apps that come as part of your Claude / ChatGPT subscription, and they do not just chat - they run code, pull data from external sources and CSVs, analyze it, and build reports and decks - basically do the work. Post plainly points out this is not one-golden-prompt magic - you still need to know what you are doing, provide context, guide, iterate, and most importantly validate results; the buck stops with you and the apps are just tools. Links two go-deeper resources: a 23-page guide on what Claude Cowork can do (author has just tried ChatGPT Work and confirms it can do the same things) and the Claude for Analytics hub. Quick-start tips section: install the desktop app and try things; if you want structured onboarding, share the guide PDF with the Cowork app and have it tutor you with test cases; the Anthropic docs at claude.com/resources/tutorials are simple language + good examples, and the author's favourites are curated in the Analytics hub. Second half - practitioner notes on when to use the other Claude flavors instead. Claude in Excel: stick to it for Excel work, far more efficient than doing Excel through Cowork. Claude in PowerPoint: efficient for PPT, but lately the author has stopped using PowerPoint - prepares decks in HTML from Cowork or Claude Code and converts to PDF, because HTML-to-PPT is wonky on font and placement and needs iterations. Claude Code: the author's bread and butter - the 40+ apps on tigzig.com have been built, maintained, and are defended by Claude Code (earlier Cursor); also solid for ML models with diagnostics reviewed as HTML reports, though the author tends to prefer CC over Cowork for that. Claude Design: rarely used - Claude Code or Cowork are faster for report and deck templates, Claude Design is overkill.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2076696214482125142" }, { "id": "tremor-macro-api-ask-your-ai-jul2026", "slug": "tremor-macro-api-ask-your-ai-jul2026", "title": "Tremor: A Free, No-Auth API for 330+ Macro and Market Indicators, 350K records, Refreshed 3x/Day. Ask your AI to explore.", "date_published": "2026-07-12T15:00:00.000Z", "date_updated": "2026-07-12T16:30:00.000Z", "last_modified": "2026-07-12T16:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-macro-api-ask-your-ai-jul2026", "thumbnail": "/images/blog/tremor_ask_your_ai_onepager.png", "tweet_url": "https://x.com/amarHarolikar/status/2076696155464024497", "status": "published", "deck": "release", "tags": [ "mcp", "infrastructure" ], "summary": "Release note on the current state of Tremor, tigzig.com's free open API and MCP server for macro, credit, valuation and market data. 331 public indicators across 8 categories, 350K records, refreshed 3x a day, no key and no sign-up. Ask-your-AI framing - point an AI agent at api.tigzig.com's Tremor and it reads the category menu (8 high-level buckets), drills into the one that matches the question, gets the full indicator list with metadata, searches if needed, then pulls the series - one prompt, the human never touches the docs. MCP also available for agents that prefer that surface. Category breakdown: US macro (35 - FRED 32 + Michigan Consumer Sentiment 1 + Manheim Used Vehicle Value Index 2), US credit stress (216 - NY Fed Household Debt 64 + FDIC bank aggregates 80 + NCUA credit-union aggregates 72, covering delinquency by product / charge-offs / originations by credit tier), US valuation (1 series - Shiller CAPE since 1881, with the us_valuation grouping also surfacing the Buffett indicator and nominal GDP from us_macro and the 10Y Treasury yield), US insurance (currently empty - Fed Z.1 Life + P&C invested-asset breakdown scaffolded and loading), India macro and credit (47 - MoSPI 27 + RBI 15 + NSE 3 + FRED USDINR 1 + one computed CD ratio), Global macro (8 - OECD Business Confidence for 4 countries + Bundesbank 10Y Bund + Bank of England 10Y Gilt + ECB Euro-area 10Y yield curves for AAA and all-issuers), Currencies (7 daily FX pairs including USDINR + EM Asia + Fragile-X peer cuts USDJPY / USDKRW / USDTWD / USDPHP / USDTRY / USDVND), Markets (16 daily Yahoo Finance symbols - S&P 500, Nifty 50, Nikkei 225, Hang Seng, DAX, Shanghai, US 10Y ^TNX, DXY, gold, silver, copper, WTI, Brent, natural gas, USDINR, USDJPY). Each indicator ships a notes field with the gotchas baked in for the agent: us_real_gdp is already SAAR-annualized (do not multiply by 4), in_nifty50_pe has a standalone-to-consolidated methodology break in April 2021, in_net_fdi negative means outflow and there is a 6+ month publication lag. Pipeline detail: pull via API where available (FRED, RBI WSS, ECB Statistical Data Warehouse, OECD, Bundesbank, Bank of England); for sources published only as files with year-to-year format drift (NCUA 5300 Call Reports, FDIC SDI aggregates, NY Fed Household Debt Study, MoSPI eSankhyiki, RBI DBIE Excel, Shiller's ie_data.xls, Manheim MUVVI) the file is downloaded, parsed with error correction, standardized into a series. Runs 3x a day - Fed publishes at 3pm ET, we pick it up in the next cycle and it is live in Tremor by early evening. Multi-cadence handling: RBI is a mix (weekly rates + fortnightly credit + monthly trade/FDI/FPI), MoSPI monthly or quarterly by series, NSE Nifty PE/PB daily, quarterly GDP + GFCF + credit-union aggregates land once a quarter and get ingested the day published. Pipeline does error checking: sums validate, YoY consistency checked, data quality issues (missing values, spikes that do not match the source document) flagged in metadata so the caller sees them. Sources summary lists FRED, NY Fed, FDIC, NCUA, RBI, MoSPI, NSE, ECB, Bundesbank, BoE, OECD, Shiller, Michigan, Manheim, Yahoo Finance with a one-line note on each. Includes 'what moved recently' section with current-through dates on Treasury curve + VIX + Baa credit spread + 10Y TIPS + 10Y-2Y spread + 30Y fixed mortgage rate + PMMS + initial jobless claims + Bund + Gilt + Euro-area yield curves + the 16 daily market symbols. Launched March with ~50 indicators; now 331 and growing. Single-image release post with the one-pager PNG at the bottom.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2076696155464024497" }, { "id": "trump-2025-annual-disclosure-jul2026", "slug": "trump-2025-annual-disclosure-jul2026", "title": "Trump's 2025 annual financial disclosure: all 28,007 records, extracted and searchable", "date_published": "2026-07-11T18:00:00.000Z", "date_updated": null, "last_modified": "2026-07-11T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/trump-2025-annual-disclosure-jul2026", "thumbnail": "/images/blog/trump_2025_annual_disclosure_onepager.png", "tweet_url": "https://x.com/amarHarolikar/status/2075935749988564999", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Release post announcing that OGE certified and released President Trump's 2025 annual public financial disclosure (Form 278e, 927 pages) on June 30, 2026, and the whole thing has been extracted into a structured, searchable, downloadable dataset - 28,007 records across eight tables - and added to the Trump Trades tool on tigzig alongside the periodic 278-T trade reports. What is now on the tool: an Overview landing page explaining the two filing types, an At-a-glance dashboard, a Companies tab with ticker rollup, a vs S&P 500 tab that plots all 142 trading days on the index chart, full table viewers with search / filters / sort / pagination for Transactions / Holdings / Business & Income, a Liabilities & Gifts tab, a Data tab with record counts + per-table CSVs + the full JSON + the source PDF, and an About & Validation tab. Extraction breakdown: Transactions Part 7 = 21,285; Holdings Part 6 = 6,181; Business entities Part 2 Schedule 1 = 501; Liabilities Part 8 = 11; Gifts Part 9 = 11; Spouse assets and income Part 5 = 11; Positions Part 1 = 4; Agreements Part 3 = 3. Excludes the 54-page Exhibit A trademark schedule (unstructured) and Part 4 (filed not applicable). Non-obvious rule flagged in the post: the annual's transaction section does NOT repeat trades already disclosed on the 278-T trade reports (OGE rule, and this filing uses it), so the 21,285 trades in the annual are ones the public had not seen before (mostly from the managed stock accounts), while the municipal and corporate bond trades are only on the 278-Ts - neither document alone is the full 2025 trading record, the union is; empirically the two sets are essentially disjoint, and the filing's own cover notes that late filing fees were paid for transactions not previously reported on 278-Ts. Trading detail: 15,524 purchases against 5,761 sales, midpoint ~$942M bought vs $286M sold (net buyers all year), across 8 managed accounts (uneven - Account 7 alone holds 10,311 trades, Account 8 has 3,972, Account 6 has 2,130, Account 1 has 1,757), 2,148 distinct securities, 51% of trades in the $1,001-$15,000 disclosure band and 83% under $50,000 (pattern of professionally-managed direct-indexing / diversified accounts, not concentrated stock-picking), trades on 142 trading days including net buying near the bottom of the April 2025 drawdown. Top securities by gross midpoint volume with tickers consolidated: Microsoft (~$43.7M bought / $1.7M sold, 84 trades), NVIDIA ($42.0M bought, 65 trades), Apple ($41.4M bought, 71 trades), Amazon ($21.6M bought, 74 trades), Alphabet ($18.5M bought, 93 trades); by trade count Alphabet 93 / Meta 86 / Microsoft 84 / Amazon 74 / Apple 71; money-market sweeps excluded. Business income: 97 exact-dollar business lines totaling $2.16B - Celebration Coins royalties $635,068,835, World Liberty Financial token sales $526.8M across token-sale lines (main $236.25M + per-wallet distributions of $150.6M ETH / $56.0M USDC / $42.3M USD / $33.5M BTC), stablecoin-related DT Marks SC LLC $196.9M, Trump Endeavor 12 (Doral golf resort) $121.9M, Mar-a-Lago Club $77.5M, WLF Holdco equity sale $65.6M. Litigation settlements $86.5M - Twitter/X $8M, ABC $16M, CBS $16M, Meta $24.5M, YouTube $22M. Melania Trump entities separately $17.2M exact license proceeds in Part 5 (film $10.71M + NFTs $6.01M + book $0.52M). Note that press coverage generally aggregates crypto items to ~$1.4B total but the tool lists each line as filed rather than as one combined number. Holdings: 6,181 lines totaling ~$1.86B midpoint across the 8 investment accounts + a revocable trust + a family trust + direct holdings - heavy in municipal bonds and blue-chip equities. Revocable trust holds a ladder of brokerage money-market accounts including two reported at 'Over $50,000,000' each, and the direct holdings include a line reading 'INVESTMENT IN GOLD BARS' in the $500K-$1M band. Liabilities: 11 lines including the two E. Jean Carroll judgments (one over $50M stayed pending appeal + bonded), the NY AG judgment noted as overturned on appeal, a Charles Schwab pledged-asset line over $50M opened in 2025, and several mortgages three of which are marked paid off in 2025. Gifts: 11 gifts totaling $372,050 - mostly event tickets (Super Bowl, FIFA World Cup Final, Ryder Cup, UFC) plus a $250,000 sculpture. Convention flagged: securities values and trade amounts are disclosed as ranges so every dollar total involving securities uses the midpoint of each range and 'Over $X' ranges use the lower bound - treat those totals as approximations; business income figures are exact dollar amounts as filed. Validation methodology on the tool: (1) two independent extraction methods with unrelated parsing engines reconciled field-by-field across every row to 100% agreement, (2) completeness gates on the filing's own row numbering + every amount / type / date matched against the official OGE vocabulary, (3) independent AI vision cross-reads on sampled pages + the full business-entity schedule blind to the dataset with disagreements adjudicated against a high-resolution page image (zero extraction errors found), (4) external tallies checked against reported figures (exact-dollar match on the $635M Celebration Coins line). Filing quirks preserved as filed (inconsistent capitalization, occasional typos, filer-truncated names). Author flags plainly that OGE publishes the filing only as a PDF with no machine-readable version, and this dataset is a best-effort reproduction of a 927-page government document not the document itself - the source PDF is one click away on the tool for verification. Sources: the OGE-certified 278e PDF (927 pages) and the OGE release note of June 30, 2026. Analytical release post - single-image, one-pager PNG at the bottom.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2075935749988564999" }, { "id": "india-bank-credit-may2026", "slug": "india-bank-credit-may2026", "title": "India bank credit, May 2026: up 18%, and the mix is where the story is", "date_published": "2026-07-10T18:00:00.000Z", "date_updated": "2026-07-10T20:00:00.000Z", "last_modified": "2026-07-10T20:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/india-bank-credit-may2026", "thumbnail": "/images/blog/india_credit_may2026_onepager.png", "tweet_url": "https://x.com/amarHarolikar/status/2075614657176571992", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Analytical commentary on RBI Sectoral Deployment of Bank Credit for May 2026 - total bank credit at about Rs 215 lakh crore, up 18% year-on-year with banks adding roughly Rs 32 lakh crore over twelve months. The mix is where the story is: loans against gold jewellery grew about 105% (more than doubled to ~Rs 5.1 lakh crore, share moving from 1.4% to 2.4%), and bank lending to NBFCs grew about 34% (share 8.5% to 9.7%) - gold loan companies sit inside the NBFC bucket so both banks and NBFCs are lending more against gold at the same time (Equifax independently confirms gold loan originations up ~103% YoY for the quarter to March 2026). Slow lane: housing (largest consumer segment at ~Rs 33.7 lakh crore, ~16% of all bank credit) grew only 11%, well under the 18% average, and lost the most share; credit card outstanding essentially flat, up 1%. The mechanism is not household preference - it is capital rules. RBI's November 2023 risk-weight hike on unsecured lending (personal loans and credit cards) and on bank lending to NBFCs deliberately excluded loans secured by gold, plus housing, education and vehicle loans - so one type of lending became costlier on capital while gold stayed cheap. February 2025 partly reversed the NBFC and microfinance weights, which is part of why the NBFC number is as high as it is. Second driver: gold price up around 39% YoY through end-May 2026 boosted collateral value on gold loans - but gold peaked near USD 5,370 in early March 2026 and has since fallen ~23% to around USD 4,130 by early July 2026, so loans given near the top are the tight ones on LTV (still not a segment-wide problem because gold is higher than a year ago, but the group of at-risk loans grows if the price keeps falling). One important correction: NBFC and microfinance did NOT both boom - microfinance actually shrank ~9% YoY to roughly Rs 3.33 lakh crore after a stressed stretch and is only now recovering. Post explicitly is NOT claiming households are consciously swapping credit cards for gold loans - only that the flat cards and fast-growing gold share a common cause (capital rules + gold price). Includes pointer to the interactive tool on tigzig -> Tremor -> India Credit for sorting by growth / share / share of new credit added, with CSV download. Full source list: RBI Capital Adequacy Directions 2025 (RBI/DOR/2025-26/151 for banks, /345 for NBFCs), the three superseded risk-weight circulars from 2023 and 2025, the RBI 4-Dec-2025 consolidation of 9000 circulars into 238 Master Directions (unchanged values), the forthcoming Standardised Approach Directions 2026 effective 1 April 2027, plus Equifax gold + microfinance releases and COMEX gold futures via Tremor. Single-image analytical post - one-pager PNG at the bottom.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2075614657176571992" }, { "id": "mfpro-data-api-download-jul2026", "slug": "mfpro-data-api-download-jul2026", "title": "MFPRO Data & API Screen Live - Download the Whole India MF NAV Database in One Click. No Code, No API.", "date_published": "2026-07-10T14:00:00.000Z", "date_updated": null, "last_modified": "2026-07-10T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-data-api-download-jul2026", "thumbnail": "/images/blog/mfpro_data_api_screen.png", "tweet_url": "https://x.com/amarHarolikar/status/2075564830409511286", "status": "published", "deck": "release", "tags": [ "mutual-funds", "converters-tools" ], "summary": "Release note announcing that MFPRO (India Mutual Fund Advanced Analytics on tigzig.com) now has a Data & API screen - no code, no API, point-click-download. Two flows: (1) pick a basket of up to 20 funds, any period, pull their full NAV history as CSV; (2) take the entire database - every scheme since 2008, 20M+ rows - in one click. Whole-database download comes in four formats: CSV and TSV about 132 MB each, Parquet 107 MB, SQLite 748 MB. The file is rebuilt three times a day from AMFI. For the API crowd, everything on this screen is also an endpoint - docs at tigzig.com -> APIs -> MF NAV, and live uptime for all of it at status.tigzig.com. Post also carries a short note to frontend users: if you were running MF advanced analytics on the frontend these past few days and hit errors, that was the revamp in motion, those paths are being smoothed out and most of it already fixed. Deep-links straight to the MFPRO Download Full Database tab. Single-image release post with the actual Data & API screen screenshot.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2075564830409511286" }, { "id": "anthropic-marketing-ops-claude-skills-jul2026", "slug": "anthropic-marketing-ops-claude-skills-jul2026", "title": "Anthropic's Marketing Ops on Using Claude for Campaign Reporting - and How I Do the Same for Small Businesses. Notes on Skills.", "date_published": "2026-07-09T14:00:00.000Z", "date_updated": "2026-07-09T17:00:00.000Z", "last_modified": "2026-07-09T17:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-marketing-ops-claude-skills-jul2026", "thumbnail": "/files/ANTHROPIC_MKTG_OPS_JUL2026/Slide1.PNG", "tweet_url": "https://x.com/amarHarolikar/status/2075240349325549893", "status": "published", "deck": "analysis", "tags": [ "ai-coders" ], "summary": "Commentary on Anthropic's marketing-ops team post about how they run campaign reporting and builds using Claude Cowork - the practical details of moving from chat to actual coworker runs. Anthropic's setup, per their public post: weekly metrics reporting cut from days to hours using Sunday runs, and campaign ops triggered via Slack, routed to specialists, and verified by an audit agent - everything controlled via skills. Post carries an 8-page summary deck of the Anthropic setup, and links to Anthropic's own full post. Second half is the author's own practical experience running campaign ops for small businesses (no Slack on the client side - it is calls, emails and WhatsApp) - the core setup revolves around four skills (list creation, list validation, response modeling, campaign measurement), each carrying business-specific context and business rules very specific to the domain and client, plus pointers to baseline Python scripts that the agent modifies. Modeling / measurement skill produces the outputs the author wants (validations, decile tables, Gini, ROC charts) - nearly all outputs as HTML nowadays. Measurement skill also includes business rules like what counts as an attributable conversion. Important tips section - every recurring task = skill; skills carry business context and how-you-want-it-done, not code (agents don't need to learn Python; they need to learn how you want it done); create baseline scripts and point at them from the skill; update skills immediately after every task and session; and every few weeks run a skills reorg (author has a skill for that too). Worth-reading section links Anthropic's own Complete Guide to Building Skills for Claude PDF, their agents-and-tools docs, and the author's Claude Cowork guide + Claude for Analytics hub. Closes with a section of the author's own tips on skills: names carry more weight than you'd expect (renaming working-style.txt to amar-user-profile.txt killed a persistent ambiguity); skills vs CLAUDE.md - any 'when this comes up, do these 5 steps' workflow belongs in a skill, the skill description IS the pointer, not a CLAUDE.md doc line; live code pointers over prose - agents open the real script; description does the triggering, body does the doing (use YOUR actual phrasing in the description - author's own examples: 'do the cache warmer thing', 'bat it', 'break it', 'do the SEO thing').", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2075240349325549893" }, { "id": "fred-your-ai-pulls-directly-jul2026", "slug": "fred-your-ai-pulls-directly-jul2026", "title": "FRED, 845,000 US Economic Series - Your AI Can Pull Any of Them Directly. No MCP, No Plugin, No Install.", "date_published": "2026-07-08T18:00:00.000Z", "date_updated": null, "last_modified": "2026-07-08T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/fred-your-ai-pulls-directly-jul2026", "thumbnail": "/images/blog/fred_api_your_ai_onepager.png", "tweet_url": "https://x.com/amarHarolikar/status/2074874495203185051", "status": "published", "deck": "release", "tags": [ "ai-coders", "infrastructure" ], "summary": "Pitch + practical starter for pointing an AI at public data APIs directly, no plugin required. Headline: FRED, the St. Louis Fed's free database, has ~845,000 economic time series (inflation, jobs, GDP, rates, markets) and your AI (Claude Cowork, OpenAI Codex, Claude in Excel and PowerPoint) can just reach in and pull any of them - no MCP server, no plugin, no install, free. Mechanism: each of those AI tools has a Python sandbox inside, so calling an API is just Python for them, and an API reads like plain English ('get me US gasoline prices from FRED, chart it, tell me what changed' - it goes, connects, pulls, analyses, pushes into Excel or a slide or an HTML report). Three-step get-going: grab a free FRED API key at fredaccount.stlouisfed.org/apikeys (generous rate limits), first time enable outside access for the agent (one setting), then just ask. Post also links two go-deeper resources: the Claude Cowork guide (a deck on how the author runs the whole thing) and the Claude for Analytics hub. Second half lists a handful of free APIs the author keeps using: FRED (845k US series, key required), World Bank (api.worldbank.org, no key, global GDP + inflation + debt), US Treasury FiscalData (api.fiscaldata.treasury.gov, no key, federal debt + rates), ECB reference FX (api.frankfurter.dev, no key), Yahoo Finance (via yfinance in the sandbox). Plus author's own free tigzig.com endpoints (all on api.tigzig.com, no key): Tremor (curated US + global macro/credit/valuation indicators), Mutual Fund NAV India from AMFI (19.8M records, 17k schemes), Vigil (India corporate red flags), Portfolio Analysis, AI Technical Analysis, Securities Comparisons. For a human - tigzig.com API/MCP menu. For an agent - copy the page or point it at api.tigzig.com. Single-image release post.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2074874495203185051" }, { "id": "tigzig-apis-status-page-live-jul2026", "slug": "tigzig-apis-status-page-live-jul2026", "title": "Tigzig APIs - Reading the Logs, Shipping the Fixes. Status Page Live, R2 Downloads, Self-Correcting 4xxs.", "date_published": "2026-07-08T15:00:00.000Z", "date_updated": null, "last_modified": "2026-07-08T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-apis-status-page-live-jul2026", "thumbnail": "/images/blog/tigzig_status.png", "tweet_url": "https://x.com/amarHarolikar/status/2074870789833449607", "status": "published", "deck": "release", "tags": [ "mutual-funds", "infrastructure" ], "summary": "Release note on what shipped for the Tigzig public APIs in the last 48 hours, driven by daily log reading (200s tell what is working, 4xx and 5xx tell what to fix next). Four things live: (1) common-guess coercion - if a caller or their AI guessed /series instead of /nav or sent start= instead of since=, those now quietly work with one canonical spec still in the docs; (2) every 4xx now returns a help block linking the docs page and the machine catalog so an AI agent can self-correct without the human stepping in; (3) full-database downloads (100 to 500+ MB files) now served from Cloudflare R2, noticeably faster than before; (4) status page live and probed every minute at status.tigzig.com - downtime rings the author's phone. Underway: fallback to a secondary server if the primary goes down. Second half of the post is a practical errors-and-fixes list: 404 (scheme code guessing/enumeration - use /search?q=fund-name or an ISIN from the broker statement, both work), heavy parallel bursts now return proper 429s instead of the occasional 503, 429 rate limits (300/min per public IP with X-RateLimit headers so scripts/agents can pace themselves), 400 validation (YYYY-MM-DD dates, 50-scheme bulk cap, scheme= for one vs schemes= for many), and every failure comes back as JSON with an error code + docs_url pointing straight at the fix. Polling tip: send If-Modified-Since and you get a 304 on days AMFI hasn't published (weekends, holidays) so scripts can skip re-downloading history they already have. Single-image release post - status page screenshot.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2074870789833449607" }, { "id": "tigzig-copy-page-button-jul2026", "slug": "tigzig-copy-page-button-jul2026", "title": "Copy Page as Markdown - Every Page on Tigzig.com Now Hands Your AI Full Context in One Click.", "date_published": "2026-07-05T14:00:00.000Z", "date_updated": null, "last_modified": "2026-07-05T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-copy-page-button-jul2026", "thumbnail": "/images/blog/copy_page_button_onepager.png", "tweet_url": "https://x.com/amarHarolikar/status/2073798449766801724", "status": "published", "deck": "release", "tags": [ "ai-coders", "converters-tools" ], "summary": "Release note announcing the site-wide 'Copy page' button on tigzig.com. Every page (API doc, MCP guide, blog post, hub, landing page) now carries a Copy page split-button that hands the reader's AI (Claude Cowork / Codex / ChatGPT / Gemini) the entire page as clean markdown - full context in one paste, no partial copy paste and no screenshots. Workflow: read an API or Excel-MCP guide, click Copy page, paste into your AI, and it takes it from there - explains the endpoints, writes the code, deploys on the user's machine. Alternative flow for the browse-averse: skip the site entirely and just ask the AI directly ('go to tigzig.com and find what to do for my Excel to talk to my database.. and help me deploy it also'). Working example inside the post: pasted the MF NAV API page (tigzig.com/apis/mf-nav) into Claude Cowork with a 5-year SIP analysis prompt - it searched the scheme by name, pulled 2,464 daily NAVs, ran 120 monthly installments of Rs 10,000 on Parag Parikh Flexi Cap Direct, and returned Rs 12 lakh grown to Rs 30.5 lakh, XIRR 17.75% - one paste, no code from the user. Framing: the whole site is now AI-Agent-First, humans welcome too. Standalone one-pager PNG attached at the end.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2073798449766801724" }, { "id": "tigzig-private-credit-hub-jul2026", "slug": "tigzig-private-credit-hub-jul2026", "title": "New Hub - Private Credit Stress on Tigzig.com. 19 Analyses, 3 Live Tools, Regulator Reports on One Page.", "date_published": "2026-07-04T18:00:00.000Z", "date_updated": null, "last_modified": "2026-07-04T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-private-credit-hub-jul2026", "thumbnail": "/files/TIGZIG_PRIVATE_CREDIT_HUB_JUL2026/Slide1.PNG", "tweet_url": "https://x.com/amarHarolikar/status/2073774786241794451", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Announcement post for the new Private Credit Stress hub on tigzig.com (/private-credit) that consolidates the author's ongoing coverage - 19 analyses, 3 live tools, and the underlying regulator reports on one page. Framing set at the top: 'is private credit cracking? Depends who you ask' - 2008 mortgage-market analogy where the market was either perfectly stable or falling apart depending on the source, right up to Lehman. What the last 30 days added: $14B of investor money sitting behind 5% redemption gates, one Blue Owl fund with redemption requests for 38% of its shares, a BlackRock BDC chief exiting mid federal valuation probe, and PIMCO calling out a 'confidence gap' where NAVs reflect manager marks rather than a shared clearing price - so the question has moved from 'are borrowers defaulting' to 'can we trust the marks'. Post also carries two LinkedIn-comment sections. Section one - 'The real story: in just one month supervisors moved from writing warnings to running drills' - the June regulator drills: ECB doubled its private credit probe to 20+ banks with annual reporting (Jun 15), France ran its first ever system-wide NBFI stress test with Banque de France + ACPR + AMF, 25 institutions, fire-sale and interconnection risk (Jun 17), Bank of England sent 46 firms a hypothetical five-year recession (Jun 19), and at Sintra both Lagarde and Bailey flagged non-bank oversight in the same week. Warnings are cheap; stress tests cost money and staff, so three central banks funding drills in one month is not idle curiosity. Bull case acknowledged - gates working as designed so far, no fire sales, Goldman and Oaktree saw redemption demand below their caps. Section two - 'Market for lemons' - Sarah Breeden (Bank of England deputy governor for financial stability) April warning that private credit could tip into an Akerlof-style lemons market: no market price (the manager's own mark IS the price), stacked leverage at borrower + fund + sponsor, sliced structures too complex for anyone outside to see through - same features 2008 CDOs ran on. Same worry echoing across FSB (data gaps + valuation opacity), OCC (PIK masking credit deterioration), PIMCO (confidence gap). Ties back to the author's May 2026 'private credit market for lemons' post. 2-page overview PDF carousel included.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2073774786241794451" }, { "id": "tigzig-mf-nav-api-revamp-jul2026", "slug": "tigzig-mf-nav-api-revamp-jul2026", "title": "Free Mutual Fund NAV API for India - Revamped. AI Ready. 18,000+ Schemes Since 2008 from AMFI.", "date_published": "2026-07-04T14:00:00.000Z", "date_updated": null, "last_modified": "2026-07-04T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-mf-nav-api-revamp-jul2026", "thumbnail": "/files/TIGZIG_MF_NAV_API_REVAMP_JUL2026/Slide1.PNG", "tweet_url": "https://x.com/amarHarolikar/status/2073412176673693878", "status": "published", "deck": "release", "tags": [ "mutual-funds", "converters-tools", "mcp" ], "summary": "Release note for the revamped free Mutual Fund NAV API for India on tigzig.com. Daily NAVs for 18,000+ schemes since 2008, straight from AMFI. Free, no sign up, no API key. Rebuild targets AI + automation friendliness: latencies and errors significantly down, more capacity, accepts AMFI Code or ISIN or a mix (auto-detected at the backend), plus a set of other flexibility improvements captured in the attached 2-page overview. Claude Cowork and OpenAI Codex can work with the API directly - point them at the API docs page and they take it from there. Claude in Excel needs a thin MCP wrapper on top (open source coming soon). Two consumption paths: API docs at tigzig.com -> APIs -> MF NAV, or the MFPRO UI at tigzig.com -> MFPRO which runs advanced analytics on up to 200 schemes in one shot (risk, returns including rolling returns, alpha, betas, drawdowns). Whole database is also downloadable in two ways: human path via MFPRO Data and API button (Parquet, CSV, TSV, SQLite), or AI/API path via a single GET on /mf/v1/download which returns all 20.5M+ rows as a single file. Approx sizes: Parquet 109 MB, CSV 132 MB compressed, SQLite 462 MB compressed. Every metric in MFPRO ships with a validation pack - the UI computes on DuckDB SQL and the validation pack recomputes in pure Python, with a ready-to-run main.py against the NAV file so a user or their AI can reproduce and extend. 2-page overview PDF carousel included.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2073412176673693878" }, { "id": "tigzig-claude-for-analytics-hub-jul2026", "slug": "tigzig-claude-for-analytics-hub-jul2026", "title": "Claude for Analytics on Tigzig.com - New Hub: Excel, PowerPoint, Cowork, 7 MCP Servers, 20+ Guides.", "date_published": "2026-07-01T15:00:00.000Z", "date_updated": null, "last_modified": "2026-07-01T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-claude-for-analytics-hub-jul2026", "thumbnail": "/files/TIGZIG_CLAUDE_ANALYTICS_HUB_JUL2026/Slide1.PNG", "status": "published", "deck": "guide", "tags": [ "claude-in-excel", "mcp", "excel" ], "summary": "Launch announcement for the new 'Claude for Analytics' hub on tigzig.com (canonical URL /ai-in-excel) - one page that pulls together everything the author has built and written on driving analyst work with Claude in Excel, PowerPoint and Cowork. Contents: 20+ guides, 5 PDF playbooks, 7 MCP servers, plus the source workbooks (formulas, charts, models). Specific capabilities highlighted: talk to Postgres and DuckDB from inside Excel, pull 81 risk and return metrics live into a sheet (QREP MCP), work with Claude-in-Excel and have it push results into Claude-in-PowerPoint for a deck, and Cowork extending the same Claude beyond the sheet to files, databases, ML models and code as one frontend. Two ways to consume - browse the hub yourself or ask Claude / ChatGPT / Gemini to read the page and summarise. All free, almost all open source. 2-page overview PDF included as a small carousel.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2073310306227585094" }, { "id": "bis-ai-boom-red-flag-central-banks-jun2026", "slug": "bis-ai-boom-red-flag-central-banks-jun2026", "title": "BIS, the Central Bank of Central Banks, Just Red-Flagged the AI Boom. Railway Mania, Roaring 20s, Dotcom - Same Chart.", "date_published": "2026-06-30T18:00:00.000Z", "date_updated": "2026-06-30T20:00:00.000Z", "last_modified": "2026-06-30T20:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/bis-ai-boom-red-flag-central-banks-jun2026", "thumbnail": "/files/BIS_AI_REDFLAG_JUN2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Commentary on the Bank for International Settlements' (BIS - owned by 63 central banks) June 2026 Annual Economic Report that flagship-red-flags the AI boom. BIS charts today's AI boom next to canal mania, railway mania, roaring 20s and dotcom - each of which attracted 'capital in excess of what commercial returns could ultimately justify' and ended in economy-wide recessions. Six BIS red flags: $1trn+ hyperscaler AI capex outpacing free cash flow (part funded by debt); circular financing with 'the same asset being pledged multiple times' (2008 language, in print before the event); valuations pricing growth above anything these firms have ever delivered; the bust is modelled in the BIS adverse scenario (sector surplus goes negative); direct lenders quadrupled AI/IT lending to ~15% of books; firms signalling automation on earnings calls (BIS 'demand bottleneck' scenario - every displaced worker is a lost consumer). Charles Mackay's 1841 book 'Extraordinary Popular Delusions' invoked as historical parallel. Ties to author's earlier documented calls since March (Tremor Early Warning, Irrational Exuberance at S&P 7,400, Private Credit as Market for Lemons). Post also carries two companion pieces from Ashwinder Singh Bakhshi (22 years fixed-income, former MD Barings): 'phantom collateral' on the same BIS sentence about pledged assets, and 'Margin Called' on Korea's KOSPI 100% rally with W148 trillion of foreign-institutional selling absorbed by retail on record margin debt. 10-slide deck with BIS charts + author's read on each.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2072701427995271479" }, { "id": "markdown-to-pdf-hub-launch-jun2026", "slug": "markdown-to-pdf-hub-launch-jun2026", "title": "New Markdown to PDF Hub on Tigzig.com - ReportLab, Repos, Live Tools, MCP + API Endpoints.", "date_published": "2026-06-30T14:00:00.000Z", "date_updated": null, "last_modified": "2026-06-30T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/markdown-to-pdf-hub-launch-jun2026", "thumbnail": "/images/blog/markdown_to_pdf_onepager.png", "status": "published", "deck": "release", "tags": [ "converters-tools", "mcp", "fastapi" ], "summary": "Release announcement for the new Markdown to PDF Hub on tigzig.com - the author's learnings, open-source repos, live tools, and endpoints for taking AI-generated markdown into a properly formatted PDF report (charts, side-by-side tables, page templates, layout you want). Built on ReportLab (uses only the open-source version, all features present). Try it live at quants.tigzig.com - have the AI agent run a technical analysis report or just hit the sample button (~30-60s). Multiple surfaces: form-based interface for humans, AI-assisted interfaces, an API endpoint (Claude Cowork can connect directly, no MCP server required), and MCP servers for AI. Free, open source, no sign-up, no key. Quick tips section covers the working-style trade-offs: this is not an auto-layout tool (you set the format once), stick with ReportLab + Claude Code for iteration speed, Gemini 2.5 Flash is the go-to LLM for auto-formatting (cost-effective + reliable), the PDF conversion is split into a separate backend service (author's choice; combine into a single app for own use), the main trick is getting the AI to return structured JSON with image paths and formatting tags, and offline automation is straightforward but live tools need edge/frontend/backend/MCP security.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2072318721671266503" }, { "id": "boris-cherny-archetypes-roles-melting-jun2026", "slug": "boris-cherny-archetypes-roles-melting-jun2026", "title": "Boris Cherny: Engineering, Product, Design, Data Science Are Melting Into Archetypes. Seeing the Same.", "date_published": "2026-06-29T18:00:00.000Z", "date_updated": null, "last_modified": "2026-06-29T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/boris-cherny-archetypes-roles-melting-jun2026", "thumbnail": "/files/BORIS_CHERNY_ROLES_JUN2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "ai-coders" ], "summary": "Commentary on Boris Cherny's (Creator & Head of Claude Code at Anthropic) point that engineering, product, design and data science are melting into one kind of role and the work now maps to archetypes (prototyper, builder, sweeper, grower, maintainer) rather than to titles. The author sees the same in small businesses where he is bringing in Claude Code and Claude Cowork. Day-to-day pattern: marketing and finance folks building their own local automations and dashboards (often in VBA and Python); developers and builders turning those into production-grade tools, then maintaining them, working full-stack regardless of where they started out (frontend, backend, etc.); data scientists running more models, better validated, refreshed more often, and creating working prototypes of deployable tools and handing off to builders. Boris' team-mix view: pre-PMF products lean on archetypes 1+2+3, growing/found-PMF products on 2+3+4 plus some 5, strong-PMF products on 3+4+5 plus some 2. The 4-slide carousel pairs Boris' original framing with the author's own field observations. Work moves across the team; job titles matter less and less.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2071587155068256404" }, { "id": "qrep-claude-in-excel-mcp-jun2026", "slug": "qrep-claude-in-excel-mcp-jun2026", "title": "QREP in Excel: 81 Risk and Return Metrics Inside Your Sheet Through Claude in Excel.", "date_published": "2026-06-29T15:00:00.000Z", "date_updated": "2026-06-29T16:30:00.000Z", "last_modified": "2026-06-29T16:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/qrep-claude-in-excel-mcp-jun2026", "thumbnail": "/images/blog/qrep_excel_onepager.png", "status": "published", "deck": "release", "tags": [ "mcp", "claude-in-excel", "portfolio-analytics", "excel" ], "summary": "Standalone one-pager release: QREP, a Tigzig MCP server, brings 81 risk and return metrics straight into Excel through Claude in Excel - asset returns, drawdowns, Sharpe, Sortino, alpha, beta, volatility and more. Just ask Claude in Excel things like 'Compare gold and bitcoin, last 10 years' or 'compare Mag 7 past 10 years' - any stock, commodity or crypto that's on Yahoo Finance. Change symbols, time period, run cuts your own way, format it your way, push it into a deck, or do it directly in PowerPoint. MCP guide at tigzig.com -> MCP -> QREP. For AI-coders, just ask Gemini / ChatGPT / Claude to help connect Claude in Excel to QRep from tigzig.com - it walks through. Wary of someone else's MCP server? The code is open source, a single Python file - ask your AI to deploy the QREP MCP server from tigzig.com. Free to use. Powered by Ran Aroussi's QuantStats. Three companion guides linked: Claude Cowork Guide, Claude in Excel & PowerPoint 26 working tips, and Claude as the universal agent.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2071534003979682041" }, { "id": "tigzig-mcp-hub-launch-jun2026", "slug": "tigzig-mcp-hub-launch-jun2026", "title": "New MCP Hub on Tigzig.com - 7 MCP Servers, 6 Live Apps, 16 Guides, 108-Point Security Checklist. Just Ask Your AI.", "date_published": "2026-06-26T17:00:00.000Z", "date_updated": null, "last_modified": "2026-06-26T17:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-mcp-hub-launch-jun2026", "thumbnail": "/images/blog/tigzig_mcp_hub_onepager.png", "status": "published", "deck": "release", "tags": [ "mcp", "security" ], "summary": "Standalone one-pager release announcing the new MCP hub on tigzig.com - 7 open-source and live MCP servers, 6 live apps, 16 guides, plus a 108-point security checklist. Designed for AI and automation builders. Either browse the hub at tigzig.com/mcp, or just ask ChatGPT / Gemini / Meta AI on WhatsApp / Claude something like 'Go to tigzig.com's MCP page and lemme know what's there' - the AI will pull what's there and Claude Code / any AI coder will implement it for you. The hub carries reusable components that work across domains - sending data and charts to AI and getting structured results back, formatted PDF and HTML output, accessing backend databases on DuckDB and Postgres, Python processing for statistical and quant analysis. Live MCP servers and apps cover macro data, a database query server, portfolio analytics, and tools used regularly for own analysis. The companion security checklist (tigzig.com/security) covers backend, auth, database, edge and third-party apps - 108 items.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2070516131530764377" }, { "id": "tigzig-ask-chatgpt-stop-browsing-jun2026", "slug": "tigzig-ask-chatgpt-stop-browsing-jun2026", "title": "Stop Browsing Tigzig.com. Ask ChatGPT First. (Or Whichever AI You Use.)", "date_published": "2026-06-26T13:00:00.000Z", "date_updated": null, "last_modified": "2026-06-26T13:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-ask-chatgpt-stop-browsing-jun2026", "thumbnail": "/images/blog/tigzig_ask_chatgpt_onepager.png", "status": "published", "deck": "release", "tags": [ "ai-coders" ], "summary": "Standalone one-pager release: tigzig.com now has 300+ assets (live apps, repos, build guides, data APIs, MCP servers, analysis) and is indexed across ChatGPT, Claude, Gemini and Meta AI on WhatsApp. Don't click through it the old way - just ask whichever AI you keep open, and it walks you through what is there. Sample agent prompts in the deck: 'tigzig.com - anything on Python in Excel?', 'tigzig.com - anything on Claude in Excel?', 'tigzig.com - any guides on security for AI tools?', 'tigzig.com - any portfolio analysis tools I can use?', 'tigzig.com - pull the Claude Cowork guide', 'tigzig.com - what is on mutual fund analytics?', 'tigzig.com - any infra guides for tool builders?'. Faster than clicking around, and a fair way to sanity-check the site too. All free, AI-agent-first.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2070145413240475844" }, { "id": "tigzig-tremor-v2-mcp-330-indicators-jun2026", "slug": "tigzig-tremor-v2-mcp-330-indicators-jun2026", "title": "Tigzig Tremor v2 Is Live. 330+ Macro + Credit Indicators Behind an MCP. 83% Lighter Startup. Just Ask Your AI Agent.", "date_published": "2026-06-24T14:00:00.000Z", "date_updated": null, "last_modified": "2026-06-24T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-tremor-v2-mcp-330-indicators-jun2026", "thumbnail": "/images/blog/tremor_v2_onepager.png", "status": "published", "deck": "release", "tags": [ "mcp", "portfolio-quants" ], "summary": "Release announcement for Tigzig Tremor v2 - the MCP behind tigzig.com's 330+ macro and credit indicators across US, India, global macro, credit, markets and FX (17+ official publishers). Pulling macro data for analysis is mostly grunt work - finding the series, downloading, cleaning - so it now sits behind an MCP that AI agents (Claude Cowork, Claude in Excel, any agent) can ask in plain English. Sample asks in the post: 'US consumer net charge-offs, banks vs credit unions, last 15 years, chart it', 'S&P 500 and CAPE past 25 years, dual axis', 'Overlay Brent and WTI crude past 5 years'. The headline v2 change: catalog discovery split from tool calls. v1 stuffed the whole catalog into one ~8,000-token tool description the agent loaded up front; v2 surfaces an 8-category menu, the agent drills into only the tables it needs and pulls only the relevant bits - startup load cut from 7,854 to 1,352 tokens, 83% lighter. The discovery layer also carries house notes on each series (e.g. 'this charge-off is quarterly, multiply by 4 to annualize') so the AI does not quietly get it wrong. Free, open, no sign-up - use the live MCP, install your own, hit the APIs directly, or download the whole DB in your chosen format. Humans: tigzig.com -> TREMOR -> API / MCP. AI agents: api.tigzig.com.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2069783657095856383" }, { "id": "cors-anywhere-alternative-cloudflare-worker", "slug": "cors-anywhere-alternative-cloudflare-worker", "title": "CORS Anywhere Alternative: A Free Cloudflare Worker CORS Proxy (POST and Any URL)", "date_published": "2026-06-22T10:00:00.000Z", "date_updated": "2026-06-22T10:00:00.000Z", "last_modified": "2026-06-22T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cors-anywhere-alternative-cloudflare-worker", "thumbnail": "/images/blog/corsFastTips.png", "status": "published", "tags": [ "infrastructure", "security" ], "markdown_file": "output/markdown-manually-fixed/cors-anywhere-alternative-cloudflare-worker.md", "html_file": "output/html-pages-manually-fixed/cors-anywhere-alternative-cloudflare-worker.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-06-22T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "skip", "summary": "A free cors-anywhere alternative and replacement: a self-hosted Cloudflare Worker CORS proxy that supports POST and any URL via the ?url= parameter, and adds CORS headers. Full copy-paste Worker code plus a GitHub repo. Works like cloudflare-cors-anywhere on a free workers.dev URL. Covers the four locks (origin allow list, target allow list, X-API-Key, rate limit), what Origin means, and the free-tier safety net. Companion to the main CORS proxy post." }, { "id": "tigzig-excel-to-databases-ai-find-jun2026", "slug": "tigzig-excel-to-databases-ai-find-jun2026", "title": "Connect Excel to a Database with Tens of Millions of Rows? Tigzig.com Has 15+ Apps and Guides for That Alone. Just Ask Your AI.", "date_published": "2026-06-19T15:00:00.000Z", "date_updated": null, "last_modified": "2026-06-19T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-excel-to-databases-ai-find-jun2026", "thumbnail": "/images/blog/tigzig_excel_db_onepager.png", "status": "published", "deck": "release", "tags": [ "excel", "python-in-excel", "mcp" ], "summary": "Standalone one-pager release: how to connect Excel to a database with tens of millions of rows and GBs of data. There are 15+ apps, repos and guides on tigzig.com just for this one niche - xlwings-Lite (Python in Excel), a Claude-in-Excel MCP server, open repos connecting to MySQL, PostgreSQL and DuckDB, plus security setups, auth and semantic layers. And Excel-to-databases is only one corner - the whole site has 250+ assets, from live tools to open repos. The point of the post: don't click around. Tigzig.com is indexed across ChatGPT, Gemini, Claude, Google AI and Meta AI on WhatsApp, so just ask whichever AI you keep open what you want to pull from the site - it will find it and walk you through. If you are on Claude Code, Cursor or any AI coder, it will download and implement it for you. Sample agent prompts in the deck: 'What does tigzig.com have for connecting Excel to a database?' and 'What about security, semantic layer, auth?'", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2068711028016501244" }, { "id": "ecb-npl-small-bank-corporate-strain-jun2026", "slug": "ecb-npl-small-bank-corporate-strain-jun2026", "title": "Euro-Area NPLs Look Calm. Split by Bank Size, the Small-Bank Corporate Book Is at 5.16% and Climbing.", "date_published": "2026-06-19T13:00:00.000Z", "date_updated": null, "last_modified": "2026-06-19T13:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/ecb-npl-small-bank-corporate-strain-jun2026", "thumbnail": "/files/ECB_NPL_NOTE_JUN2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Euro-area non-performing loans are near a decade low - but the calm headline hides where the stress is. Split by bank size and the small banks (less significant institutions, LSI - the Sparkassen, cooperative and savings banks; ~15% of euro-area bank assets) have pulled ahead of the big banks (significant institutions, SII, ~85% of assets). Big-bank NPLs sit near 2%; the small-bank corporate book has climbed to 5.16%, up about a third since 2023 and still rising. The consumer book is steady; the corporate book is the engine, and within that the two stressed corners are commercial real estate and small-business loans. Germany, Austria and Italy carry most of it - right where corporate insolvencies are running hot (Germany +57% vs the 2016-19 pre-pandemic May average per IWH; Austria 6,810 full-year 2025, +3.4%, property failures +35%; Italy ~+38% YoY per Allianz Trade). Households stay calm in the big systems (Germany 1.6% NPL, Netherlands 0.7%); the euro-area signal is corporate. Supervisors agree (ECB SREP 2025, OeNB FSR 51, ECB FSR May 2026) and tailored letters have gone to banks. Cycle to watch, not a system in trouble. 10-slide deck pulled live from the ECB SDMX REST API on 20 Jun 2026 (Q1 2026 release for SII / Q4 2025 for LSI - LSI lags one quarter by design, so SII vs LSI comparisons all use Q4 2025). Full 8-table copy-as-markdown dataset published at tigzig.com/ecb-npl-data-tables. Plus the parallel for the US: bank headline is also calm but the strain sits on the consumer (cards + autos) and in private credit, where losses hit the P&L directly and never show as a bank charge-off.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2068561383080554552" }, { "id": "us-consumer-squeeze-real-retail-jun2026", "slug": "us-consumer-squeeze-real-retail-jun2026", "title": "May 2026's Record US Retail Sales Number Is Hiding the Pain. Real Spending Hasn't Grown in Five Years. The Consumer Is Squeezed.", "date_published": "2026-06-18T19:00:00.000Z", "date_updated": null, "last_modified": "2026-06-18T19:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-consumer-squeeze-real-retail-jun2026", "thumbnail": "/files/US_CONSUMER_SQUEEZE_JUN2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "The May 2026 record US retail sales headline ($763.7bn, +6.9% YoY) is hiding the pain of a consumer being squeezed - and the squeeze was here long before the Iran war. Strip out inflation and real goods spending has not grown in five years: of the $763.7bn rung up in May 2026, only $605.9bn is real 2021 goods - the other $157.8bn is pure inflation. Households are holding the line by stretching: personal saving rate back to pre-2008 lows, NY Fed HELOC balances up 40% since 2021. The strain is starting to show: University of Michigan consumer sentiment collapsed to 44.8 (near the lowest on record), and 90+ day delinquencies on all consumer debt are back to 3.4% - past the 2007 mark. Real GDP still grows ~2% on services, AI investment and government, so this is a squeeze on the household goods budget, not an economy-wide downturn. The proof is the balance sheet: thinning savings, more borrowing, delinquencies past 2007. Companion piece: jobs market softening since 2024 (unemployment 4.3%, underemployment 8.1%, long-term joblessness near a 3-year high). Cheaper oil is real relief on the price side, but it does not refill a drained savings account or put the long-term unemployed back to work. 6-slide deck with every series live on tigzig.com -> Tremor (retail RSAFS + RRSFS, saving rate BEA, HELOC + delinquency NY Fed, sentiment U-Mich), pullable by API, Excel, or AI agent. Real retail definition: nominal Advance Retail & Food Services Sales deflated by headline CPI (the honest test of volume vs price).", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2067954939356422191" }, { "id": "working-with-claude-cowork-claude-code-jun2026", "slug": "working-with-claude-cowork-claude-code-jun2026", "title": "Claude Cowork and Claude Code Have Quietly Become My Universal Front End. More Than Excel, Jupyter or Colab.", "date_published": "2026-06-18T17:00:00.000Z", "date_updated": "2026-06-19T08:00:00.000Z", "last_modified": "2026-06-19T08:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/working-with-claude-cowork-claude-code-jun2026", "thumbnail": "/files/WORKING_WITH_CLAUDE_JUN2026/Slide1.PNG", "status": "published", "deck": "guide", "tags": [ "ai-coders", "mcp" ], "summary": "First-person guide on running almost the whole analysis-and-modeling pipeline from one place. For the author, Claude Cowork (and Claude Code) have quietly become the universal front end - past Excel, Jupyter and Colab - covering exploration, cleaning, ensembles, hyperparameter tuning, validation, and final presentation deck. Each iteration is shared as HTML reviewed in the browser, so a session ends with both a model AND full documentation written as work progressed. Same flow works for database-connected analysis, not just model builds. Excel work goes through Claude in Excel or Cowork; PowerPoint too. Full-stack builds stay on Claude Code. The difficult part was letting go: after years of notebooks and code editors, with the comfort of code you can touch and feel, stepping back to talk into a chat box and review the results took some getting used to. Once you let go, it is way faster and gives much better results, since the human focuses on validations, logic, business and domain pieces - the places where an AI agent still needs guidance. Includes a 23-slide deck that Cowork put together about itself: chat -> coworker shift, desktop + Code teammate, hitting APIs (FRED, Yahoo Finance), reaching backends via MCP, working files in place, cleaning messy spreadsheets, 11.85M-row / 1.6GB analyses in 13 seconds, EDA-as-browser-page iteration loop, gradient-boosting model builds with ROC / gains, A/B significance tests, packaging as HTML/PDF/Word/PPT/Excel, live dashboards that pull fresh data, real URL fetching (not snippets), scheduled briefings, browser/desktop control, cross-session memory, sandbox toolkit (4GB RAM / 10GB disk / pandas / polars / pyarrow / scikit-learn / matplotlib / OpenCV / Pillow), no-code-on-your-machine handoff to Claude Code, and Anthropic's own resources (Cowork product, best-practices guide, the HTML-beats-Markdown post, computer-use docs). Deck self-built in Cowork as HTML then exported to PDF.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2067908664779010465" }, { "id": "tigzig-ask-your-ai-front-door-jun2026", "slug": "tigzig-ask-your-ai-front-door-jun2026", "title": "AI Is the Front Door Now. Don't Dig Through Tigzig.com - Ask Your AI About It.", "date_published": "2026-06-18T13:00:00.000Z", "date_updated": null, "last_modified": "2026-06-18T13:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-ask-your-ai-front-door-jun2026", "thumbnail": "/images/blog/tigzig_ask_ai_onepager.png", "status": "published", "deck": "release", "tags": [ "ai-coders", "mcp" ], "summary": "Standalone one-pager release: AI is the front door now. Just like you ask your AI about Cloudflare, Vercel or AWS instead of digging through the docs, tigzig.com (40+ live apps, repos, ~200 build guides, data APIs, MCP servers, 100s of assets) is indexed across the major AI assistants - ChatGPT, Gemini, Google AI, Claude, WhatsApp Meta AI - so you can ask whichever one you keep open what's there, what's not, and how to use it before you even visit. Sample questions: Is tigzig.com legit? What can it do with my database? Does it have a security checklist for AI tools? How do I pull a portfolio or technical-analysis report? What free data APIs does it have? Quicker than browsing, and a fair way to sanity-check it.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2067508168943677737" }, { "id": "mf-nav-api-india-free-no-signup-jun2026", "slug": "mf-nav-api-india-free-no-signup-jun2026", "title": "A Free Mutual Fund NAV API for India. AMFI Data. No Key, No Sign-Up. 20.4M+ Records, 18,000+ Schemes, Back to 2008.", "date_published": "2026-06-17T18:00:00.000Z", "date_updated": null, "last_modified": "2026-06-17T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mf-nav-api-india-free-no-signup-jun2026", "thumbnail": "/images/blog/mf_nav_api_onepager.png", "status": "draft", "hidden": true, "redirect_to": "/post/free-mutual-fund-nav-api-india", "deck": "release", "tags": [ "mutual-funds", "fastapi" ], "summary": "Release announcement for the Tigzig Mutual Fund NAV API for India - a free, no-key, no-sign-up open data API serving AMFI data. Pulls the entire NAV history (20.4M+ daily records across 18,000+ schemes back to 2008) in a single call as Parquet, CSV, TSV or SQLite, refreshed three times a day as the NAVs get filed. Also supports single-scheme pulls with name search and date filters. Endpoint: api.tigzig.com/mf/v1/download. Designed AI-agent-first: point Claude Code, Cursor, or any AI coder at api.tigzig.com and it pulls the lot and runs the analysis for you. For humans who prefer a UI: tigzig.com -> MFPRO runs analytics, risk, returns, comparisons, and lets you download the full database. Free.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2067247272124174729" }, { "id": "anthropic-claude-code-expertise-survey-jun2026", "slug": "anthropic-claude-code-expertise-survey-jun2026", "title": "Agentic Coding and Persistent Returns to Expertise - Anthropic's 400k Claude Code Sessions, Summarized", "date_published": "2026-06-17T14:00:00.000Z", "date_updated": "2026-07-09T10:00:00.000Z", "last_modified": "2026-07-09T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-claude-code-expertise-survey-jun2026", "thumbnail": "/files/CLAUDE_CODE_EXPERTISE_SURVEY_JUN2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Anthropic just published 'Agentic coding and persistent returns to expertise' - a privacy-preserving analysis of ~400,000 interactive Claude Code sessions from ~235,000 people, October 2025 to April 2026. Three takeaways pulled into a 9-slide summary deck with every headline a direct quotation: (1) coding background is becoming less relevant - every major occupation succeeds at rates within a few points of software-related occupations (software engineers verified at 34%; all ten largest groups within seven points); (2) management occupations show the highest verified success, slightly above software engineering, which Anthropic reads as management skills transferring to directing an agent (with a measurement caveat); (3) the more domain expertise a person brings, the more work Claude does per instruction (more than twice the actions, five times the output from the same prompt) and the more often the session ends in success (verified success doubles 15% to 33%, novice-to-intermediate is where most of the jump happens, and only 5-7% of expert sessions end abandoned vs 19% for novices). Bonus: the share of sessions spent fixing broken code fell from 33% to 19% while average task value rose 27% vs a freelance-market benchmark. Anthropic's honest caveat: classifiers remain challenging to validate at scale; they cannot measure whether the code is actually used. Matches the author's 25 years as an analyst - on coding the agents win by multiples, on deep domain the human still wins.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2067149450280423664" }, { "id": "tigzig-agent-first-just-point-your-claude-jun2026", "slug": "tigzig-agent-first-just-point-your-claude-jun2026", "title": "The Fastest Way to Use Tigzig.com? Don't Click. Point Your Claude / AI Agent at It.", "date_published": "2026-06-13T15:00:00.000Z", "date_updated": null, "last_modified": "2026-06-13T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-agent-first-just-point-your-claude-jun2026", "thumbnail": "/images/blog/tigzig_agent_first_just_point.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "mcp" ], "summary": "The fastest way to use tigzig.com is to not click - point your Claude or AI agent at it. The site is built AI-agent-ready: 40+ analytics apps, open analytics APIs, MCP servers, 200+ guides, plus a repos shelf - all free, no sign-up. An agent pointed at the site can find the right tool / guide / dataset for the task, read app and database data and run the analysis, call the analytics APIs, set up MCP servers, and even download / install / deploy an open-source repo. Claude Cowork is particularly good at this - it runs its own sandbox inside the Claude desktop app, so once a domain is enabled it makes the API calls itself, pulls the data, runs the analysis and hands back a PDF. Codex, Cursor, any agent works the same way. Rather explore yourself? The whole site is still right there - the agent just gets you there faster. Don't believe it? Point your agent at tigzig.com, let it explore, then ask it 'how do you like the site?'", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2066030760705114170" }, { "id": "trump-vs-dimon-debanking-crypto-two-fronts-jun2026", "slug": "trump-vs-dimon-debanking-crypto-two-fronts-jun2026", "title": "Why Trump Is Going After Jamie Dimon Right Now. Two Fronts - Debanking + Crypto. The DOJ Subpoena Made It Real.", "date_published": "2026-06-13T12:00:00.000Z", "date_updated": null, "last_modified": "2026-06-13T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/trump-vs-dimon-debanking-crypto-two-fronts-jun2026", "thumbnail": "/images/blog/trump_vs_dimon_two_fronts.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Why is Trump going after Jamie Dimon right now, five years after JPMorgan shut his accounts weeks after Jan 6, and why this particular week? The legal war front: Feb 2021 debanking, Aug 2025 Fair Banking order, Jan 2026 Trump sues Dimon for $5 billion, and for months the lawsuit just sat there - lawyers filing papers, nothing moving. This week it got teeth: on 10 Jun 2026 the DOJ (through Jeanine Pirro's office) subpoenaed JPMorgan, Bank of America and Wells Fargo over the debanking. Private lawsuits limping along are one thing; the federal machinery moving in behind them is a different beast. Running alongside is the crypto fight - Trump signed the GENIUS Act last summer (the big stablecoin law) and wants the US to be 'the crypto capital of the world'; his family has crypto ventures of their own. Dimon has been the loudest bank voice fighting the next bill (CLARITY Act) and told the Coinbase CEO to his face he is full of it. Then on 5 Jun 2026 - days before the subpoenas landed - the big banks announced they are building their own blockchain money, a shared tokenized deposit network, so customer deposits stay locked in the regulated banking system and do not bleed out into the stablecoins Trump has been backing. Did the crypto fight cause the DOJ to move? No paper trail. But the incidents line up too close to wave away - read the motive for yourself.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2065784644667335087" }, { "id": "anthropic-vs-us-government-fable5-shutdown-jun2026", "slug": "anthropic-vs-us-government-fable5-shutdown-jun2026", "title": "The Fable 5 Shutdown Isn't About a Jailbreak. It's the Anthropic vs US Government Standoff Coming to a Head.", "date_published": "2026-06-13T08:00:00.000Z", "date_updated": null, "last_modified": "2026-06-13T08:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/anthropic-vs-us-government-fable5-shutdown-jun2026", "thumbnail": "/images/blog/anthropic_vs_us_government_standoff.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "This week's Fable 5 shutdown is not really about a jailbreak. It is the Anthropic vs US government standoff coming to a head. Months ago Anthropic refused to give the Pentagon 'all lawful use' of its models, holding two red lines - no fully autonomous weapons, no domestic surveillance. Trump ordered every federal agency to stop using Anthropic, and the Pentagon branded it a 'supply chain risk', a label normally reserved for foreign adversaries - the first American company to get it. The defense work went to OpenAI. A federal judge called the blacklisting 'classic illegal First Amendment retaliation' and 'Orwellian', but an appeals court let the blacklist stand while the case runs. Meanwhile the NSA - under the same Pentagon - is reported (per Axios) to be using Anthropic's most powerful model anyway: too dangerous to allow, too useful to drop. This week Commerce blocked all foreign nationals from Fable 5 and Mythos 5 over a jailbreak Anthropic says is minor and doable on other models. To comply, Anthropic switched both models off worldwide. The irony: the Pentagon's whole fear was that Anthropic could pull the plug on its own model. This week, Anthropic pulled the plug. A standoff between a company that draws red lines on its own tech and a state that treats frontier AI as too strategic to leave to a company. The company has the model. The government has the machinery.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2065685021000884626" }, { "id": "iran-deal-fragility-peace-on-loss-of-face-jun2026", "slug": "iran-deal-fragility-peace-on-loss-of-face-jun2026", "title": "Markets Are Celebrating an Iran Deal. Nobody Has Agreed Which Deal. Peace Is Balanced on Loss of Face.", "date_published": "2026-06-12T19:00:00.000Z", "date_updated": null, "last_modified": "2026-06-12T19:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/iran-deal-fragility-peace-on-loss-of-face-jun2026", "thumbnail": "/images/blog/iran_deal_fragility_peace_loss_of_face.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Markets are celebrating an Iran deal, but nobody has agreed which deal. Trump says the war is 'ended'; Tehran calls the reports 'merely speculation' and says there is no final decision yet. CNN counts ~40 times Trump has called a deal imminent. ~100 days into the war the macro damage is real: UK GDP at -0.1% in April (released today), US gasoline $4.65, stressed US consumer, global economy showing cracks. Two drafts in circulation, and they look almost nothing alike. Tehran's 14-point version (leaked to Iranian state media, picked up by CNBC): ceasefire on all fronts, naval blockade lifted, Hormuz reopened on Iran's arrangements, oil sanctions suspended, $12bn unfrozen before talks even begin, $300bn reconstruction plans, US forces out, missiles and proxies off the agenda - and the nuclear negotiation only after all of that, with a 60-day window. Washington's version (via diplomat to CNN): Hormuz reopened immediately with no Iranian fees, sanctions relief with no set date, deal 'satisfies all US requirements on the nuclear issue.' Each side's plan reads like the other side's surrender. Stocks jumped ~2%; Brent futures fell 4%. Whichever version gets signed as written, the other side loses face. My read: whatever gets signed will be brittle - this peace has already cracked four times since March. If it survives its own 60-day window, we are through to the other side.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2065421075115303365" }, { "id": "free-mutual-fund-nav-api-india", "slug": "free-mutual-fund-nav-api-india", "title": "Free AMFI Mutual Fund NAV API for India - Historical NAV by Scheme Code, No Key", "date_published": "2026-06-12T10:00:00.000Z", "date_updated": "2026-09-09T10:00:00.000Z", "last_modified": "2026-09-09T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/free-mutual-fund-nav-api-india", "thumbnail": "/images/og-image.png", "status": "published", "tags": [ "mutual-funds", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/free-mutual-fund-nav-api-india.md", "html_file": "output/html-pages-manually-fixed/free-mutual-fund-nav-api-india.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-06-12T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "skip", "summary": "A free, open AMFI Mutual Fund NAV API for India - no key, no sign-up. 20.5M+ daily NAV records across 18,000+ schemes, back to 2008, refreshed daily. Search schemes, pull a scheme's full NAV history by code and date, or download the ENTIRE database in a single call (Parquet / CSV / SQLite). Copy-paste curl and Python examples." }, { "id": "us-inflation-shock-no-modern-playbook-jun2026", "slug": "us-inflation-shock-no-modern-playbook-jun2026", "title": "There Is No Modern Playbook for This. US Core Inflation at 3.3%, Re-Accelerating. Producer Prices at 6.4%. Consumer Delinquencies Past 2007.", "date_published": "2026-06-12T18:00:00.000Z", "date_updated": null, "last_modified": "2026-06-12T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-inflation-shock-no-modern-playbook-jun2026", "thumbnail": "/files/US_INFLATION_SHOCK_JUN2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "US core PCE - the gauge the Fed targets at 2% - is at 3.3% and re-accelerating. Outside Covid, last at this level in April 1992. 25-year pre-Covid average: 1.7%. It has now spent 62 straight months above target. And the pipeline says more is coming: PPI final demand at 6.4% - hottest in the modern series outside Covid - with core PPI at 4.9%; PPI is running 2.2 points ahead of CPI, putting the bigger share of the bill on producers (margin compression) but with consumer pass-through inevitable. The consumer is already stretched: gasoline at $4.65 (only June/July 2022 ever higher), and 3.4% of consumer debt is in the 90+ DPD bucket - past the 3.1% mark of late 2007 and more than double the end-2022 low, rising in 12 of the last 13 quarters. Against all of this the S&P set a record at 7,610 on June 2 with Shiller CAPE 2nd-highest in 145 years and Michigan sentiment lowest in 73 years - a handful of AI names doing most of the lifting. The last two times the market was priced like this while the economy cracked underneath - 2000 and 2007 - the index halved. 6-slide deck with full series codes, method, and the dot-com (-49%) / GFC (-57%) drawdown references.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2065362252157551068" }, { "id": "us-lending-data-q1-2026-tigzig-api-jun2026", "slug": "us-lending-data-q1-2026-tigzig-api-jun2026", "title": "Q1 2026 US Lending Data Live on Tigzig. NCUA + FDIC + NY Fed in One API. Two of Three Not on FRED. AI-Agent Ready.", "date_published": "2026-06-12T10:00:00.000Z", "date_updated": null, "last_modified": "2026-06-12T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-lending-data-q1-2026-tigzig-api-jun2026", "thumbnail": "/images/blog/us_lending_data_q1_2026_tigzig_api.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants" ], "summary": "Q1 2026 update of US lending aggregates is now live on Tigzig - loans, delinquency, charge-offs by segment back to 2002, ready for AI agents (Claude Cowork, Claude Code, Codex, Cursor) and for the human web UI on TREMOR. Three datasets: credit unions (NCUA), banks (FDIC), consumer (NY Fed) - two of those three are not on the FRED API; all three are on the Tigzig API with no auth, free. Plus 100+ macro indicators (US and India) to overlay on the data. All sourced from the regulators' own releases and validated before publishing - methodology and validation are on the site. AI-agent ready surface includes: charts and custom cuts on the web, an open API, an MCP server for Claude / Codex / any agent, and full-database downloads in Parquet, SQLite, DuckDB and CSV. This is the same data behind the recent US credit-union red flag deck (card charge-offs past their GFC peak 10 straight quarters) and the consumer credit stress piece. Point your AI at tigzig.com and it connects to the data, explores, and runs the analysis as you direct.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2065328184095301866" }, { "id": "india-offshore-shift-ai-compute-jun2026", "slug": "india-offshore-shift-ai-compute-jun2026", "title": "Two Announcements, One Day Apart. Operational Seats Moving Out of India. AI Compute Moving In.", "date_published": "2026-06-11T18:00:00.000Z", "date_updated": null, "last_modified": "2026-06-11T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/india-offshore-shift-ai-compute-jun2026", "thumbnail": "/images/blog/india_offshore_shift_ai_compute.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Two announcements, one day apart: Opendoor shut its India operations (~250 FTE) and is shifting to smaller AI-native US teams; Meta signed its first AI data center deal in India. Neither announcement is a first - what caught the eye is both reported within a day of each other. Engine 1, the seats: India offshore IT and BPM employs 5.95 million people and exports $246B a year; TCS announced ~12,000 layoffs last year and silent layoffs run at ~50,000 a year. Operational work is going home to smaller US AI-native teams. Engine 2, the capital: India data centers + power, grid, construction headed for ~10 GW by 2030 with $250bn+ already committed by Google, Meta, Adani, Reliance, Microsoft, AWS, TCS. The labour-arbitrage engine is showing cracks while the capital engine accelerates at dot-com cadence. Is this the real shift? Too early to call. The new engine runs on hardware, construction, power - and hard-hat engineers. Companion to the prior 'India's AI data center wave' and 'India economy red flag' analyses.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2065057616653652075" }, { "id": "us-unemployment-long-term-hidden-jun2026", "slug": "us-unemployment-long-term-hidden-jun2026", "title": "US Unemployment Is 4.3%. The Stuff Underneath Says Otherwise. 2M Jobless Over Six Months, Up 3 Straight Years.", "date_published": "2026-06-11T15:00:00.000Z", "date_updated": null, "last_modified": "2026-06-11T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-unemployment-long-term-hidden-jun2026", "thumbnail": "/files/US_UNEMPLOYMENT_LONG_TERM_JUN2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "US unemployment is 4.3% and the headline curve looks like it is calming - eased from 4.5% in Nov, held at 4.3% since March. The data underneath says otherwise. Long-term joblessness (27+ weeks) has nearly doubled since 2023 to 2.0 million people, with three straight years up and no give-back. When long-term pulls away from the headline like this, that is the 2008 pattern - into 2008 the long-term line towered about 6x its pre-crisis level while U-3 only doubled. Covid never had this shape; rehiring was too fast. Today's rise has the slow-grind 2008 shape, with no recession to blame. Composition: the long-term share fell to 17.8% after Covid (Feb 2023) and is back to 27.5% now (GFC extreme was 45.5%) - more than one in four of the unemployed are now past the line that scars. Structural test: measured against the whole workforce, long-term joblessness has stepped up once in 25 years - 0.53% to 0.81% in the dot-com era. This cycle is at 1.18% in May and still rising. Is AI doing to the jobless floor what dot-com did? Too early to call - the test comes when this cycle finally turns. Fits the picture from last week's note: consumer sentiment at a 73-year low, valuations near 145-year highs. 5-slide deck on BLS / FRED data through May 2026.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2065011970039038255" }, { "id": "brsr-india-esg-explorer-jun2026", "slug": "brsr-india-esg-explorer-jun2026", "title": "India Inc's BRSR Filings - All 656 in One Analytics Workspace. Wage Gap at Top Management: 68%. 1 in 3 Workers on Contract.", "date_published": "2026-06-10T18:30:00.000Z", "date_updated": null, "last_modified": "2026-06-10T18:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/brsr-india-esg-explorer-jun2026", "thumbnail": "/images/blog/brsr_india_esg_explorer.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants" ], "summary": "India Inc's ESG (BRSR) filings - workforce, wages, complaints - sit in separate XBRL files. Pulled 656 of them - the full Nifty Total Market basket, FY25 - into one analytics workspace on tigzig.com -> VIGIL -> BRSR. Aggregate, compare, benchmark, read the raw filing. Coverage: 656 companies across 8 indices and 22 sectors, ~1.2 crore workers. What the data shows: ~1 in 3 of India Inc's workforce is on contract; female share of total workforce under 1 in 5; median wage gap - women paid 11% less at employee level and 68% less at top management (KMP, Key Managerial Personnel). From aggregates to company level with sector benchmarking - every chart clicks through to the source XBRL filing. Full CSV download. Free. FY26 filings are coming in through AGM season; next data refresh planned for August.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2064702206444806406" }, { "id": "credit-union-red-flag-jun2026", "slug": "credit-union-red-flag-jun2026", "title": "Red Flag: US Credit Unions. A $1.73T System at GFC-Era Losses. Card Charge-Offs 10 Straight Quarters Past the Crisis Peak.", "date_published": "2026-06-10T15:00:00.000Z", "date_updated": null, "last_modified": "2026-06-10T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/credit-union-red-flag-jun2026", "thumbnail": "/files/CREDIT_UNION_RED_FLAG_JUN2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Red flag on US credit unions - a $1.73 trillion lending system running GFC-level losses in consumer, with no recession on the board. Card charge-offs hit 5.30% in Q1 2026, above the 2008-09 crisis peak (4.68%) for 10 consecutive quarters - a plateau, not a spike. Real estate and commercial are calm (59% of loans, 4% of losses). Consumer is 41% of loans and 95% of losses, with auto NCO at a fresh series record (0.97%) and cards at 33% of all CU loan losses. Measured vs each sector's own GFC peak: credit unions are at 113% on cards (past), banks just 38% - this is not system-wide banking stress, it is concentrated in credit unions. The silver lining: consumer balances are $19B below their end-2023 peak and falling, the shape of a deliberate tightening - but vintages already booked will run their loss curves. The Share Insurance Fund equity ratio is at 1.30% (below the 1.33% normal operating level); a wave could force a premium assessment on every surviving credit union (precedent: 2009-10 corporate CU crisis). The NCUA is run by a single board member - the other two were removed in April 2025, dispute now at the Supreme Court. Banks have Jamie Dimon. Credit unions have nobody. 13-slide carousel deck with full method, source notes and the failure-channel transmission path. Companion to the prior 'Auto Past the GFC Peak. Cards Almost There' post.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2064639883474543093" }, { "id": "us-valuations-record-sentiment-low-jun2026", "slug": "us-valuations-record-sentiment-low-jun2026", "title": "Index at a Record. Peak Valuation. Record-Low Sentiment. Last Two Times This Setup Held - 2000 and 2007 - the Market Halved.", "date_published": "2026-06-08T12:00:00.000Z", "date_updated": null, "last_modified": "2026-06-08T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-valuations-record-sentiment-low-jun2026", "thumbnail": "/images/blog/us_valuations_record_sentiment_low.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Index at a record. Peak valuation. Lowest sentiments. And the economy cracking. Last two times the market was priced up here - 2000 and 2007 - it halved. Shiller CAPE near 41 P/E, the highest in 145 years apart from the 1999-2000 dot-com bubble (44), above the 2007 peak (28). University of Michigan consumer sentiment at 44.8, the lowest in 73 years of data - below the 2022 low (50) and every recession since 1952. In dot-com it was all about page views with no line of sight on revenues; in 2026 it is all about compute and revenues with no line of sight on cash profitability. Companion to the prior 'Irrational Exuberance' note when the S&P was at 7,400. Consumer delinquencies high, unemployment high, producer prices the hottest in 25 years (excl Covid), oil and gasoline high, consumer stretched. Seven stocks doing most of the lifting in the index. Dimon called it too much exuberance. Buffett sitting on the biggest cash pile in Berkshire's history. Drawdown ground-truth from tigzig Market Tools: S&P 500 deepest -56.8% beginning Oct 2007, over five years to climb back to even, 58% of its time spent in the seven worst drawdowns. Not investment advice.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2063914739131142558" }, { "id": "consumer-credit-stress-fed-ncua-jun2026", "slug": "consumer-credit-stress-fed-ncua-jun2026", "title": "Auto Losses Past the GFC Peak. Cards Almost There. Credit Unions Already at GFC Loss Rates. NY Fed + NCUA Data Now on Tigzig.", "date_published": "2026-06-08T10:00:00.000Z", "date_updated": null, "last_modified": "2026-06-08T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/consumer-credit-stress-fed-ncua-jun2026", "thumbnail": "/images/blog/consumer_credit_stress_fed_ncua.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "NY Fed and NCUA aggregate loss / delinquency data comes out as a spreadsheet - pain to analyze. Pulled, disaggregated by quarter, validated and loaded into a database with an interface (tigzig.com -> TREMOR), an API (api.tigzig.com) and an MCP layer (mcp.tigzig.com). Quarterly coverage, by loan type, ~44 quarters, sourced from the regulators' own spreadsheets. And the data is talking loudly. Bank charge-offs look fine on the surface but the pressure is one step away from the banks: auto losses past the GFC peak, cards almost there. Credit unions running loss rates last seen during the GFC period - overall, consumer and cards; the card book has already pushed past its 2009 peak. And banks keep funding non-bank lenders - $1.65T to non-banks in Q1, up 42% in five quarters.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2063854783753306182" }, { "id": "private-credit-bdc-stress-regulators-jun2026", "slug": "private-credit-bdc-stress-regulators-jun2026", "title": "Private Credit and BDC Stress. Discounts at 13-Year Lows. Withdrawals Past the Gate. Regulators Now Arriving.", "date_published": "2026-06-06T14:00:00.000Z", "date_updated": null, "last_modified": "2026-06-06T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/private-credit-bdc-stress-regulators-jun2026", "thumbnail": "/images/blog/private_credit_bdc_stress.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "The median listed BDC trades at 25% discount to NAV, near its deepest in 13 years excluding Covid - 0.75x price to book today. Pimco's CIO: 'the first sustained default or loss cycle in many, many years.' Q1 losses: FS KKR $441M, Blue Owl's BDC $184M. Withdrawal requests blew past the 5% redemption gate at Blackstone's BCRED (~10%) and Cliffwater (17%) - a first-ever net outflow. Private-credit rating downgrades up 50% in a year. UBS sees defaults rising from ~4.4% to 9-10% by end-2026. US issuance fell ~40% vs Q1 to $44.8B in the 3 months to May. Partners Group warns the redemption pressure that began in private credit is now spreading into private equity. The regulators have started arriving: federal prosecutors in NY questioning BlackRock executives over how their credit fund valued loans; FSB flags private credit's leverage and interconnections as a source of broader risks to financial stability; ECB puts US private credit on its watchlist as a spillover risk into Europe; Sarah Breeden at the Bank of England calls it a potential 'market for lemons' (Akerlof). Companion update to the prior 'Is Private Credit Turning Into a Lemon' analysis.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2063158123955900751" }, { "id": "tigzig-apis-data-open-jun2026", "slug": "tigzig-apis-data-open-jun2026", "title": "The Data Behind My Analyses Is Now Open. Download the Whole Database or Query a Single Row.", "date_published": "2026-06-05T14:00:00.000Z", "date_updated": null, "last_modified": "2026-06-05T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-apis-data-open-jun2026", "thumbnail": "/images/blog/tigzig_apis_data_open.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants", "mcp" ], "summary": "The analyses I've been posting these past months all run on the same data - and that data is now open to everyone. US delinquencies, private credit, life insurance, the markets piece - all off primary data, FRED, RBI, Fed, FDIC, refreshed three times a day by the same pipeline that runs the live apps. Three open APIs powering the Tigzig apps: MF NAV (18,000+ schemes, 13 yrs, ~20M daily NAVs), VIGIL (corporate signals: ratings, insider, pledge, SAST, RPT), TREMOR (~300 macro indicators). Pull a single row across ~30 endpoints, or take the whole database from one endpoint in SQLite, DuckDB, Parquet, CSV or TSV - refreshed 3x a day by the CI/CD that powers the apps. Connect to the live MCP server, or wrap any of these APIs in your own MCP. No auth. Rate limited. Free to use. For humans: tigzig.com -> APIs. For agents: agents.tigzig.com.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2062806038609612906" }, { "id": "hormuz-quietly-opening-jun2026", "slug": "hormuz-quietly-opening-jun2026", "title": "Hormuz Is Quietly Opening. The First Good News in 100 Days. And It Is Running in Two Lanes.", "date_published": "2026-06-05T08:00:00.000Z", "date_updated": "2026-06-05T08:00:00.000Z", "last_modified": "2026-06-05T08:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/hormuz-quietly-opening-jun2026", "thumbnail": "/images/blog/hormuz_quietly_opening_v2.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "A trickle of tankers is getting out of the Strait of Hormuz again - the first real relief after more than three months of a shut chokepoint. But how they are getting out is the interesting bit. There is not one quiet arrangement, there are two. One lane Iran controls and charges a toll for, close to the Iranian coast (most traffic this way - CSIS says half the vessels since early March are from just four countries with China at the top). One lane the US Navy quietly watches over near the Oman coast - mines cleared, ~70 ships guided through in three weeks per New York Times sources; officially the US says it is not escorting, just coordinating. Tankers go dark on both sides - on Iran's lane to stay off the Western sanctions record, on the US lane to not broadcast their position to Iran. Use Iran's lane and you risk US sanctions; use the US lane and you risk an Iranian attack. Per Kpler, of the ~895 ships that crossed early March to mid-May, just over half took Iran's route and about 40% went by a route nobody can quite place - that 40% is the story. Producers shut in ~11 mbd; Energy Aspects: oil flow recovery will be slow, uneven and possibly incomplete. Gulf producers are even building pipelines to go around Hormuz. The US-Iran interim memo (30-60 days) is in the final stretch, leaving the strait basically under Iranian control however the fees end up dressed. Opening, yes - real. Solved, no.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2062759627671142744" }, { "id": "bank-loss-rates-look-calm-storm-brewing-may2026", "slug": "bank-loss-rates-look-calm-storm-brewing-may2026", "title": "Bank Loss Rates Look Calm. The Stuff Underneath - Consumer, Private Credit, Non-Banks - Says Otherwise.", "date_published": "2026-06-04T16:00:00.000Z", "date_updated": null, "last_modified": "2026-06-04T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/bank-loss-rates-look-calm-storm-brewing-may2026", "thumbnail": "/images/blog/bank_loss_rates_storm_brewing.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "US bank net charge-offs at 0.58% in Q1 2026 - right at pre-GFC, miles under the GFC peak, so the headline number looks calm. But pull the consumer book out and consumer NCO is already running hotter than pre-GFC at 2.9% vs 2.5% in 2007 - cards highest, auto rising. The sharpest stress sits off the bank books: most private credit losses are marked-to-market and hit P&L directly, never showing as a charge-off. Credit unions and non-bank consumer lenders carry the weakest paper, which loops back to banks via their non-bank and private credit lines. The macro is weak across unemployment, savings rate, producer inflation, oil and gas - all moving the wrong way. The bank NCO looks calm; the stuff it can't show (consumer cards, private credit mark-to-market, non-bank consumer lenders) says otherwise. Analysis supported by TIGZIG TREMOR self-serve analytics tool covering FDIC, NY Fed, NCUA and private-credit series.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2062427498592485757" }, { "id": "ecb-stress-test-private-credit-may2026", "slug": "ecb-stress-test-private-credit-may2026", "title": "ECB Stress-Tested Private Credit. Pensions Worst at 6% of Assets. Almost All the Damage Is the Wider Market Crash It Sets Off.", "date_published": "2026-06-02T16:00:00.000Z", "date_updated": null, "last_modified": "2026-06-02T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/ecb-stress-test-private-credit-may2026", "thumbnail": "/images/blog/ecb_stress_test_private_credit.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "The ECB just ran a three-stage stress test on private credit, part of the May 2026 Financial Stability Review special feature. Stage 1: direct private credit losses. Stage 2: contagion to leveraged loans and high-yield bonds. Stage 3: tail scenario (equities -30%, HY repriced -25%, money runs out of private credit funds). Almost all the simulated damage is Stage 3 - the wider market crash the scare sets off, not the loans themselves. Severe-scenario losses: insurance corporations ~4% of assets, pension funds ~6% (the worst-hit), banks barely move. Euro area's direct private credit exposure is tiny (insurers 2.3%, banks 0.2%), but the US owns multiples more - insurers ~10% (15%+ for PE-owned), banks ~1.3-7%, with $300B in private credit and $1.65T in broader non-bank lending. Author commentary: contagion is bigger than the asset (as in prior crises), and given simultaneous global pressures plus the dot-com / GFC playbook, this should not be treated as 'just tail risk' (GFC 2008 was supposed to be tail risk too, until Lehman). ECB report: 'Stress in global private credit markets and its implications for euro area financial stability.'", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2061796889381748949" }, { "id": "fsb-vulnerabilities-private-credit-report-may2026", "slug": "fsb-vulnerabilities-private-credit-report-may2026", "title": "FSB Just Published 50 Pages on Private Credit. The Title: 'Vulnerabilities in Private Credit.' Worth Reading.", "date_published": "2026-06-01T16:00:00.000Z", "date_updated": null, "last_modified": "2026-06-01T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/fsb-vulnerabilities-private-credit-report-may2026", "thumbnail": "/images/blog/fsb_vulnerabilities_private_credit.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "The Financial Stability Board's 6 May 2026 report 'Vulnerabilities in Private Credit' - 50 pages. The body that coordinates the Fed, ECB, BoE and the world's other financial regulators putting on the record what they're collectively worried about - layered leverage, opaque valuations, PE-owned insurers, interconnections and instrument risks that aren't fully understood, with a potential for systemic risk to the financial system in stress scenarios. One-pager highlights: market sizing $1.5-2T narrow estimate (full market can't be sized); multiple layers of leverage stacked on top of each other; US insurance liabilities run by PE-backed insurers grown from $67B to $900B. FSB quote: 'interconnections between private credit funds and banks, insurers, and private equity firms are deepening, raising potential vulnerabilities... investors and other stakeholders may only have partial information and understanding of correlations and concentrations... the sector's complexity, leverage, and interconnectedness could amplify stress in adverse scenarios, posing broader risks to financial stability.' Continues the Tigzig private-credit dispatch series (4 prior pieces).", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2061452895548325954" }, { "id": "india-economy-red-flag-may2026", "slug": "india-economy-red-flag-may2026", "title": "India Was the Market's Darling. Not Any More. The Growth Story Has a Big Red Flag.", "date_published": "2026-05-31T16:00:00.000Z", "date_updated": null, "last_modified": "2026-05-31T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/india-economy-red-flag-may2026", "thumbnail": "/files/INDIA_ECO_RED_FLAG_MAY2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "13-slide deep dive on the India macro red-flag stack as of May 2026. GDP forecast trims across the board - Moody's FY27 at 6% (the low-end call), S&P 6.6% (from 7.1%), ICRA 6.2% (from 6.5%). IT sector silent layoffs - TCS ~12,000 cut, ~50,000 IT jobs going this year, ~500,000 at risk over 2-3 years, with the model itself being questioned as AI replaces offshore engineering work. Foreign portfolio investors net seller (FY26 net exit, BofA expecting selloff into 2027/2028); FDI anchor held at +$7.7B. Wholesale Price Index at a 13-year high (ex-Covid spike); the WPI-CPI lag means retail catch-up is the base case. Record merchandise trade deficit of -$333B (FY26) on flat exports (+0.9%) and rising imports (+7.6%); China overtakes US as India's largest trading partner. Oil shock still playing out - four fuel hikes in two weeks, Delhi petrol Rs 102, OMC losses ~Rs 30,000 crore/month per ICRA. IMD El Niño monsoon forecast at 90% of LPA - driest in 11 years - which none of the GDP downgrades have priced in. Bankers warning publicly (Uday Kotak at CII: 'it's coming, and it's coming big'); PM appealing for fuel austerity. For India, anything below 4-5% GDP growth is recession territory. Any one is manageable. All of them at once is the problem. Silver lining: the India AI data-centre buildout (Adani, Reliance, Bharti) - covered in the prior India's Next Tech Wave dispatch.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2061087332854591942" }, { "id": "spot-the-odd-chart-out-may2026", "slug": "spot-the-odd-chart-out-may2026", "title": "Which Chart Is the Odd One Out? Three Macro Charts Tell the Same Story. One Doesn't.", "date_published": "2026-05-31T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-31T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/spot-the-odd-chart-out-may2026", "thumbnail": "/images/blog/spot_odd_chart_out_may2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Four-chart US macro one-pager (May 2026) asking the spot-the-odd-one-out question. Three charts show rising stress - U-6 underemployment at 8.2% (climbing since 2023, up from 6.7% Jan 2023 and past 8.7% Peak 2025), personal saving rate down to 2.6% (from 6.4% peak 2024 and 4.9% Jan 2023, the cushion is nearly gone), consumer 90 DPD loss rate at 3.4% (past the pre-GFC 3.06% reference, worst at credit unions and non-bank lenders). The fourth chart: S&P 500 at a record 7,580, up 86% over the three-year window. Three macro-stress charts in one direction; the index chart in the other. Tools that drive this kind of comparison are on tigzig.com - free, no login, 300+ series covering macros, prices, savings, labor and delinquencies, available via web interface, API or MCP for AI agents.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2061005144742060142" }, { "id": "us-banks-ndfi-q1-2026-update-may2026", "slug": "us-banks-ndfi-q1-2026-update-may2026", "title": "Banks Keep Funding the Non-Banks. $1.65T in Q1 2026, Up 42% in Five Quarters. US Banks NDFI Update.", "date_published": "2026-05-30T16:00:00.000Z", "date_updated": null, "last_modified": "2026-05-30T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-banks-ndfi-q1-2026-update-may2026", "thumbnail": "/files/US_BANKS_NDFI_MAY2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Q1 2026 update on US bank lending to non-depository financial institutions (NDFI) - the channel funding private credit. Balances hit $1.65T, up 42% in five quarters and now 14.2% of the entire bank loan book (up from 11.1%) or 7.1% of total assets (up from 5.6%). 622 banks. Top 10 hold 73% of all NDFI loans. Subcategory mix: Business Credit $435B (26%), PE Funds $383B (23%), Mortgage Credit $368B (22%), Other NDFIs $269B (16%), Consumer Credit $166B (10%). Top lenders: JPMorgan $238B, Wells Fargo $228B, Bank of America $213B, Goldman $123B, Citi $123B. Add undrawn commitments of $1.06T and total exposure hits $2.7T. The Fed, FSB, OCC, IMF, ECB, BoE and DOJ all flagging it; Fitch private-credit default rate at a record 6.0%; Bank of England calling it a 'market for lemons'. Tool live at tigzig.com/tools/us-ndfi (TREMOR) with bank-level balances, commitments, credit quality and exposure - free, no login, sourced directly from quarterly FFIEC Call Reports (Schedule RC-C). Continues the four-dispatch private-credit series (Mar 24, Mar 26, May 8, May 28).", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2060719613772718153" }, { "id": "market-tools-20-free-tigzig-markets-may2026", "slug": "market-tools-20-free-tigzig-markets-may2026", "title": "Over 20 Market Tools, Free. Built and Used for My Own Analysis. Now on tigzig.com/markets.", "date_published": "2026-05-30T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-30T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/market-tools-20-free-tigzig-markets-may2026", "thumbnail": "/images/blog/market_tools_20_free.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants" ], "summary": "Tool release - Over 20 market tools live on tigzig.com/markets, free, no login, no ads. Five categories: Macro & credit data (~290 indicators across US, India, EU and Global), Track and compare securities (price, returns, drawdown, volatility), AI technical analysis (daily + weekly, 6 sections), AI macro briefing (agent + macros + news), Risk & return metrics (80+ metrics). Six ways to connect - Web, GPT, xlwings Lite, Agent, MCP and API. Works inside Claude in Excel and PowerPoint, Claude AI, Cowork. Most are open source. Built by an analyst and ex-derivatives trader. Author uses these same tools daily for own analysis and client work - connect to the tools, pull data, run checks, get analysis out, right inside whatever Claude surface is in use. Analysis section on the site shows the kind of work these feed into - market exuberance, S&P/Nifty positioning, early warning signals, stagflation risk, consumer lending stress.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2060640323391594685" }, { "id": "banks-holding-line-lemons-off-chart-may2026", "slug": "banks-holding-line-lemons-off-chart-may2026", "title": "Banks Holding the Line.. But the 'Lemons' Sit Off the Chart. In Private Credit, Credit Unions, and Non-Bank Consumer Lenders.", "date_published": "2026-05-29T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-29T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/banks-holding-line-lemons-off-chart-may2026", "thumbnail": "/images/blog/banks_holding_line_lemons_off_chart.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "FDIC Q1 2026 NCO chart shows US banks holding the line at 0.58%, right at pre-GFC. Real Estate (45% of book) at 0.05% vs 2.50% GFC peak. Consumer at 2.87% (vs 6.64% GFC). C&I 0.52% (vs 2.69%). But the credit stress is in two places the bank NCO chart doesn't show. First: private credit, which is mark-to-market and hits P&L directly, never flowing through to NCO. Bank loans to non-bank lenders crossed ~$1.6T (FFIEC) with about a quarter being private credit. DOJ probing BlackRock fund valuations, FSB/BoE/ECB all warning on data gaps, opacity and spillover risk. Second: credit unions and non-bank consumer lenders. NCUA consumer NCO at 1.82% (nearly 2x pre-crisis), cards at 5.11% past the 4.68% GFC peak, used vehicle loans at the highest on record. Non-banks carrying the weakest paper. Single-image chart post with Q1 2026 numbers across the bank book - Total Loans 0.58%, Real Estate, Consumer, C&I segments and All Other.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2060362823180714181" }, { "id": "private-credit-market-for-lemons-may2026", "slug": "private-credit-market-for-lemons-may2026", "title": "Private Credit Default Rate Hits a Record 6.0% (2026): A Market for Lemons? Banks and Insurers Deep In It.", "date_published": "2026-05-28T10:00:00.000Z", "date_updated": "2026-06-12T10:00:00.000Z", "last_modified": "2026-05-28T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/private-credit-market-for-lemons-may2026", "thumbnail": "/files/PRIVATE_CREDIT_MAY2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Private credit's default rate hit a record 6.0% in April 2026 (Fitch, trailing 12 months). Bank of England's Sarah Breeden invokes Akerlof's 'market for lemons'; a government report calls it a layered cake like 2008 subprime. Banks and insurers are deep in - JPMorgan $22B, Citi $22B, Deutsche $30B - with a 19% off-cycle NAV writedown, a $648M bank credit-line cut, KKR rescuing its own FSK fund, and a DOJ probe into BlackRock TCPC valuations. Numbers with sources.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2059972366726570207" }, { "id": "track-trump-stock-trades-tool-release-may2026", "slug": "track-trump-stock-trades-tool-release-may2026", "title": "Track Trump's Stock Trades. Public Filing Explorer. 3,568 Trades, 986 Tickers, $220M-$750M. Searchable on TREMOR.", "date_published": "2026-05-25T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-25T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/track-trump-stock-trades-tool-release-may2026", "thumbnail": "/images/blog/track_trump_stock_trades_tool.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants" ], "summary": "Tool release - Public Filing Explorer for Trump's stock trades, live on TIGZIG TREMOR. 3,568 equity trades from the latest OGE filing (Jan-March 2026), $220M-$750M disclosed value, all searchable across 986 tickers. Per-ticker price chart with buy/sell timeline overlay and news around each trade. Trades plotted on S&P 500 day by day. Raw OGE filings standardized with name-to-ticker mapping (filings come with messy long names, no tickers). CSV download for offline cuts. For 9 months before this filing it was mostly bonds; the May filing alone was almost all equities. Educational analysis, not financial advice.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2058842593828761720" }, { "id": "india-next-tech-wave-ai-data-centers-may2026", "slug": "india-next-tech-wave-ai-data-centers-may2026", "title": "India's Next Tech Wave. AI Data Centers for the World. Adani, Reliance, Airtel, L&T. Same Pattern as Software in the 2000s.", "date_published": "2026-05-24T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-24T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/india-next-tech-wave-ai-data-centers-may2026", "thumbnail": "/files/INDIA_DATACENTER_C/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "India's next tech wave - AI data centers for the world - is being built by infra players Adani, Reliance, Bharti Airtel and L&T. World is out of compute and out of power - global data centers ~97% full, leased before they're finished. Big-4 US tech firms guided ~$700bn capex in 2026 and still can't keep up. First movers in India: Google's $15bn / 1 GW Vizag hub (largest outside US), Adani $100bn by 2035, Reliance $110bn / 7yr Jamnagar, Microsoft $17.5bn Hyderabad live mid-2026, AWS $12.7bn by 2030, TCS HyperVault. Government policy backing: 2047 tax holiday for foreign cloud firms, IndiaAI Mission, 15% safe harbour. India's edge isn't lower-cost labour - it's industrial build muscle for gigawatt-scale capacity. Risks covered: US-India relations and chip access, water and heat in stressed regions, land approvals. Includes 15-slide deck and full source links (JLL, McKinsey, CEEW, Jefferies, BIS, CNBC, Google, Microsoft, Adani, Reliance, etc.).", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2058432206004277343" }, { "id": "tremor-six-tools-banking-insurance-analysts-may2026", "slug": "tremor-six-tools-banking-insurance-analysts-may2026", "title": "TREMOR. Six Free Tools for Banking and Insurance Analysts. Multi-Layer Validated. Built by a Practitioner.", "date_published": "2026-05-22T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-22T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-six-tools-banking-insurance-analysts-may2026", "thumbnail": "/images/blog/tremor_six_tools_banking_insurance.png", "status": "published", "deck": "release", "tags": [ "portfolio-quants" ], "summary": "Six free tools for banking and insurance analysts on TREMOR (tigzig.com). Bank Aggregates from FDIC, Credit Union Aggregates from NCUA, Consumer Credit from NY Fed, Bank-Level NDFI from FFIEC, Insurance Assets from Fed Z.1, Global Macro Indicators from multiple sources. Primary sources only - no third-party aggregators. Every tool carries a three-layer Validations tab. Walks through what it took to validate the data: regulator nuances (NCUA YTD charge-offs need de-cumulation, NY Fed revises back-data, FDIC late-filers drift against QBP totals), abnormal patterns, and even an apparent error in the regulator's own filing. Built by an analyst, for analysts. Free. No ads. No login.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2057843323482898661" }, { "id": "trump-trades-3642-equity-trades-tool-may2026", "slug": "trump-trades-3642-equity-trades-tool-may2026", "title": "Trump Trades. From Mostly Bonds to 3,642 Equity Trades in One Filing. Free Tool to Search Every Transaction.", "date_published": "2026-05-21T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-21T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/trump-trades-3642-equity-trades-tool-may2026", "thumbnail": "/files/TRUMP_TRADES_MAY2026_C/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "9 months of mostly bonds in President Trump's OGE filings broken by one filing on 14 May 2026 carrying 3,642 equity trades, $220M to $750M. Heavy activity in Nvidia, Apple, Microsoft, Amazon, Meta, Oracle. 15 filings since Aug 2025 - earlier 14 combined had 1,726 trades almost all bonds. Built a free interactive tool on top of the OGE PDFs - every transaction searchable across 1,058 tickers, click any company to see trade timeline overlaid on price chart, CSV download. Short analysis deck included on most active tickers, top buys, top sells, and policy overlaps press has flagged.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2057407911123705982" }, { "id": "three-red-flags-30yr-ppi-oil-may2026", "slug": "three-red-flags-30yr-ppi-oil-may2026", "title": "Three Red Flags Hit Together. 30Y at 5.14%, PPI at 5.99%, Brent Above $100. Setup Worse Than 2000 or 2007.", "date_published": "2026-05-20T10:00:00.000Z", "date_updated": "2026-05-20T15:30:00.000Z", "last_modified": "2026-05-20T15:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/three-red-flags-30yr-ppi-oil-may2026", "thumbnail": "/images/blog/three_red_flags_30yr.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "Three macro red flags hit at the same time, each a major disruptor on its own: US 30-year Treasury yield 5.14% (first since 2007), US Producer prices 5.99% (15-year high ex-COVID), Brent above $100 (IEA calls it the largest oil supply disruption in history). Alongside: S&P 7,400 with CAPE 39.6 (2nd-highest in 145 years), auto loan losses past 2007 GFC peak, unemployment near Q3 2007 levels, Mag 7 ~35% of S&P (NVIDIA alone 8.9%). The last time these general patterns showed up: 2000 and 2007. 2026 is looking worse.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2057040507436679233" }, { "id": "mfs-collapse-cockroaches-private-credit-may2026", "slug": "mfs-collapse-cockroaches-private-credit-may2026", "title": "Dimon Was Right. More Cockroaches Arrive. UK Lender MFS Collapses - $2.5B Across 12+ Global Banks.", "date_published": "2026-05-18T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-18T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfs-collapse-cockroaches-private-credit-may2026", "thumbnail": "/images/blog/mfs_collapse_cockroaches.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "UK lender MFS collapsed in February with $2.5B exposure across 12+ global banks - Barclays $308M, HSBC $400M, Atlas/Apollo $508M, Santander $267M, Elliott $254M, Wells Fargo $182M. £2.4B loan book, fraud and double-pledging allegations. £230M verified collateral against £1.16B owed (80% shortfall). Affiliated lending chain: HSBC > Apollo Atlas > Zircon Bridging > MFS.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2056382967052910793" }, { "id": "sp500-irrational-exuberance-7400-may2026", "slug": "sp500-irrational-exuberance-7400-may2026", "title": "S&P at 7,400. Irrational Exuberance. The Last Two Times This Setup Held, the Index Halved.", "date_published": "2026-05-17T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-17T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sp500-irrational-exuberance-7400-may2026", "thumbnail": "/files/IRRATIONAL_EXUBERANCE_MAY2026/Slide1.PNG", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "S&P 500 at 7,400 with Irrational Exuberance. Last two times setup held: 2000 dot com -49% in 30 months, 2007 subprime -57% in 17 months. Apply pattern math, S&P lands 3,200-3,800. CAPE 39.6 (2nd time in 145 years), auto delinquencies past GFC peak, U-6 back to pre-GFC, PPI 25-year high ex-COVID, Brent $106. Dimon: too much exuberance. Buffett on $397B cash.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2055907701436399696" }, { "id": "us-consumer-lending-crisis-without-recession-may2026", "slug": "us-consumer-lending-crisis-without-recession-may2026", "title": "US Consumer Lending Is Already at Crisis-Level Numbers - Without a Recession. NY Fed Q1 2026 Data.", "date_published": "2026-05-15T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-15T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-consumer-lending-crisis-without-recession-may2026", "thumbnail": "/files/US_CONSUMER_LENDING_CRISIS_MAY2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "NY Fed Q1 2026 release. Auto delinquencies past GFC peak. Cards near it. Mortgage stock calm but flow rate cracking. HELOC usage and delinquencies rising. Home prices down 9% from 2022 peak. U-6 unemployment back to pre-GFC 2007 levels. Underwater mortgages up in 45 states YoY. Crisis numbers without a recession.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2054919304446157092", "deck": "analysis" }, { "id": "fed-fsr-may2026-stress-points-match-analysis", "slug": "fed-fsr-may2026-stress-points-match-analysis", "title": "US Fed Financial Stability Report May 2026. Same Stress Points - Now in Fed Own Survey.", "date_published": "2026-05-09T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-09T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/fed-fsr-may2026-stress-points-match-analysis", "thumbnail": "/images/blog/usfedmay2026.png", "status": "published", "deck": "analysis", "tags": [ "portfolio-quants" ], "summary": "US Fed FSR May 2026 covers 4 of the top 6 shocks already in tigzig.com/analysis - private credit (life insurance exposure, bank lending to non-banks), AI impact (labor displacement), oil shock (spot-futures divergence), risk asset correction (S&P disbelief rally). Plus consumer/auto lending stress and stagflation-lite.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2053437897307808241" }, { "id": "us-life-insurance-red-flag-private-credit-offshore", "slug": "us-life-insurance-red-flag-private-credit-offshore", "title": "Red Flag in US Life Insurance. $2.4T (7.5% of GDP) in Private Credit and Offshore Reinsurance.", "date_published": "2026-05-08T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-08T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-life-insurance-red-flag-private-credit-offshore", "thumbnail": "/files/US_INSURANCE_RED_FLAG_MAY2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "US Life Insurers have $2.4T (7.5% of US GDP) in private credit and offshore reinsurance. $1.2T in Fed Unidentified Misc Assets, $1.2T offshore reinsurance memo. Both nearly doubled in 5 years. PE-affiliated loop - same firms own insurers, credit funds and offshore reinsurers. Doom loop risk if retirees surrender annuities en masse.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2053041495008293190", "deck": "analysis" }, { "id": "us-insurance-assets-tracker", "slug": "us-insurance-assets-tracker", "title": "US Insurance Assets Tracker - 25 Years of Data, 73 Series, Life & P&C", "date_published": "2026-05-06T10:00:00.000Z", "date_updated": null, "last_modified": "2026-05-06T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-insurance-assets-tracker", "thumbnail": "/images/blog/usInsurance.png", "status": "published", "tags": [ "portfolio-quants" ], "summary": "US Insurance Assets Tracker on TREMOR. 73 line items across 104 quarters / 25 years. Life and P&C, General and Separate Account. Source FRED + Fed Z.1, validated against L.116 and NAIC/ACLI. Life Unidentified Misc Assets grew 76% in 5 years to $1.2T - now 10% of Life assets, the Fed catch-all for private credit, PE, BDCs, hedge funds.", "twitter_status": "posted", "markdown_file": "output/markdown-manually-fixed/us-insurance-assets-tracker.md", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2052002878559297711", "deck": "release" }, { "id": "releasing-brsr-analytics-india-esg-workforce-wages", "slug": "releasing-brsr-analytics-india-esg-workforce-wages", "title": "Releasing BRSR Analytics Tool. For Tracking Indian Corporate ESG, Workforce, Wages, Complaints.", "date_published": "2026-04-30T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-30T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/releasing-brsr-analytics-india-esg-workforce-wages", "thumbnail": "/images/blog/brsr_poshcompany.png", "status": "published", "tags": [ "vigil" ], "summary": "BRSR Analytics Tool released on VIGIL. SEBI BRSR XBRL data for FY 2024-25 covering 712 Nifty Total Market companies. ~1.25 crore workforce. Over Rs.8 lakh crore wages disclosed. 8 indices, 22 sectors. Aggregate views, company drilldowns, benchmarking, XBRL viewer, data explorer, CSV download, separate DQ tab for data quality issues.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2050477520362119305", "deck": "release" }, { "id": "convergence-risk-credit-stress-past-2007", "slug": "convergence-risk-credit-stress-past-2007", "title": "The Convergence Risk. Credit Stress Already Past 2007 Pre-Crisis. Five Aggravators Stacking On Top.", "date_published": "2026-04-29T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-29T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/convergence-risk-credit-stress-past-2007", "thumbnail": "/files/CONVERGENCE_RISK_29APR2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "Multiple credit stress signals at the same time across US banks, credit unions, consumer debt. Past 2007 pre-crisis on most metrics. Auto loans already at GFC peak. 5 aggravators on top - AI labor shock, U-1 above 2007, GDP at 0.5%, Brent $100+, private credit stress with $2.5T+ exposure. Warnings from Jamie Dimon and Sarah Breeden.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2049460063455379749", "deck": "analysis" }, { "id": "us-credit-unions-ncua-tremor-card-nco-past-gfc", "slug": "us-credit-unions-ncua-tremor-card-nco-past-gfc", "title": "US Credit Unions on TREMOR. Card Charge-Offs at 5.11% - Already Past the 2009 GFC Peak.", "date_published": "2026-04-25T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-25T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-credit-unions-ncua-tremor-card-nco-past-gfc", "thumbnail": "/images/blog/cu_cc.png", "status": "published", "tags": [ "portfolio-quants" ], "summary": "US Credit Unions analytics on TREMOR. 23 years, 86 quarters, $1.72T loans across 4,287 federally-insured credit unions from NCUA quarterly FPR. Card NCO at 5.11% past 4.68% GFC peak. Consumer NCO at 1.82% vs 0.99% pre-crisis. 18 segments, 30-59 DPD, 60+ DPD, NCO. CSV download with 74-field reference.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2049026618925121669", "deck": "analysis" }, { "id": "auto-loans-crisis-2026-subprime-parallel", "slug": "auto-loans-crisis-2026-subprime-parallel", "title": "The Crisis in Auto Loans. 2007 was Sub-Prime Mortgage. Is 2026 Auto?", "date_published": "2026-04-22T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-22T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/auto-loans-crisis-2026-subprime-parallel", "thumbnail": "/files/AUTO_LOANS_APR2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "NY Fed Auto 90+DPD at 5.21% vs 5.27% at GFC peak. Bank auto NCO at 2x 2011 baseline. Credit union NCO at 0.95% record high. S&P ABS subprime NCO ~10%. Tricolor fraud $800M. First Brands fraud $2.3B. $1.67T direct exposure plus $350B in ABS. FDIC SDI, NY Fed CCP, NCUA, S&P Global, FFIEC analysis.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2047644192504164730", "deck": "analysis" }, { "id": "releasing-us-credit-fdic-nyfed-interactive-tool", "slug": "releasing-us-credit-fdic-nyfed-interactive-tool", "title": "Releasing US-CREDIT. For Analysts Tracking US Banking. Interactive Tool Covering FDIC SDI and NY Fed CCP.", "date_published": "2026-04-20T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-20T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/releasing-us-credit-fdic-nyfed-interactive-tool", "thumbnail": "/images/blog/us_credit_nyf_auto.png", "status": "published", "tags": [ "portfolio-quants" ], "summary": "US-CREDIT interactive analytics tool. 25 years, 100 quarters. $13.6T bank loans across 4400+ FDIC institutions. $18.8T consumer debt via NY Fed CCP. 18 FDIC segments, delinquency buckets, charge-offs, FICO bands, age groups. Validated against FDIC QBP - 100 quarters reconciled. Includes disbelief rally research deck.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2046475958644089303", "deck": "release" }, { "id": "sp500-ath-cracking-underneath-oct2007-parallel", "slug": "sp500-ath-cracking-underneath-oct2007-parallel", "title": "S&P at All-Time High. Under the Hood, Things Are Cracking. Just Like October 2007.", "date_published": "2026-04-18T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-18T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sp500-ath-cracking-underneath-oct2007-parallel", "thumbnail": "/files/DISBELIEF_RALLY_18APR2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "S&P at ATH while private credit at 2x 2007 subprime showing stress. Credit card and auto delinquencies above 2007 pre-crisis. Bank charge-offs higher than 2007. AI displacing workers at unprecedented pace inside a stress cycle. Tracking across FFIEC, FDIC, NY Fed, IEA, Challenger, Anthropic Economic Index.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2045440167666913485", "deck": "analysis" }, { "id": "claude-code-sub-agents-performance-fix", "slug": "claude-code-sub-agents-performance-fix", "title": "Claude Code Was Getting Dumber. Stopped It from Auto-Spawning Sub-Agents. Performance Came Back Instantly.", "date_published": "2026-04-15T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-15T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-code-sub-agents-performance-fix", "thumbnail": "/images/blog/claude_code_logo.png", "status": "published", "tags": [ "ai-coders" ], "summary": "Claude Code Opus 4.6 was auto-spawning sub-agents for every task causing rework loops, sloppy work, and lost context. Sub-agents reported Done without validation. Fixed with one CLAUDE.md global rule: no agents without express authorization. Performance returned instantly. Still uses sub-agents for bulk tasks but main agent sets up process first.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2044417437060550755" }, { "id": "oil-masking-effect-spot-futures-divergence-apr2026", "slug": "oil-masking-effect-spot-futures-divergence-apr2026", "title": "The Masking Effect in Oil Markets - The Price You See on Screens Is Hiding a $50 Supply Shock", "date_published": "2026-04-12T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-12T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/oil-masking-effect-spot-futures-divergence-apr2026", "thumbnail": "/files/DATED_BRENT_12APR2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "Largest spot-futures divergence in oil market history. Brent futures at $94, physical crude at $144. Analysis of 9,864 trading days since 1987. 40 bids vs 4 offers in North Sea. Diesel near $200/bbl. US gasoline inventories at 16-year lows. Hormuz shut 40 days, Kuwait says 3-4 months to restore.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2043282869951533383", "deck": "analysis" }, { "id": "india-bank-credit-analytics-tool", "slug": "india-bank-credit-analytics-tool", "title": "Analytics Tool for India's Bank Credit", "date_published": "2026-04-10T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-10T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/india-bank-credit-analytics-tool", "thumbnail": "/images/blog/ind_credit_01.png", "status": "published", "tags": [ "portfolio-quants" ], "summary": "Interactive analytics tool for India bank credit deployment. 39 months of RBI sectoral data across 34 sectors. Growth rates, share of delta, composition shifts, sector-to-subsector drill downs, hierarchical tree or RBI format views, custom sector groups, time frame controls, CSV downloads. Includes NBFC sub-categories RBI doesn't break out separately.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2042506654542106754", "deck": "release" }, { "id": "tremor-claude-managed-agents-macro-analysis", "slug": "tremor-claude-managed-agents-macro-analysis", "title": "Anthropic Released Claude Managed Agents Yesterday, Live on TREMOR Today", "date_published": "2026-04-09T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-09T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-claude-managed-agents-macro-analysis", "thumbnail": "/images/blog/treor_ai_02.png", "status": "published", "tags": [ "portfolio-quants", "ai-coders" ], "summary": "Claude Managed Agents integrated into TREMOR macro dashboard. AI analysis on 70+ indicators across US and India. Three modes: full dashboard briefing, selected chart analysis, deep research with citations. Covers equities, bonds, gold, oil, forex, unemployment, delinquencies, valuations, inflation.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2042147416435196385", "deck": "release" }, { "id": "india-credit-deposit-ratio-gold-loans-apr2026", "slug": "india-credit-deposit-ratio-gold-loans-apr2026", "title": "India's Credit-Deposit Ratio Just Hit 83%. A 15-Year High. Gold Loans Up 4x in Two Years.", "date_published": "2026-04-08T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-08T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/india-credit-deposit-ratio-gold-loans-apr2026", "thumbnail": "/files/IND_CREDIT_APR2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "India credit-deposit ratio at 83%, 15-year high. Gold loans up 4x in two years. NBFC loans up 21% in 9 months. Six sectors growing at 2x overall rate pulling one-third of new credit. Credit grew 12.3% vs deposits 9%. Housing growth slowed. RBI sectoral data analysis.", "twitter_status": "posted", "markdown_file": "", "html_file": "", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2041839363668242716", "deck": "analysis" }, { "id": "us-bank-ndfi-interactive-analytics-tool", "slug": "us-bank-ndfi-interactive-analytics-tool", "title": "US Bank Exposure to Non-Bank Lenders - Now in an Interactive Analytics Tool", "date_published": "2026-04-04T10:00:00.000Z", "date_updated": null, "last_modified": "2026-04-04T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-bank-ndfi-interactive-analytics-tool", "thumbnail": "/images/blog/ndfi_apr4_bals.png", "status": "published", "tags": [ "portfolio-quants" ], "summary": "Interactive analytics tool for US bank exposure to non-bank lenders. 629 banks, $1.57 trillion in NDFI loans, 5 quarters of FFIEC Call Report data. CET1 concentration, ACL reserve coverage, bank-level drill downs, data explorer with 55+ columns, methodology with MDRM codes, 7 levels of validation. Full dataset download.", "twitter_status": "skip", "markdown_file": "", "html_file": "output/html-pages-manually-fixed/us-bank-ndfi-interactive-analytics-tool.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "deck": "release" }, { "id": "ndfi-analytics-us-bank-exposure-tool", "slug": "ndfi-analytics-us-bank-exposure-tool", "title": "NDFI Analytics - US Bank Exposure to Non-Bank Lenders", "date_published": "2026-03-31T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-31T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/ndfi-analytics-us-bank-exposure-tool", "thumbnail": "/files/NDFC_TOOL_MARCH2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "Interactive NDFI analytics tool covering US bank exposure to non-bank lenders. FFIEC Call Reports for 629 banks across 5 quarters. Stress scenarios, CET1 concentration, ACL coverage, nonaccrual rates, commitments. Validated against FDIC BankFind API, FRED Series, FDIC Quarterly Banking Profile. Full data download.", "twitter_status": "posted", "markdown_file": "", "html_file": "output/html-pages-manually-fixed/ndfi-analytics-us-bank-exposure-tool.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2038956456217842071", "deck": "release" }, { "id": "three-tools-market-analysis-qsuite-tremor-qrep", "slug": "three-tools-market-analysis-qsuite-tremor-qrep", "title": "Three Tools I Built for Market Analysis - QSUITE, TREMOR, QREP", "date_published": "2026-03-29T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-29T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/three-tools-market-analysis-qsuite-tremor-qrep", "thumbnail": "/files/QUANT_TOOLS/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "Three market analysis tools - QSUITE for AI technical analysis, TREMOR for macro early warning with 50+ indicators across US and India, QREP for portfolio stats powered by QuantStats. Built on Claude Code, DuckDB, Hetzner, Vercel, Coolify. Free, no login, no ads.", "twitter_status": "posted", "markdown_file": "", "html_file": "output/html-pages-manually-fixed/three-tools-market-analysis-qsuite-tremor-qrep.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2038443382482366865", "deck": "release" }, { "id": "tremor-macro-dashboard-50-indicators", "slug": "tremor-macro-dashboard-50-indicators", "title": "TREMOR - Macro Early Warning Dashboard - 50+ Indicators, Updated Daily", "date_published": "2026-03-27T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-27T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-macro-dashboard-50-indicators", "thumbnail": "/images/blog/tremor_combined.png", "status": "published", "tags": [ "portfolio-quants" ], "summary": "TREMOR macro early warning dashboard with 50+ indicators for US and India. Updated daily from FRED, RBI, NSE, MOSPI, NY Fed. Swap charts as news breaks, YoY/MoM/MA toggles, PNG export to PowerPoint, CSV export, news feed from 30+ sources. Free, no login.", "twitter_status": "skip", "markdown_file": "", "html_file": "output/html-pages-manually-fixed/tremor-macro-dashboard-50-indicators.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "deck": "release" }, { "id": "stagflation-lite-macro-signals-march-2026", "slug": "stagflation-lite-macro-signals-march-2026", "title": "Are We Headed for Stagflation-Lite", "date_published": "2026-03-28T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-28T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/stagflation-lite-macro-signals-march-2026", "thumbnail": "/files/TREMOR_28MAR_BONDS/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "Stagflation-Lite signals intensifying. Fed trapped between inflation and weak jobs. US 10Y at 4.4%, PPI at 3.4% with oil shock not yet in data, GDP at 0.7%, consumer sentiment 7th lowest in 40 years, Moody recession odds at 48.6%. Bond markets pricing in high inflation globally.", "twitter_status": "posted", "markdown_file": "", "html_file": "output/html-pages-manually-fixed/stagflation-lite-macro-signals-march-2026.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2037823469401059402", "deck": "analysis" }, { "id": "us-banks-ndfc-lending-systemic-risk-2026", "slug": "us-banks-ndfc-lending-systemic-risk-2026", "title": "US Banks & Non-Bank Lending - How Deep is the Exposure? Is there a Systemic Risk?", "date_published": "2026-03-26T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-26T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/us-banks-ndfc-lending-systemic-risk-2026", "thumbnail": "/files/TREMOR_NDFC_EXPOSURE/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "US banks hold $1.57 trillion in loans to non-bank lenders facing redemptions and defaults. 41 banks have this single portfolio exceeding 100% of core capital. 62 banks have loan loss reserves covering less than 10% of exposure. Analysis from FFIEC Call Reports for 629 banks across 5 quarters, cross-validated against FDIC, FRED and S&P Global.", "twitter_status": "posted", "markdown_file": "", "html_file": "output/html-pages-manually-fixed/us-banks-ndfc-lending-systemic-risk-2026.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2037155761105805779", "deck": "analysis" }, { "slug": "private-credit-shadow-lending-cracks-2026", "title": "Private Credit - The $2.7 Trillion Shadow Lending Market Is Showing Cracks", "date_published": "2026-03-24T10:00:00.000Z", "summary": "Private credit showing cracks that remind Wall Street of 2008. Jamie Dimon and Lloyd Blankfein flagging risks. 40% of borrowers have negative cash flow. Blackstone scrambled to meet $3.8B in redemptions. Management companies down 30-50% in 6 months.", "thumbnail": "/files/TREMOR_MAR_2026_PRIVATE_CREDIT/Slide1.PNG", "tags": [ "portfolio-quants" ], "twitter_status": "posted", "id": "private-credit-shadow-lending-cracks-2026", "status": "published", "source": "fresh", "original_url": "https://www.tigzig.com/post/private-credit-shadow-lending-cracks-2026", "date_updated": null, "last_modified": "2026-03-24T10:00:00.000Z", "has_images": true, "has_videos": false, "markdown_file": "", "html_file": "output/html-pages-manually-fixed/private-credit-shadow-lending-cracks-2026.html", "content_length": 0, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2036466233831559558", "deck": "analysis" }, { "id": "structural-weakness-intensifying-march-2026", "slug": "structural-weakness-intensifying-march-2026", "title": "Structural Weakness Intensifying", "date_published": "2026-03-20T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-20T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/structural-weakness-intensifying-march-2026", "thumbnail": "/files/TREMOR_MAR_2026_20MAR/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "3 major US macro data points in 3 days all pointing the same direction. Feb payrolls minus 92K against expectations of 59K rise. PPI Core at 3.87%. Fed held at 3.5-3.75%. Brent crude nearly doubled in 3 months. Delinquencies past COVID highs. Multiple macro signals flashing stress simultaneously.", "twitter_status": "posted", "markdown_file": "", "html_file": "output/html-pages-manually-fixed/structural-weakness-intensifying-march-2026.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2034983738602660217", "deck": "analysis" }, { "id": "claude-the-universal-agent", "slug": "claude-the-universal-agent", "title": "Claude - The Universal Agent", "date_published": "2026-03-19T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-19T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-the-universal-agent", "thumbnail": "/files/CLAUDE_CODE_UNIVERSAL_AGENT/Slide1.PNG", "status": "published", "tags": [ "ai-coders", "claude-in-excel", "mcp-servers" ], "summary": "Claude went from AI coder to universal agent in 6 months. Excel formulas, slide decks, PDF reports, MCP connectors, database queries, security scans, full-stack builds - one tool handles what used to need 5-6 separate tools. 10-slide overview covering the evolution and use cases across roles.", "twitter_status": "posted", "has_images": true, "has_videos": false, "content_length": 0, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2034608247349682320" }, { "id": "vigil-data-api-download", "slug": "vigil-data-api-download", "title": "VIGIL Data API and Download - 315K+ Records, 7 Tables, Updated Daily", "date_published": "2026-03-13T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-13T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-data-api-download", "thumbnail": "/images/blog/vigid_api_download.png", "status": "published", "tags": [ "vigil", "database-ai" ], "summary": "VIGIL full database now open for download and API access. 315K+ records across 7 tables covering related party transactions, credit ratings, insider trading, pledges, takeover, encumbrance and surveillance. Download as CSV, TSV, Parquet or SQLite. REST API with no auth required. Related party data from XBRL filings across 728 Nifty Total Market companies.", "twitter_status": "posted", "markdown_file": "output/markdown-manually-fixed/vigil-data-api-download.md", "html_file": "output/html-pages-manually-fixed/vigil-data-api-download.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2032388845069828192", "deck": "release" }, { "id": "tremor-macro-signals-march-2026", "slug": "tremor-macro-signals-march-2026", "title": "Tremor - Macro Early Warning Signals - March 2026", "date_published": "2026-03-14T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-14T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tremor-macro-signals-march-2026", "thumbnail": "/files/TREMOR_MAR_2026/Slide1.PNG", "status": "published", "tags": [ "portfolio-quants" ], "summary": "S&P down 5%, Nifty in bear territory down 12% from highs. CAPE at 40-year highs, gold going vertical, crude shooting towards 2008 levels, US delinquencies crossing COVID highs. When macro signals move together - tremors before a quake. 12 chart slides from tremor.tigzig.com.", "twitter_status": "posted", "has_images": true, "has_videos": false, "content_length": 0, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2034608302282391995", "deck": "analysis" }, { "id": "mcp-guide-claude-in-excel", "slug": "mcp-guide-claude-in-excel", "title": "MCP Guide for Claude in Excel - and any MCP client", "date_published": "2026-03-11T10:00:00.000Z", "date_updated": "2026-07-15T18:30:00.000Z", "last_modified": "2026-07-15T18:30:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mcp-guide-claude-in-excel", "thumbnail": "/images/blog/mcp_guide01.png", "status": "published", "tags": [ "mcp", "claude-in-excel", "security" ], "summary": "35-slide visual guide covering MCP (Model Context Protocol) from fundamentals to production deployment. Explains MCP vs regular APIs, schema documentation, common mistakes, and security hardening. Includes 5 live open-source MCP servers connecting to PostgreSQL, DuckDB, PDF/HTML files, and Python backends. Covers honest trade-offs around token overhead and context bloat.", "twitter_status": "posted", "markdown_file": "output/markdown-manually-fixed/mcp-guide-claude-in-excel.md", "html_file": "output/html-pages-manually-fixed/mcp-guide-claude-in-excel.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2032388891957920055" }, { "id": "mfpro-india-mutual-fund-nav-data-api", "slug": "mfpro-india-mutual-fund-nav-data-api", "title": "India Mutual Fund NAV Data Since 2013 - 19.8M+ Records. Download or Query via API. Free. No Auth.", "date_published": "2026-03-10T15:00:00.000Z", "date_updated": null, "last_modified": "2026-03-10T15:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-india-mutual-fund-nav-data-api", "thumbnail": "/images/blog/mfpro_api01.png", "status": "draft", "hidden": true, "redirect_to": "/post/free-mutual-fund-nav-api-india", "tags": [ "mutual-funds" ], "summary": "Free India mutual fund NAV data since 2013 with 19.8M+ daily records across 17,866 schemes. Download as CSV, TSV, Parquet or SQLite. REST API for querying by scheme code and date. Updated 3x daily from AMFI India. 104 tracked funds with pre-computed rolling returns. No login, no API key required.", "twitter_status": "deleted", "markdown_file": "output/markdown-manually-fixed/mfpro-india-mutual-fund-nav-data-api.md", "html_file": "output/html-pages-manually-fixed/mfpro-india-mutual-fund-nav-data-api.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2034608357261377917", "tweet_deleted": true, "deck": "release" }, { "id": "vigil-full-validation-reports-published", "slug": "vigil-full-validation-reports-published", "title": "Published Full Validation Reports for Every Dataset on VIGIL", "date_published": "2026-03-10T14:00:00.000Z", "date_updated": null, "last_modified": "2026-03-10T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-full-validation-reports-published", "thumbnail": "/images/blog/vigil_validation01.png", "status": "published", "tags": [ "vigil" ], "summary": "Full validation reports published for all 9 VIGIL datasets covering 315,000+ records. AI-assisted audit cross-referenced against NSE downloads. Covers takeovers, pledge, encumbrance, related party transactions, insider transactions, credit ratings, and surveillance. Documents source data quality issues and fixes.", "twitter_status": "posted", "markdown_file": "output/markdown-manually-fixed/vigil-full-validation-reports-published.md", "html_file": "output/html-pages-manually-fixed/vigil-full-validation-reports-published.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "tweet_id": "2034983769581789634" }, { "id": "upstash-redis-vercel-rate-limiting", "slug": "upstash-redis-vercel-rate-limiting", "title": "Upstash Redis on Vercel - The Tool I Didn't Know I Needed", "date_published": "2026-03-10T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-10T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/upstash-redis-vercel-rate-limiting", "thumbnail": "/images/blog/upstash-redis-explainer.png", "status": "published", "tags": [ "infrastructure", "security", "open-source" ], "summary": "How I use Upstash Redis on Vercel for rate limiting across 10-12 apps with a single free instance. Explains what Redis is (key-value store like a Python dictionary in RAM), why not Postgres for this, TTL auto-expiry, key prefix namespacing, and the free tier (500K commands/month, 256MB).", "markdown_file": "output/markdown-manually-fixed/upstash-redis-vercel-rate-limiting.md", "html_file": "output/html-pages-manually-fixed/upstash-redis-vercel-rate-limiting.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-10T10:00:00.000Z", "scraped_from": null, "scrape_quality": "perfect" }, "publishDate": "2026-03-10", "twitter_status": "posted", "tweet_id": "2034983796622467277" }, { "id": "full-backend-infrastructure-duckdb-production", "slug": "full-backend-infrastructure-duckdb-production", "title": "Full backend Infrastructure for DuckDB production and analytics system", "date_published": "2026-03-07T16:00:00.000Z", "date_updated": null, "last_modified": "2026-03-07T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/full-backend-infrastructure-duckdb-production", "thumbnail": "/images/blog/basekick_01.png", "status": "published", "tags": [ "duckdb", "open-source", "infrastructure" ], "summary": "Arc by Basekick Labs - a full backend infrastructure for DuckDB written in Go. REST API, auth, multiple storage backends (S3/MinIO/Azure), compaction, partitioning, retention policies, connection pooling, Prometheus metrics. Open source AGPL-3.0.", "markdown_file": "output/markdown-manually-fixed/full-backend-infrastructure-duckdb-production.md", "html_file": "output/html-pages-manually-fixed/full-backend-infrastructure-duckdb-production.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-07T16:00:00.000Z", "scraped_from": null, "scrape_quality": "perfect" }, "publishDate": "2026-03-07", "twitter_status": "posted", "tweet_id": "2032388938892276057" }, { "id": "qrep-mcp-quants-numbers-claude-chatgpt-excel", "slug": "qrep-mcp-quants-numbers-claude-chatgpt-excel", "title": "Pull your quants numbers directly with Claude / ChatGPT in Excel (with MCP).", "date_published": "2026-03-07T14:00:00.000Z", "date_updated": null, "last_modified": "2026-03-07T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/qrep-mcp-quants-numbers-claude-chatgpt-excel", "thumbnail": "/images/blog/qrep_mcp.png", "status": "published", "tags": [ "mcp", "quantstats", "excel", "open-source" ], "summary": "QREP MCP server for pulling 81 quant metrics (returns, sharpe, sortino, drawdowns) directly into Claude in Excel, ChatGPT in Excel, Claude Desktop, Cursor, n8n or any MCP client. Powered by QuantStats library. Free public server, full source code open.", "markdown_file": "output/markdown-manually-fixed/qrep-mcp-quants-numbers-claude-chatgpt-excel.md", "html_file": "output/html-pages-manually-fixed/qrep-mcp-quants-numbers-claude-chatgpt-excel.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-07T14:00:00.000Z", "scraped_from": null, "scrape_quality": "perfect" }, "publishDate": "2026-03-07", "twitter_status": "posted", "tweet_id": "2032388986673766827" }, { "id": "evaluating-a-stock-pull-its-vigil-report", "slug": "evaluating-a-stock-pull-its-vigil-report", "title": "Evaluating a stock? Pull its VIGIL Report. Free.", "date_published": "2026-03-07T12:00:00.000Z", "date_updated": null, "last_modified": "2026-03-07T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/evaluating-a-stock-pull-its-vigil-report", "thumbnail": "/images/blog/vigil_report_01.png", "status": "published", "tags": [ "vigil", "open-source" ], "summary": "VIGIL Corporate Signals report for any Indian listed stock. Surveillance, insider trading, takeover, credit ratings, pledges, related party transactions. Five download formats including AI-friendly TSV and SQLite. Free, no login.", "markdown_file": "output/markdown-manually-fixed/evaluating-a-stock-pull-its-vigil-report.md", "html_file": "output/html-pages-manually-fixed/evaluating-a-stock-pull-its-vigil-report.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-07T12:00:00.000Z", "scraped_from": null, "scrape_quality": "perfect" }, "publishDate": "2026-03-07", "twitter_status": "posted", "tweet_id": "2034608408524169683" }, { "id": "your-api-key-is-visible-in-the-browser", "slug": "your-api-key-is-visible-in-the-browser", "title": "Your API Key Is Visible in the Browser. Even if you put it as Vercel's 'secret' backend env variable.", "date_published": "2026-03-07T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-07T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/your-api-key-is-visible-in-the-browser", "thumbnail": "/images/blog/vercel_serverless_power.svg", "status": "published", "tags": [ "security", "infrastructure", "fastapi" ], "summary": "Why putting API keys in Vercel secret env variables still exposes them in the browser network tab. Walks through three common mistakes and the one fix that works - Vercel serverless functions. Also covers rate limiting, data processing, and security gates you can add in the serverless layer.", "markdown_file": "output/markdown-manually-fixed/your-api-key-is-visible-in-the-browser.md", "html_file": "output/html-pages-manually-fixed/your-api-key-is-visible-in-the-browser.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-07T10:00:00.000Z", "scraped_from": null, "scrape_quality": "perfect" }, "publishDate": "2026-03-07", "twitter_status": "posted", "tweet_id": "2030279805544083903" }, { "id": "qsuite-nifty-sp500-technical-analysis-llm-comparison", "slug": "qsuite-nifty-sp500-technical-analysis-llm-comparison", "title": "Nifty and S&P 500 Down - Run a Technical Analysis Check. 9 LLMs Compared.", "date_published": "2026-03-06T16:00:00.000Z", "date_updated": null, "last_modified": "2026-03-06T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/qsuite-nifty-sp500-technical-analysis-llm-comparison", "thumbnail": "/images/blog/qsuite_02.png", "status": "published", "tags": [ "technical-analysis" ], "summary": "QSUITE AI technical analysis tool with 9 LLM choices including GPT 5.4 and Claude Sonnet 4.6. Generates daily and weekly charts, support/resistance, MACD, RSI, Bollinger Bands, volume analysis, price outlook in PDF and HTML. Comparison of all 9 models on S&P 500 and Nifty 50 for the same period.", "markdown_file": "output/markdown-manually-fixed/qsuite-nifty-sp500-technical-analysis-llm-comparison.md", "html_file": "output/html-pages-manually-fixed/qsuite-nifty-sp500-technical-analysis-llm-comparison.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-06T16:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "posted", "tweet_id": "2030279862225907922" }, { "id": "related-party-transactions-vigil", "slug": "related-party-transactions-vigil", "title": "Related Party Transactions - Now Live on VIGIL", "date_published": "2026-03-06T14:00:00.000Z", "date_updated": null, "last_modified": "2026-03-06T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/related-party-transactions-vigil", "thumbnail": "/images/blog/relatedParty01.png", "status": "published", "tags": [ "vigil" ], "summary": "Related party transactions data now live on VIGIL. 250,000 transactions across 728 companies from SEBI LODR Reg 23 half-yearly XBRL filings. Covers Nifty Total Market universe. Text parsing groups free-text relationship fields into 12 standardized categories. Filter by transaction type, relationship group, company, index. CSV export available.", "markdown_file": "output/markdown-manually-fixed/related-party-transactions-vigil.md", "html_file": "output/html-pages-manually-fixed/related-party-transactions-vigil.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-06T14:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "posted", "tweet_id": "2030279918551285841" }, { "id": "are-you-rate-limiting-the-wrong-ips", "slug": "are-you-rate-limiting-the-wrong-ips", "title": "Are You Rate Limiting the Wrong IPs? A SlowAPI Story.", "date_published": "2026-03-06T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-06T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/are-you-rate-limiting-the-wrong-ips", "thumbnail": "/images/blog/rate_limit_wrong_ip.svg", "status": "published", "tags": [ "security", "fastapi", "cloudflare", "infrastructure" ], "summary": "How modern multi-hop architectures (Vercel serverless + Cloudflare + FastAPI) cause SlowAPI to rate limit the wrong IPs. Covers the CF-Connecting-IP overwrite problem, X-Forwarded-For spoofing, the custom header fix, and a detailed FAQ on IP extraction across different proxy setups (Caddy, nginx, Docker, direct).", "markdown_file": "output/markdown-manually-fixed/are-you-rate-limiting-the-wrong-ips.md", "html_file": "output/html-pages-manually-fixed/are-you-rate-limiting-the-wrong-ips.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-06T10:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "posted", "tweet_id": "2029869883459260900" }, { "id": "rolling-returns-why-cagr-alone-can-mislead-you", "slug": "rolling-returns-why-cagr-alone-can-mislead-you", "title": "Rolling Returns: Why CAGR Alone Can Mislead You (And What To Use Instead)", "date_published": "2026-03-05T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-05T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/rolling-returns-why-cagr-alone-can-mislead-you", "thumbnail": "/images/blog/rolling_01.png", "status": "published", "tags": [ "mutual-funds", "portfolio-analytics", "duckdb" ], "summary": "Explains why point-to-point CAGR is fragile and how rolling returns provide a more reliable picture of fund performance. Covers the difference between rolling window and evaluation period, the ASOF JOIN computation in DuckDB SQL, minimum gap thresholds, CAGR vs absolute return for sub-1-year windows, and how to read each column (average, median, min, max, % negative, observations). Includes validation results across 7,485 data points within 0.50 bps of Excel. Live on MFPRO with 95 funds and 9 indices.", "markdown_file": "output/markdown-manually-fixed/rolling-returns-why-cagr-alone-can-mislead-you.md", "html_file": "output/html-pages-manually-fixed/rolling-returns-why-cagr-alone-can-mislead-you.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-05T10:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "posted", "tweet_id": "2029869828241268872" }, { "id": "claude-the-hunter-killer-pen-test", "slug": "claude-the-hunter-killer-pen-test", "title": "Claude the Hunter-Killer - Have You Seen Your Nice Little Claude Run a Penetration Test on Your Apps?", "date_published": "2026-03-04T12:00:00.000Z", "date_updated": null, "last_modified": "2026-03-04T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-the-hunter-killer-pen-test", "thumbnail": "/images/blog/claude_code_logo.png", "status": "published", "tags": [ "security", "ai-coders", "infrastructure" ], "summary": "Real-world penetration test using Claude Code against a hardened DuckDB dashboard app (230M rows, IMDB data). Despite API keys, Cloudflare edge rate limiting, JS challenge, SQL blocklist and backend rate limits, Claude found repeat() memory bombs that finish within timeout, metadata leaks, and missing conn.interrupt() leaving DuckDB crunching after timeout. Shows how Playwright bypasses JS challenge using real Chrome and fires attacks from same-origin context. Practical lesson: use separate Claude instances for coding and pen testing.", "markdown_file": "output/markdown-manually-fixed/claude-the-hunter-killer-pen-test.md", "html_file": "output/html-pages-manually-fixed/claude-the-hunter-killer-pen-test.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-04T12:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "posted", "tweet_id": "2029869755843366984" }, { "id": "sp500-drawdown-qrep-analysis", "slug": "sp500-drawdown-qrep-analysis", "title": "S&P 500 Drawdown Analysis with QREP", "date_published": "2026-03-04T11:00:00.000Z", "date_updated": null, "last_modified": "2026-03-04T11:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sp500-drawdown-qrep-analysis", "thumbnail": "/images/blog/sp500_drawdown_qrep.png", "status": "published", "tags": [ "portfolio-analytics", "technical-analysis" ], "summary": "S&P 500 drawdown analysis using QREP - Security Analytics Reports tool. How many drawdowns in 20 years, how long, how deep. One-click drawdown charts and metrics powered by QuantStats. Also covers interactive technical analysis charts, performance and risk KPIs, advanced ratios, and multi-security comparison up to 6 securities.", "markdown_file": "output/markdown-manually-fixed/sp500-drawdown-qrep-analysis.md", "html_file": "output/html-pages-manually-fixed/sp500-drawdown-qrep-analysis.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-04T11:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "posted", "tweet_id": "2029145714031567231" }, { "id": "claude-in-excel-mcp-xlwings-lite-claude-code-combining-4-tools", "slug": "claude-in-excel-mcp-xlwings-lite-claude-code-combining-4-tools", "title": "Claude in Excel + MCP + xlwings Lite + Claude Code: Pull Data, Build a Model, Validate It.", "date_published": "2026-03-04T10:00:00.000Z", "date_updated": "2026-06-12T10:00:00.000Z", "last_modified": "2026-03-04T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-in-excel-mcp-xlwings-lite-claude-code-combining-4-tools", "thumbnail": "/images/blog/claude_4tools.png", "status": "published", "tags": [ "claude-in-excel", "mcp", "xlwings-lite", "ai-coders", "portfolio-analytics" ], "summary": "Live walkthrough of S&P 500 forward returns scenario model built with four tools. Claude in Excel pulls data via YFIN MCP server and builds MAP/LAMBDA formula model. Claude Code validates offline with independent Python recomputation - catches a formula error that in-Excel checks missed. xlwings Lite generates distribution charts (fan, ridgeline, raincloud) from Python into Excel. Covers when to use each tool and MCP architecture for client work.", "markdown_file": "output/markdown-manually-fixed/claude-in-excel-mcp-xlwings-lite-claude-code-combining-4-tools.md", "html_file": "output/html-pages-manually-fixed/claude-in-excel-mcp-xlwings-lite-claude-code-combining-4-tools.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-04T10:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "posted", "tweet_id": "2029067544922079301" }, { "id": "perimeter-security-cloudflare-free-plan", "slug": "perimeter-security-cloudflare-free-plan", "title": "2026 Infra Guide for AI Tool Builders - Part 5: Perimeter Security on Cloudflare (Free Plan)", "date_published": "2026-03-03T10:00:00.000Z", "date_updated": "2026-06-25T10:00:00.000Z", "last_modified": "2026-03-03T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/perimeter-security-cloudflare-free-plan", "thumbnail": "/images/blog/edgeScurity_01.png", "status": "published", "tags": [ "cloudflare", "security", "infrastructure" ], "summary": "Set up perimeter security (edge defense) for web apps on Cloudflare's free plan. Covers orange-cloud proxying, WAF JS challenges (and their impact on AI agent traffic), browser integrity checks, per-domain rate limiting via Cloudflare Workers (100K free invocations/day), zone-level IP blocking with CIDR notation, and Vercel .vercel.app bypass fix. All running across 60+ subdomains and 40+ apps at zero cost.", "markdown_file": "output/markdown-manually-fixed/perimeter-security-cloudflare-free-plan.md", "html_file": "output/html-pages-manually-fixed/perimeter-security-cloudflare-free-plan.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-03T10:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "posted", "tweet_id": "2028854972373426193" }, { "id": "mfpro-v2-mutual-fund-analytics-rolling-returns", "slug": "mfpro-v2-mutual-fund-analytics-rolling-returns", "title": "Now live - MFPRO v2 - Mutual Fund Analytics (India) - now with rolling returns, custom eval periods, multi-period & multi-instrument comparisons", "date_published": "2026-03-03T10:00:00.000Z", "date_updated": null, "last_modified": "2026-03-03T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mfpro-v2-mutual-fund-analytics-rolling-returns", "thumbnail": "/images/blog/mfpro_rolling.png", "status": "published", "tags": [ "mutual-funds", "portfolio-analytics" ], "summary": "MFPRO v2 release for Indian mutual fund analytics. Adds rolling return statistics (1Y/3Y/5Y windows with average, median, min, max), custom evaluation periods, multi-fund and multi-index return comparisons across 95 funds and 9 indices. Includes downloadable NAV data, Excel templates for independent verification, multi-period holdings comparison, and index constituent analysis. Data from Jan 2013, auto-synced twice daily.", "markdown_file": "output/markdown-manually-fixed/mfpro-v2-mutual-fund-analytics-rolling-returns.md", "html_file": "output/html-pages-manually-fixed/mfpro-v2-mutual-fund-analytics-rolling-returns.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-03-03T10:00:00.000Z", "scraped_from": null, "round": null }, "twitter_status": "deleted", "tweet_id": "2028845744330047544", "tweet_deleted": true, "deck": "release" }, { "id": "talk-to-your-database-from-excel-mcp-part-2", "slug": "talk-to-your-database-from-excel-mcp-part-2", "title": "Talk to Your Database from Excel via Claude & MCP - Part 2", "date_published": "2026-02-28T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-28T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/talk-to-your-database-from-excel-mcp-part-2", "thumbnail": "/images/blog/claudeExcelMCP_DB_part2.png", "status": "published", "tags": [ "claude-in-excel", "mcp" ], "markdown_file": "output/markdown-manually-fixed/talk-to-your-database-from-excel-mcp-part-2.md", "html_file": "output/html-pages-manually-fixed/talk-to-your-database-from-excel-mcp-part-2.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-28T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2027647853892669632", "summary": "Part 2 of connecting Excel to databases via Claude MCP. Two new server options: open public server hardened with 23 defense layers (rate limits, concurrency caps, SQL validation) and OAuth-secured server via Auth0 with JWT validation for client sharing. Full source code open as single Python file. Includes standard MCP security advice and link to 80+ item security checklist." }, { "id": "cloudflare-rate-limiting-free-plan-tricky", "slug": "cloudflare-rate-limiting-free-plan-tricky", "title": "Per-IP rate limit on Cloudflare free plan - the gotchas", "date_published": "2026-02-27T18:00:00.000Z", "date_updated": null, "last_modified": "2026-02-27T18:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cloudflare-rate-limiting-free-plan-tricky", "thumbnail": "/images/blog/security_02.png", "status": "published", "tags": [ "security", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/cloudflare-rate-limiting-free-plan-tricky.md", "html_file": "output/html-pages-manually-fixed/cloudflare-rate-limiting-free-plan-tricky.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-27T18:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2027343867637534831", "summary": "Cloudflare free plan rate limiting challenges for multi-app setups. Vercel's .vercel.app URL bypasses Cloudflare - fix with deployment protection. Free plan allows only 1 rate limit rule, Pro plan gives 2. Workarounds for 60+ subdomains across 40+ apps using Cloudflare Workers. Key takeaway: for single domains, Cloudflare free tier protection is essential. Security checklist updated to 80 items." }, { "id": "vigil-encumbrance-events-india", "slug": "vigil-encumbrance-events-india", "title": "Is your company's promoter pledging shares to raise money? Are lenders releasing the pledge - or invoking and taking control?", "date_published": "2026-02-27T14:00:00.000Z", "date_updated": null, "last_modified": "2026-02-27T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-encumbrance-events-india", "thumbnail": "/images/blog/encumbrance_01.png", "status": "published", "tags": [ "vigil" ], "markdown_file": "output/markdown-manually-fixed/vigil-encumbrance-events-india.md", "html_file": "output/html-pages-manually-fixed/vigil-encumbrance-events-india.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-27T14:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2027329645100913066", "summary": "VIGIL now tracks encumbrance events - promoter share pledges, releases, and lender invocations filed under SEBI SAST Reg 31/32. Three event types: Creation (pledge as collateral), Release (pledge removed, loan repaid), Invocation (lender seized shares, red flag). 1,400+ events tracked with presets and filters by company or Nifty indices. Sits alongside insider trading, takeover filings, credit ratings on company pages." }, { "id": "going-beyond-google-login-hardening-entry-points", "slug": "going-beyond-google-login-hardening-entry-points", "title": "Going beyond Google Login for critical apps. Identifying gaps & hardening your entry points.", "date_published": "2026-02-27T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-27T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/going-beyond-google-login-hardening-entry-points", "thumbnail": "/images/blog/tigzigCommand.png", "status": "published", "tags": [ "security", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/going-beyond-google-login-hardening-entry-points.md", "html_file": "output/html-pages-manually-fixed/going-beyond-google-login-hardening-entry-points.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-27T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "skip", "summary": "Security hardening beyond OAuth for admin apps on public internet. Two Claude instances ran 130 adversarial tests across 3 phases against a production monitoring dashboard. Implemented layered gates: Cloudflare Turnstile bot detection, pre-login password gate, Google OAuth with email whitelist, Google Authenticator MFA, and JWT verification on every API call. Security checklist updated to 78 items." }, { "id": "tigzig-ai-agent-first-site", "slug": "tigzig-ai-agent-first-site", "title": "tigzig.com is AI-agent first. But what happens when your AI coder runs into a problem on my site?", "date_published": "2026-02-26T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-26T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-ai-agent-first-site", "thumbnail": "/images/blog/tigzigCommand_feedback.png", "status": "published", "tags": [ "ai-coders", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/tigzig-ai-agent-first-site.md", "html_file": "output/html-pages-manually-fixed/tigzig-ai-agent-first-site.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-26T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2026984167658180993", "summary": "TigZig is now an AI-agent-first platform. AI coders and agents can access 40+ live tools, 155+ guides, and all source codes through structured text indexes built on the llms.txt standard. Includes an AI feedback API endpoint for agents to report broken links or missing content, with automated triage, resolution tracking, and email notifications. The entire site content (20,000+ lines) is downloadable as a single text file." }, { "id": "tigzig-ai-agent-first", "slug": "tigzig-ai-agent-first", "title": "TigZig is Now AI-Agent First", "date_published": "2026-02-25T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-25T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/tigzig-ai-agent-first", "thumbnail": "/images/blog/tigzigAIFirst.png", "status": "published", "tags": [ "ai-coders", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/tigzig-ai-agent-first.md", "html_file": "output/html-pages-manually-fixed/tigzig-ai-agent-first.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-25T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2026984110980506082", "summary": "TigZig is now AI-agent first. AI coders and agents are first-class citizens with access to 40+ live tools, 155+ guides, and all source codes indexed and structured for agents. Users can ask their AI coder to scan the site, find apps, explain implementations, and deploy solutions. Built using the llms.txt standard with the entire site content (20,000+ lines) downloadable as a single text file." }, { "id": "security-checklist-web-apps-71-items", "slug": "security-checklist-web-apps-71-items", "title": "Security Checklist for Web Apps - 71 Items", "date_published": "2026-02-25T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-25T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/security-checklist-web-apps-71-items", "thumbnail": "/images/blog/security_01.png", "status": "published", "tags": [ "security", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/security-checklist-web-apps-71-items.md", "html_file": "output/html-pages-manually-fixed/security-checklist-web-apps-71-items.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-25T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2026984056265818345", "summary": "Practical security checklist of 71 items across React frontend, FastAPI backend, PostgreSQL, DuckDB, Cloudflare, MCP servers, authentication, and VPS hardening. Each item describes the risk in plain English with a code fix. Built from real vulnerabilities found in 30+ public apps after a bot attack. Designed for data scientists and analysts building production tools who may lack traditional IT security background." }, { "id": "qrep-quantstats-security-analytics-live", "slug": "qrep-quantstats-security-analytics-live", "title": "QRep - Powered by QuantStats. Live Now.", "date_published": "2026-02-24T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-24T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/qrep-quantstats-security-analytics-live", "thumbnail": "/images/blog/qrep_01.png", "status": "published", "tags": [ "portfolio-analytics", "technical-analysis" ], "markdown_file": "output/markdown-manually-fixed/qrep-quantstats-security-analytics-live.md", "html_file": "output/html-pages-manually-fixed/qrep-quantstats-security-analytics-live.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-24T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2026248405413147044", "summary": "Launch announcement for QRep, a security analytics tool built on Ran Aroussi's QuantStats and yfinance libraries. Provides 90+ KPIs across risk, returns, ratios and drawdown metrics. Features HTML and PDF reports, CSV downloads, interactive technical analysis charts with adjustable parameters, and instant symbol search without needing Yahoo Finance ticker lookup. Part of Tigzig Analyst suite.", "deck": "release" }, { "id": "mcp-server-bot-attack-security-lessons", "slug": "mcp-server-bot-attack-security-lessons", "title": "My Public MCP Server Got Hammered - Security Lessons from a Bot Attack", "date_published": "2026-02-23T16:00:00.000Z", "date_updated": null, "last_modified": "2026-02-23T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mcp-server-bot-attack-security-lessons", "thumbnail": "", "status": "published", "tags": [ "infrastructure", "security", "mcp" ], "markdown_file": "output/markdown-manually-fixed/mcp-server-bot-attack-security-lessons.md", "html_file": "output/html-pages-manually-fixed/mcp-server-bot-attack-security-lessons.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-23T16:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "skip", "summary": "Real incident report of a bot attack on a public database MCP server. Had rate limiting, read-only access, and SQL validation but still got hammered. Worked with Claude Code to fix 15+ attack vectors including exposed server IPs, open system catalogs, and an unprotected Supabase REST API. Covers lessons on securing public demo apps vs client apps, and why ignoring AI coder security audit recommendations has consequences." }, { "id": "vigil-sast-takeover-disclosures-india", "slug": "vigil-sast-takeover-disclosures-india", "title": "New on VIGIL: SAST Takeover Disclosures (India)", "date_published": "2026-02-23T14:00:00.000Z", "date_updated": null, "last_modified": "2026-02-23T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-sast-takeover-disclosures-india", "thumbnail": "/images/blog/vigil_takeover_01.png", "status": "published", "tags": [ "vigil", "security" ], "markdown_file": "output/markdown-manually-fixed/vigil-sast-takeover-disclosures-india.md", "html_file": "output/html-pages-manually-fixed/vigil-sast-takeover-disclosures-india.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-23T14:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2026248469661503634", "summary": "VIGIL app now tracks SEBI Takeover Code (SAST) disclosures under Reg 29. Covers Reg 29(1) filings when someone crosses 5% ownership and Reg 29(2) when existing 5%+ holders change stake by 2%+. Around 10,000 records from last 2 years. Includes leaderboards for largest acquisitions, promoter selling, outsider accumulation, new 5%+ stakes. Filters by company, transaction type, promoter/non-promoter, Nifty indices. Updated daily." }, { "id": "vigil-rating-red-flags-india", "slug": "vigil-rating-red-flags-india", "title": "New feature on VIGIL: Rating Red Flags (India)", "date_published": "2026-02-21T14:00:00.000Z", "date_updated": null, "last_modified": "2026-02-21T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-rating-red-flags-india", "thumbnail": "/images/blog/vigilRedFlag.png", "status": "published", "tags": [ "vigil" ], "markdown_file": "output/markdown-manually-fixed/vigil-rating-red-flags-india.md", "html_file": "output/html-pages-manually-fixed/vigil-rating-red-flags-india.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-21T14:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2025156809145954796", "summary": "VIGIL app feature that classifies every credit rating filing in India into nine red flag categories: downgrades, defaults, negative outlooks, watchlists, and speculative grades. Analyzes action, outlook, and credit grade fields from CRISIL, ICRA, CARE, India Ratings, ACUITE, Infomerics, and Brickwork filings since Jan 2024. Filters available on Credit Ratings, Nifty 500 grid, and company pages. Covers 10,000+ rating records." }, { "id": "xlwings-lite-local-file-access-8-patterns", "slug": "xlwings-lite-local-file-access-8-patterns", "title": "xlwings Lite Local File Access: 8 Patterns You Can Use Today", "date_published": "2026-02-21T12:00:00.000Z", "date_updated": null, "last_modified": "2026-02-21T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/xlwings-lite-local-file-access-8-patterns", "thumbnail": "/images/blog/xlwingsLiteLocalFileAccess.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/markdown-manually-fixed/xlwings-lite-local-file-access-8-patterns.md", "html_file": "output/html-pages-manually-fixed/xlwings-lite-local-file-access-8-patterns.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-21T12:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2025158122483187903", "summary": "Demonstrates 8 local file system access patterns in xlwings Lite using Pyodide in the browser. Patterns include: unzipping and processing files, CSV-to-Parquet conversion via DuckDB, analytics with charts in Excel, HTML report generation, PDF creation with ReportLab, Parquet file persistence, email with attachments via Brevo API, and PowerPoint generation with python-pptx. Uses cricket ball-by-ball data as demo. Requires mounting a local folder." }, { "id": "talk-to-your-database-from-excel-postgres-duckdb-claude-mcp", "slug": "talk-to-your-database-from-excel-postgres-duckdb-claude-mcp", "title": "Talk to Your Database from Excel - Postgres, DuckDB - via Claude in Excel with MCP", "date_published": "2026-02-21T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-21T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/talk-to-your-database-from-excel-postgres-duckdb-claude-mcp", "thumbnail": "/images/blog/claudeExcelMCP_DB.png", "status": "published", "tags": [ "claude-in-excel", "mcp", "duckdb", "database-ai" ], "markdown_file": "output/markdown-manually-fixed/talk-to-your-database-from-excel-postgres-duckdb-claude-mcp.md", "html_file": "output/html-pages-manually-fixed/talk-to-your-database-from-excel-postgres-duckdb-claude-mcp.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-21T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2025159038988943555", "summary": "Public MCP server enabling SQL queries against live Postgres (Supabase, ODI cricket) and DuckDB (T20 cricket) databases from Claude in Excel. Covers 2 million rows of ball-by-ball data from 2013-2025. Built with FastAPI, asyncpg, and fastapi-mcp. Includes detailed tool docstrings for schema context, 1000-row response cap, read-only security, rate limiting, and SQL validation. Open source, single-file Python backend." }, { "id": "extract-python-code-from-xlwings-lite-excel-files", "slug": "extract-python-code-from-xlwings-lite-excel-files", "title": "How to Extract Python Code from xlwings Lite Excel Files", "date_published": "2026-02-20T12:00:00.000Z", "date_updated": null, "last_modified": "2026-02-20T12:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/extract-python-code-from-xlwings-lite-excel-files", "thumbnail": "/images/blog/00_xlwings_logo-lite-light.svg", "status": "published", "tags": [ "xlwings-lite", "converters-tools" ], "markdown_file": "output/markdown-manually-fixed/extract-python-code-from-xlwings-lite-excel-files.md", "html_file": "output/html-pages-manually-fixed/extract-python-code-from-xlwings-lite-excel-files.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-20T12:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "skip", "summary": "Python script to extract main.py and requirements.txt from xlwings Lite .xlsx files without opening Excel. The .xlsx is a ZIP archive; code is stored in xl/webextensions/webextension1.xml as JSON-encoded text. Script uses standard library only (zipfile, xml.etree, json). Also extracts Pyodide and add-in version info. Includes a command-line one-liner version." }, { "id": "claude-in-excel-mcp-connector-talk-to-backends", "slug": "claude-in-excel-mcp-connector-talk-to-backends", "title": "Claude in Excel with MCP Connector - Talk to Your Backends from Inside Excel", "date_published": "2026-02-20T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-20T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-in-excel-mcp-connector-talk-to-backends", "thumbnail": "/images/blog/claudeExcelMCP.png", "status": "published", "tags": [ "claude-in-excel", "mcp" ], "markdown_file": "output/markdown-manually-fixed/claude-in-excel-mcp-connector-talk-to-backends.md", "html_file": "output/html-pages-manually-fixed/claude-in-excel-mcp-connector-talk-to-backends.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-20T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2026248532966138237", "summary": "Tests Claude in Excel with MCP custom connectors across three backend servers: Yahoo Finance data pulls, AI technical analysis report generation, and multi-security performance review. Covers practical results, limitations with large data writes, URL handling, and MCP security considerations. Includes three open-source public MCP servers (YFIN, SPR, Technical Analysis). Compares Claude in Excel, Custom GPTs, xlwings Lite, and Claude Code for different use cases." }, { "id": "vigil-india-red-flag-events-tracker-v2-release", "slug": "vigil-india-red-flag-events-tracker-v2-release", "title": "VIGIL - India Red Flag Events Tracker v2 Release", "date_published": "2026-02-19T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-19T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-india-red-flag-events-tracker-v2-release", "thumbnail": "/images/blog/vigil_v2_01.png", "status": "published", "tags": [ "vigil" ], "markdown_file": "output/markdown-manually-fixed/vigil-india-red-flag-events-tracker-v2-release.md", "html_file": "output/html-pages-manually-fixed/vigil-india-red-flag-events-tracker-v2-release.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-19T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2026248589757100107", "summary": "VIGIL v2 release announcement for India markets. Tracks credit ratings (all agencies), SEBI insider trading disclosures, and promoter share pledges. Features include Nifty 50/100/500 scanning, company-level cross-referencing, filters, presets, CSV export, and info pages. Updated daily from NSE filings. Free, no login required.", "deck": "release" }, { "id": "fast-tips-what-is-cors-and-how-to-fix-it", "slug": "fast-tips-what-is-cors-and-how-to-fix-it", "title": "Free CORS Proxy for Yahoo Finance & Any API: Cloudflare Worker Code (CORS Explained Simply)", "date_published": "2026-02-18T10:00:00.000Z", "date_updated": "2026-06-30T10:00:00.000Z", "last_modified": "2026-02-18T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/fast-tips-what-is-cors-and-how-to-fix-it", "thumbnail": "/images/blog/corsFastTips.png", "status": "published", "tags": [ "infrastructure", "security" ], "markdown_file": "output/markdown-manually-fixed/fast-tips-what-is-cors-and-how-to-fix-it.md", "html_file": "output/html-pages-manually-fixed/fast-tips-what-is-cors-and-how-to-fix-it.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-18T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2025161657664897329", "summary": "Free CORS proxy for Yahoo Finance, GitHub, Drive, or any API using a Cloudflare Worker - a self-hosted alternative to cors-anywhere / cloudflare-cors-anywhere, no server, no domain, free tier. Full copy-paste Worker code plus the ?url= pattern, and CORS explained simply: preflight, the no-cors trap, and when to use a Worker vs Vercel serverless vs FastAPI." }, { "id": "mdrift-isin-mapping-process", "slug": "mdrift-isin-mapping-process", "title": "How I Identify and Map Every Holding — The ISIN Mapping Process", "date_published": "2026-02-18T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-18T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mdrift-isin-mapping-process", "thumbnail": "/images/blog/isingMapping.png", "status": "published", "tags": [ "mutual-funds" ], "markdown_file": "output/markdown-manually-fixed/mdrift-isin-mapping-process.md", "html_file": "output/html-pages-manually-fixed/mdrift-isin-mapping-process.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-18T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "deleted", "tweet_id": "2026248650264092746", "summary": "Detailed 12-step pipeline for standardizing Indian mutual fund portfolio holdings across 15 AMCs. Covers ISIN lookup against 361,000-record NSDL master, corporate action detection via ISIN serial number analysis, 7-character name-cut deduplication, CD/CP/T-Bill aggregation with synthetic ISINs, foreign stock identification, TREPS classification, human-in-the-loop validation, and grand total verification to the paisa across 96 fund-month combinations.", "tweet_deleted": true }, { "id": "vigil-credit-ratings-pledges-insider-trading-india", "slug": "vigil-credit-ratings-pledges-insider-trading-india", "title": "New Tool Release - VIGIL: Credit Ratings, Pledges and Insider Trading for India Markets", "date_published": "2026-02-17T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-17T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/vigil-credit-ratings-pledges-insider-trading-india", "thumbnail": "/images/blog/vigil_01.png", "status": "published", "tags": [ "vigil" ], "markdown_file": "output/markdown-manually-fixed/vigil-credit-ratings-pledges-insider-trading-india.md", "html_file": "output/html-pages-manually-fixed/vigil-credit-ratings-pledges-insider-trading-india.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-17T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2025543082863915519", "summary": "VIGIL tool for India markets combining credit ratings (all agencies from Jan 2024), SEBI insider trading disclosures (from Feb 2024), and promoter pledge data into a single company lookup. Filters by company, agency, action type, person category, and transaction type. Data sourced from NSE filings. Updated daily. Free, no login." }, { "id": "mdrift-flexi-cap-focused-fund-composition-analytics", "slug": "mdrift-flexi-cap-focused-fund-composition-analytics", "title": "MF Composition Analytics With MDRIFT - Interesting Moves in Top Flexi Cap and Focused Funds", "date_published": "2026-02-16T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-16T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mdrift-flexi-cap-focused-fund-composition-analytics", "thumbnail": "/images/blog/mfdriftIndex.png", "status": "published", "tags": [ "mutual-funds" ], "markdown_file": "output/markdown-manually-fixed/mdrift-flexi-cap-focused-fund-composition-analytics.md", "html_file": "output/html-pages-manually-fixed/mdrift-flexi-cap-focused-fund-composition-analytics.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-16T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2025540706698498196", "summary": "Analysis of top Indian Flexi Cap and Focused mutual funds using MDRIFT composition analytics tool. Highlights include PPFAS holding 12% in US tech, HDFC sitting on 15% cash, SBI Focused with 10% in Alphabet. Compares portfolio compositions across time periods showing entries, exits, weight changes. Nifty index comparisons added for spotting off-index bets." }, { "id": "movie-similarity-engine-sql-jaccard-duckdb", "slug": "movie-similarity-engine-sql-jaccard-duckdb", "title": "How I Built a Sub-Second Movie Similarity Engine With a 10-Line SQL Query", "date_published": "2026-02-17T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-17T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/movie-similarity-engine-sql-jaccard-duckdb", "thumbnail": "/images/blog/moviesLIkeThis.png", "status": "published", "tags": [ "duckdb", "database-ai" ], "markdown_file": "output/markdown-manually-fixed/movie-similarity-engine-sql-jaccard-duckdb.md", "html_file": "output/html-pages-manually-fixed/movie-similarity-engine-sql-jaccard-duckdb.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-17T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2025164801039892522", "summary": "Movie similarity engine using weighted Jaccard similarity in pure SQL on DuckDB. Pre-computes token lists from 97M person-to-title records, filtering to 12,000 movies with 10,000+ votes. Tokens encode genre, directors, actors (weighted by billing), writers, decade, runtime, and rating band via token duplication. A single 10-line SQL query compares one movie against all others in under 1 second. Returns matching factors for explainability. Open source." }, { "id": "sp500-vs-nifty50-returns-profile-reversing", "slug": "sp500-vs-nifty50-returns-profile-reversing", "title": "U.S. Markets (S&P 500) vs India (Nifty 50) - is the returns profile reversing?", "date_published": "2026-02-15T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-15T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/sp500-vs-nifty50-returns-profile-reversing", "thumbnail": "/images/blog/sp500Nifty.png", "status": "published", "tags": [ "portfolio-analytics" ], "markdown_file": "output/markdown-manually-fixed/sp500-vs-nifty50-returns-profile-reversing.md", "html_file": "output/html-pages-manually-fixed/sp500-vs-nifty50-returns-profile-reversing.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-15T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2025540756983947462", "summary": "Comparative returns analysis of S&P 500 vs Nifty 50 across six time horizons (1 to 18 years). Shows Nifty 50 leading long-term but S&P 500 pulling ahead over the last 2-3 years. Generated using the Portfolio Analysis Suite at quants.tigzig.com. Charts available as downloadable PDF. Educational analysis, not investment advice." }, { "id": "xlwings-lite-wingman-function-usage-patterns", "slug": "xlwings-lite-wingman-function-usage-patterns", "title": "xlwings Lite new WINGMAN function - some usage patterns: python sandbox, stats, cleaning, bucketing, judging", "date_published": "2026-02-14T14:00:00.000Z", "date_updated": null, "last_modified": "2026-02-14T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/xlwings-lite-wingman-function-usage-patterns", "thumbnail": "/images/blog/claudeExcelWingman.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/markdown-manually-fixed/xlwings-lite-wingman-function-usage-patterns.md", "html_file": "output/html-pages-manually-fixed/xlwings-lite-wingman-function-usage-patterns.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_meta": null, "twitter_status": "posted", "tweet_id": "2024697161292890210", "summary": "Explores usage patterns for xlwings Lite's new WINGMAN function (similar to Excel COPILOT). Tested patterns include: LLM Python sandbox execution, passing full ranges and adjacent ranges, structured output formatting, judgment-based data cleaning, and customer profile categorization. Works with OpenAI, Gemini, and custom models via OpenRouter. Includes downloadable test file." }, { "id": "releasing-mdrift-mutual-fund-composition-drift-analytics", "slug": "releasing-mdrift-mutual-fund-composition-drift-analytics", "title": "Releasing MDRIFT - Mutual Fund Composition & Drift Analytics Tool", "date_published": "2026-02-13T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-13T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/releasing-mdrift-mutual-fund-composition-drift-analytics", "thumbnail": "/images/blog/mdrift01.png", "status": "published", "tags": [ "mutual-funds" ], "markdown_file": "output/markdown-manually-fixed/releasing-mdrift-mutual-fund-composition-drift-analytics.md", "html_file": "output/html-pages-manually-fixed/releasing-mdrift-mutual-fund-composition-drift-analytics.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_meta": null, "twitter_status": "deleted", "tweet_id": "2024696818911854812", "summary": "MDRIFT tool release for comparing Indian mutual fund portfolio compositions across funds and time periods. Processes monthly AMC Excel files, standardizes ISINs, handles corporate action remaps, and groups debt instruments. Covers 21 equity funds across Large Cap, Mid Cap, Small Cap, Flexi Cap, and Focused categories. Sept 2025 through Jan 2026 data. Features drill-downs from scheme type to individual ISIN level.", "tweet_deleted": true }, { "id": "nifty50-30-day-forward-return-analysis-claude-in-excel", "slug": "nifty50-30-day-forward-return-analysis-claude-in-excel", "title": "NIFTY50 - 30 Day Forward Return Analysis Feb 2008 to 2026 - Claude in Excel with Python, Lambdas and Advanced Formulas", "date_published": "2026-02-11T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-11T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/nifty50-30-day-forward-return-analysis-claude-in-excel", "thumbnail": "/images/blog/claudeExcelNifty50.png", "status": "published", "tags": [ "claude-in-excel", "portfolio-analytics" ], "markdown_file": "output/markdown-manually-fixed/nifty50-30-day-forward-return-analysis-claude-in-excel.md", "html_file": "output/html-pages-manually-fixed/nifty50-30-day-forward-return-analysis-claude-in-excel.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-11T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2024696288923754953", "summary": "Nifty50 forward return analysis built entirely in Claude in Excel. For each trading day from 2008-2026, computes 30 forward returns with quintile cuts (P20-P80), positive/negative day counts, and confidence intervals. Uses Python for initial computation, then validates with manual formulas, LET+SEQUENCE, and named LAMBDA functions. MAP, REDUCE, and SCAN with LAMBDA for trade diagnostics. Completed in 2.5 hours across two sessions." }, { "id": "from-12-second-queries-to-under-1s-optimizing-230-million-row-dashboard", "slug": "from-12-second-queries-to-under-1s-optimizing-230-million-row-dashboard", "title": "From 12 second queries to under 1s: Optimizing a 230 Million Row Dashboard - 14 Bottlenecks I Had to Fix", "date_published": "2026-02-10T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-10T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/from-12-second-queries-to-under-1s-optimizing-230-million-row-dashboard", "thumbnail": "/images/blog/cineproTimeline.png", "status": "published", "tags": [ "duckdb", "fastapi", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/from-12-second-queries-to-under-1s-optimizing-230-million-row-dashboard.md", "html_file": "output/html-pages-manually-fixed/from-12-second-queries-to-under-1s-optimizing-230-million-row-dashboard.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-10T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2024695845887889710", "summary": "Documents 14 optimization techniques that reduced query times from 9-12 seconds to under 1 second on a 230M-row DuckDB dashboard (16GB). Covers pre-computed denormalized tables, single-blob dashboard cache, in-memory query caching, ORDER BY index conflicts, adaptive queries, EXISTS vs CTE (15x gap), client-side computation from loaded data, Docker container memory mismatch with DuckDB, and autocomplete race condition fixes. Open source with dual Hetzner/Oracle backends." }, { "id": "claude-in-excel-powerpoint-working-tips", "slug": "claude-in-excel-powerpoint-working-tips", "title": "Power User Guide to Claude in Excel & PowerPoint - 26 Working Tips", "date_published": "2026-02-10T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-10T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-in-excel-powerpoint-working-tips", "thumbnail": "/images/blog/claudeInExcelCollage.png", "status": "published", "tags": [ "claude-in-excel" ], "markdown_file": "output/markdown-manually-fixed/claude-in-excel-powerpoint-working-tips.md", "html_file": "output/html-pages-manually-fixed/claude-in-excel-powerpoint-working-tips.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-10T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2024420639394975911", "summary": "26 practical tips for using Claude in Excel and PowerPoint. Covers voice dictation, providing domain context, validation techniques (triple-match formulas), LET and LAMBDA usage, advanced Excel functions, Python sandbox capabilities and limitations, token conservation, automation options via xlwings Lite or Claude Code, data privacy considerations across different deployment models, and when to use each tool in the ecosystem." }, { "id": "claude-in-excel-nifty50-return-distribution-analysis", "slug": "claude-in-excel-nifty50-return-distribution-analysis", "title": "Claude in Excel - Nifty50 Return Distribution Analysis (30 days forward) 2008 to 2026", "date_published": "2026-02-08T16:00:00.000Z", "date_updated": null, "last_modified": "2026-02-08T16:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-in-excel-nifty50-return-distribution-analysis", "thumbnail": "/images/blog/claudeNifty50_01.png", "status": "published", "tags": [ "claude-in-excel", "portfolio-analytics" ], "markdown_file": "output/markdown-manually-fixed/claude-in-excel-nifty50-return-distribution-analysis.md", "html_file": "output/html-pages-manually-fixed/claude-in-excel-nifty50-return-distribution-analysis.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_meta": null, "twitter_status": "posted", "tweet_id": "2024420445840379923", "summary": "Claude in Excel used to compute Nifty50 30-day forward return distributions from 2008-2026. For each trading day, calculates 30 forward returns and extracts quintile cuts (P20-P80) plus positive/negative day counts. Built after 15-20 minutes of brainstorming with Claude on methodology. Includes manual validation for a single day and reconstructed Python code output. Shared as downloadable Excel file." }, { "id": "claude-in-excel-rbi-macroeconomic-dashboard", "slug": "claude-in-excel-rbi-macroeconomic-dashboard", "title": "Claude in Excel built a 50-chart India Macroeconomic Dashboard from RBI data in under an hour", "date_published": "2026-02-08T14:00:00.000Z", "date_updated": null, "last_modified": "2026-02-08T14:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-in-excel-rbi-macroeconomic-dashboard", "thumbnail": "/images/blog/claudeExcelRBIMacor_01.png", "status": "published", "tags": [ "claude-in-excel" ], "markdown_file": "output/markdown-manually-fixed/claude-in-excel-rbi-macroeconomic-dashboard.md", "html_file": "output/html-pages-manually-fixed/claude-in-excel-rbi-macroeconomic-dashboard.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_meta": null, "twitter_status": "posted", "tweet_id": "2024420202713403633", "summary": "Claude in Excel built a 50-chart India macroeconomic dashboard from RBI data in under one hour. Covers weekly, fortnightly, monthly, and quarterly indicators. Organized data into flat file format with frequency, unit, and value columns. Includes validation sheet with three cross-checks (formula totals, Python output, SUMIF from database sheet). Shared as downloadable Excel file with reconstructed code." }, { "id": "python-in-excel-with-claude-what-works-and-what-doesnt", "slug": "python-in-excel-with-claude-what-works-and-what-doesnt", "title": "Claude in Excel & PowerPoint. Is it worth it? What works and what doesn't", "date_published": "2026-02-08T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-08T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/python-in-excel-with-claude-what-works-and-what-doesnt", "thumbnail": "/images/blog/claudeInExcelEval.png", "status": "published", "tags": [ "claude-in-excel", "python-in-excel" ], "markdown_file": "output/markdown-manually-fixed/python-in-excel-with-claude-what-works-and-what-doesnt.md", "html_file": "output/html-pages-manually-fixed/python-in-excel-with-claude-what-works-and-what-doesnt.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_meta": null, "twitter_status": "posted", "tweet_id": "2024419923540541502", "summary": "Evaluation of Claude in Excel focusing on Excel/PowerPoint productivity and Python sandbox capabilities. Covers what works: pivots, formulas, stats, image reading, web search, ML models, charts. Python limitations: no API calls, no external databases, no local file writes, 30MB upload limit, non-deterministic outputs. Compares Claude in Excel (AI assistant) vs xlwings Lite (full Python environment). Includes 18-page slide deck." }, { "id": "python-in-excel-claude-vs-xlwings-lite", "slug": "python-in-excel-claude-vs-xlwings-lite", "title": "Python In Excel - Claude Vs. xlwings Lite? Who Wins?", "date_published": "2026-02-07T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-07T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/python-in-excel-claude-vs-xlwings-lite", "thumbnail": "/images/blog/pythonInExcelClaudeVsXlwings.png", "status": "published", "tags": [ "claude-in-excel", "python-in-excel", "xlwings-lite" ], "markdown_file": "output/markdown-manually-fixed/python-in-excel-claude-vs-xlwings-lite.md", "html_file": "output/html-pages-manually-fixed/python-in-excel-claude-vs-xlwings-lite.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-07T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2023959312117440730", "summary": "Comparison of Claude in Excel and xlwings Lite as different tools for different jobs. Claude in Excel: AI assistant for Excel heavy lifting, Python sandbox, no API calls, no automation, no local file access, non-deterministic. xlwings Lite: pure Python in Excel, API calls, web scraping, database connections, local folders, automation. Also covers when to use Jupyter/Colab for ML models and Claude Code for full-stack development." }, { "id": "claude-in-excel", "slug": "claude-in-excel", "title": "Claude in Excel just one-shotted an XGBoost response model with train-test split, AUC and full decile table. In a spreadsheet.", "date_published": "2026-02-06T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-06T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/claude-in-excel", "thumbnail": "/images/blog/claudeInExcel.png", "status": "published", "tags": [ "claude-in-excel", "python-in-excel" ], "markdown_file": "output/markdown-manually-fixed/claude-in-excel.md", "html_file": "output/html-pages-manually-fixed/claude-in-excel.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-06T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2023958692732043650", "summary": "First-hand test of Claude in Excel building an XGBoost response model with train-test split, AUC, and full decile table inside a spreadsheet. Also tested pivot table creation. Notes Python sandbox runs on Anthropic servers with no visible code editor. Discusses data privacy implications across different deployment models (direct Anthropic vs AWS Bedrock/Azure). Shared as downloadable Excel file." }, { "id": "custom-dashboard-duckdb-fastapi-230-million-rows", "slug": "custom-dashboard-duckdb-fastapi-230-million-rows", "title": "Architecture & Setup for a Dashboard with Hundreds of Millions of Records - Powered by DuckDB", "date_published": "2026-02-05T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-05T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/custom-dashboard-duckdb-fastapi-230-million-rows", "thumbnail": "/images/blog/imdb-dashboard-collage.png", "status": "published", "tags": [ "duckdb", "fastapi", "infrastructure", "react" ], "markdown_file": "output/markdown-manually-fixed/custom-dashboard-duckdb-fastapi-230-million-rows.md", "html_file": "output/html-pages-manually-fixed/custom-dashboard-duckdb-fastapi-230-million-rows.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-05T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2024419242507194665", "summary": "Architecture guide for building a custom dashboard with 230M rows on DuckDB (16GB). Covers FastAPI backend with read-only and admin endpoints, React frontend on Vercel, serverless proxy for API security, dual backend setup (Hetzner/Oracle), data pipeline with pre-computed denormalized tables, Clerk auth toggle, query timer, and smart search. Addresses Docker container memory mismatch with DuckDB. Open source, runs on 8 EUR/month Hetzner VPS." }, { "id": "claude-code-top-10-tips-from-boris-cherny", "slug": "claude-code-top-10-tips-from-boris-cherny", "title": "Claude Code: Top 10 Tips from Boris Cherny", "date_published": "2026-02-04T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-04T10:00:00.000Z", "source": "fresh", "original_url": "https://x.com/bcherny/status/2017742741636321619", "thumbnail": "/images/logos/claudeCode.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/markdown-manually-fixed/claude-code-top-10-tips-from-boris-cherny.md", "html_file": "output/html-pages-manually-fixed/claude-code-top-10-tips-from-boris-cherny.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-04T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "skip", "summary": "Verbatim transcript of Boris Cherny's (Claude Code creator) top 10 tips. Key advice: run 3-5 parallel git worktrees, start complex tasks in plan mode, invest in CLAUDE.md with self-updating rules, create reusable skills and slash commands, use subagents for complex tasks, voice dictation (3x faster), use Claude for BigQuery/analytics queries, and leverage learning modes with HTML presentations and ASCII diagrams." }, { "id": "edgartools-sec-edgar-python-library", "slug": "edgartools-sec-edgar-python-library", "title": "Found a Python library that does all the heavy lifting for working with SEC EDGAR API - EdgarTools from Dwight Gunning", "date_published": "2026-02-02T10:00:00.000Z", "date_updated": null, "last_modified": "2026-02-02T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/edgartools-sec-edgar-python-library", "thumbnail": "/images/blog/edgarTools.png", "status": "published", "tags": [ "portfolio-analytics", "fastapi" ], "markdown_file": "output/markdown-manually-fixed/edgartools-sec-edgar-python-library.md", "html_file": "output/html-pages-manually-fixed/edgartools-sec-edgar-python-library.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-02-02T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2023956915622211588", "summary": "Review of EdgarTools Python library by Dwight Gunning for SEC EDGAR data. Features built-in XBRL standardization for cross-company financial comparison, 10-30x speed improvement via PyArrow and lxml, automatic SEC rate limit compliance, and coverage of 10-K, 10-Q, 8-K, 13F, and Form 4 filings. Includes built-in MCP server for AI tool integration. Author is using it as backbone for a FastAPI quarterly comparison tool." }, { "id": "chatgpt-connected-databases-ai-coder-deployment", "slug": "chatgpt-connected-databases-ai-coder-deployment", "title": "ChatGPT connected to your databases. One-click deployment instructions for AI Coders", "date_published": "2026-01-31T10:00:00.000Z", "date_updated": null, "last_modified": "2026-01-31T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/chatgpt-connected-databases-ai-coder-deployment", "thumbnail": "/images/blog/gptCopyAICoder_landscape.png", "status": "published", "tags": [ "database-ai", "custom-gpt", "ai-coders" ], "markdown_file": "output/markdown-manually-fixed/chatgpt-connected-databases-ai-coder-deployment.md", "html_file": "output/html-pages-manually-fixed/chatgpt-connected-databases-ai-coder-deployment.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-31T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Custom GPT connected to three live databases (Supabase, Neon, Aiven) for natural language querying of cricket and Tour de France data. Features a 'Copy for AI Coders' button that provides deployment instructions for Claude Code or Google Antigravity to handle end-to-end setup including backend, frontend, and database provisioning. FastAPI server sits between ChatGPT and databases." }, { "id": "oracle-always-free-arm-vps-retry-script", "slug": "oracle-always-free-arm-vps-retry-script", "title": "How to Get Oracle's Always Free ARM VPS (24GB RAM, 4 CPUs). Auto-Retry Script to Beat 'Out of Capacity'.", "date_published": "2026-01-30T10:00:00.000Z", "date_updated": "2026-07-11T10:00:00.000Z", "last_modified": "2026-07-11T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/oracle-always-free-arm-vps-retry-script", "thumbnail": "/images/blog/ovrmTool_v2.png", "status": "published", "tags": [ "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/oracle-always-free-arm-vps-retry-script.md", "html_file": "output/html-pages-manually-fixed/oracle-always-free-arm-vps-retry-script.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-30T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Guide to obtaining Oracle Cloud's free 24GB RAM, 4 ARM CPU VPS through automated retry scripts. Capacity is rarely available, requiring 24/7 cycling through availability zones for 1-3 months. Includes open-source FastAPI monitoring tool with web UI. Author ran 100K+ API calls over 30 days. Also covers Oracle's always-available 2x AMD Micro VMs (1GB RAM each). Once obtained, deployed DuckDB dashboard as alternate backend same day." }, { "id": "cinepro-movie-explorer-duckdb", "slug": "cinepro-movie-explorer-duckdb", "title": "CinePro - 230M Rows, 16GB Database, Instant Queries with DuckDB", "date_published": "2026-01-28T10:00:00.000Z", "date_updated": null, "last_modified": "2026-01-28T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/cinepro-movie-explorer-duckdb", "thumbnail": "/images/blog/cineproLanding_v2.png", "status": "published", "tags": [ "duckdb", "fastapi", "react" ], "markdown_file": "output/markdown-manually-fixed/cinepro-movie-explorer-duckdb.md", "html_file": "output/html-pages-manually-fixed/cinepro-movie-explorer-duckdb.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-28T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "CinePro movie analytics dashboard built on 230M rows (16GB) of IMDb data in a single DuckDB file. Features type-as-you-search across 15M people, multi-filter discovery, Jaccard similarity for finding similar movies, career timeline analysis, side-by-side comparisons, and live query timer. Runs on $7/month Hetzner VPS alongside 40 other backends. Dual backend (Hetzner/Oracle) with UI toggle. Fully open source." }, { "id": "hetzner-coolify-self-hosting-ai-apps-under-10-dollars", "slug": "hetzner-coolify-self-hosting-ai-apps-under-10-dollars", "title": "You are paying ~$3-7 per deployment for your AI Apps. How do you do it in <$10 per month?", "date_published": "2026-01-24T10:00:00.000Z", "date_updated": null, "last_modified": "2026-01-24T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/hetzner-coolify-self-hosting-ai-apps-under-10-dollars", "thumbnail": "/images/blog/coolify.png", "status": "published", "tags": [ "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/hetzner-coolify-self-hosting-ai-apps-under-10-dollars.md", "html_file": "output/html-pages-manually-fixed/hetzner-coolify-self-hosting-ai-apps-under-10-dollars.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-24T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Guide to self-hosting AI app backends on Hetzner VPS with Coolify deployment manager for under 10 EUR/month. Covers the cost problem with per-deployment pricing (40 apps at $3-7 each = $100-200/month). Hetzner provides 8GB RAM, 80GB disk, 4 vCPUs for 7.69 EUR. Coolify offers Render-like deployment interface with GitHub integration. Includes setup guide reference." }, { "id": "fail2ban-server-security-bots-ai-tools", "slug": "fail2ban-server-security-bots-ai-tools", "title": "Server Meltdown: How Bots Crashed My AI Tools and What I Did About It", "date_published": "2026-01-23T10:00:00.000Z", "date_updated": null, "last_modified": "2026-01-23T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/fail2ban-server-security-bots-ai-tools", "thumbnail": "/images/blog/fail2ban.png", "status": "published", "tags": [ "security", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/fail2ban-server-security-bots-ai-tools.md", "html_file": "output/html-pages-manually-fixed/fail2ban-server-security-bots-ai-tools.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-23T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "posted-as-infra-part3", "summary": "Account of a bot attack that crashed a self-hosted server running AI tools. Thousands of SSH login attempts overwhelmed CPU despite SSH key-only auth. Fixed with tightened fail2ban settings: 5 max retries, 1-hour find window, 24-hour ban. Results: 157 currently banned IPs, 1,223 total bans, 6,082 blocked attempts in one week. Links to full 18-mistake security guide." }, { "id": "briq-duckdb-ai-browser-no-database-setup", "slug": "briq-duckdb-ai-browser-no-database-setup", "title": "BRIQ App: DuckDB AI in Browser - 500MB Files, 4M+ Records, No Database Setup", "date_published": "2026-01-22T10:00:00.000Z", "date_updated": null, "last_modified": "2026-01-22T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/briq-duckdb-ai-browser-no-database-setup", "thumbnail": "/images/blog/briq.png", "status": "published", "tags": [ "duckdb", "database-ai" ], "markdown_file": "output/markdown-manually-fixed/briq-duckdb-ai-browser-no-database-setup.md", "html_file": "output/html-pages-manually-fixed/briq-duckdb-ai-browser-no-database-setup.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-22T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2023956086739661120", "summary": "BRIQ is a browser-based DuckDB AI tool for querying flat files up to 1.5GB using natural language. No database setup or credentials needed. Upload CSV/TSV files, auto-converts to DuckDB in-browser, query with plain English. Data stays in browser except for LLM API calls. Built on SQL Rooms AI. Also available as single-file HTML for offline use. Supports merging, appending, and transforming multiple files. Open source." }, { "id": "xlwings-lite-data-importer-v2-released", "slug": "xlwings-lite-data-importer-v2-released", "title": "xlwings Lite Data Importer v2 Released", "date_published": "2026-01-17T10:00:00.000Z", "date_updated": null, "last_modified": "2026-01-17T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/xlwings-lite-data-importer-v2-released", "thumbnail": "/images/blog/xlwingsLIteImporter.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel", "duckdb" ], "markdown_file": "output/markdown-manually-fixed/xlwings-lite-data-importer-v2-released.md", "html_file": "output/html-pages-manually-fixed/xlwings-lite-data-importer-v2-released.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-17T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "xlwings Lite Data Importer v2 adds support for any file type (PDFs, images, ZIPs, data files), files up to 1.2GB tested, and token-based private access (Dropbox, Google Drive, GitHub) alongside shareable links. Auto-converts data files to DuckDB or imports as-is. Uses token access pattern from xlwings_utils package. Includes CORS proxy and token setup guides with AI coder instructions." }, { "id": "2026-infra-guide-part-3-security-mistakes", "slug": "2026-infra-guide-part-3-security-mistakes", "title": "2026 Infra Guide for AI Tool Builders - Part 3: The 18 Common Security Mistakes and How to Fix Them", "date_published": "2026-01-20T10:00:00.000Z", "date_updated": "2026-06-25T10:00:00.000Z", "last_modified": "2026-01-20T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/2026-infra-guide-part-3-security-mistakes", "thumbnail": "/images/blog/warning-triangle.svg", "status": "published", "tags": [ "security", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/2026-infra-guide-part-3-security-mistakes.md", "html_file": "output/html-pages-manually-fixed/2026-infra-guide-part-3-security-mistakes.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-20T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Documents 18 security mistakes learned from building AI tools for small businesses. Covers server security (SSH hardening, fail2ban, port management), frontend security (API key exposure, .gitignore, CORS limitations, backend URL leakage), backend security (rate limiting, token auth, frontend-vs-server auth gap, SQL sanitization, error message leakage), database security (admin credentials, connection pooling), and AI coder security audit methodology." }, { "id": "self-hosting-infrastructure-ai-tool-builders-2026-part-1-ai-coder", "slug": "self-hosting-infrastructure-ai-tool-builders-2026-part-1-ai-coder", "title": "2026 Infra Guide for AI Tool Builders - Part 1: AI Coder", "date_published": "2026-01-14T10:00:00.000Z", "date_updated": "2026-06-25T10:00:00.000Z", "last_modified": "2026-01-14T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/self-hosting-infrastructure-ai-tool-builders-2026-part-1-ai-coder", "thumbnail": "/images/logos/claudeCode.png", "status": "published", "tags": [ "ai-coders", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/self-hosting-infrastructure-ai-tool-builders-2026-part-1-ai-coder.md", "html_file": "output/html-pages-manually-fixed/self-hosting-infrastructure-ai-tool-builders-2026-part-1-ai-coder.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-14T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Describes how Claude Code serves as a complete dev team for building and deploying 30+ production AI tools. Covers full-stack app builds, direct deployment to Vercel and Coolify, database management, auth setup (Auth0, Clerk), Cloudflare DNS, server debugging via SSH, Git operations, API monitoring, and security audits. Emphasizes architecture planning and brainstorming before coding. Uses $200/month Max tier. Part 1 of infra guide series." }, { "id": "2026-infra-guide-part-2-deployment-hosting", "slug": "2026-infra-guide-part-2-deployment-hosting", "title": "2026 Infra Guide for AI Tool Builders - Part 2: Deployment & Hosting", "date_published": "2026-01-15T10:00:00.000Z", "date_updated": "2026-06-25T10:00:00.000Z", "last_modified": "2026-01-15T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/2026-infra-guide-part-2-deployment-hosting", "thumbnail": "/images/infra-part2-collage.png", "status": "published", "tags": [ "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/2026-infra-guide-part-2-deployment-hosting.md", "html_file": "output/html-pages-manually-fixed/2026-infra-guide-part-2-deployment-hosting.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-15T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "twitter_status": "posted", "tweet_id": "2023953639661072627", "summary": "Covers the four elements of deploying web apps: frontend hosting (Vercel free tier, 40+ UIs), backend hosting (Hetzner VPS at 7.69 EUR/month with Coolify), domain registration, and DNS management (Cloudflare). Details the journey from Render to Railway to Hetzner. Covers Cloudflare free tier limits (100-second timeout, single subdomain SSL), serverless functions, and Flowise/n8n deployment specifics. Total infrastructure cost: ~$7-8/month for 30+ backends." }, { "id": "llm-costing-for-database-ai-apps-live-experience-live-app-open-source", "slug": "llm-costing-for-database-ai-apps-live-experience-live-app-open-source", "title": "LLM Costing for Database AI Apps. Live Experience. Live App. Open Source", "date_published": "2025-09-03T10:00:00.000Z", "date_updated": null, "last_modified": "2025-09-03T10:00:00.000Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/llm-costing-for-database-ai-apps-live-experience-live-app-open-source", "thumbnail": null, "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/markdown-manually-fixed/llm-costing-for-database-ai-apps-live-experience-live-app-open-source.md", "html_file": "output/html-pages-manually-fixed/llm-costing-for-database-ai-apps-live-experience-live-app-open-source.html", "content_length": 0, "has_images": false, "has_videos": true, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-01-12T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "1KsVNzd1jOM", "summary": "LLM cost analysis from 250+ structured tests on database AI apps. Key findings: single-step agents cost $0.50-$2.00 per 100 queries (80% of jobs), multi-agent setups run $15-$20 per 100 queries (10-50x multiplier). Claude Sonnet 4 leads quality, GPT-5 close but costs volatile. Links to DATS-4 open-source text-to-SQL suite with live app and 49-page field guide." }, { "id": "analyze-data-aws-azure-custom-gpt", "slug": "analyze-data-aws-azure-custom-gpt", "title": "Analyze Live Data | AWS-Azure DW | via Custom GPT & LLM Apps", "date_published": "2024-07-27T10:00:00.000Z", "date_updated": null, "last_modified": "2024-07-27T10:00:00.000Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/analyze-data-aws-azure-custom-gpt", "thumbnail": null, "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/markdown-manually-fixed/analyze-data-aws-azure-custom-gpt.md", "html_file": "output/html-pages-manually-fixed/analyze-data-aws-azure-custom-gpt.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-01-12T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Demonstrates connecting Custom GPTs and LLM apps to live AWS (RDS-MySQL) and Azure (MySQL) data warehouses for natural language querying. Covers data transformations, analysis, table operations, cross-warehouse operations, and ML model building. Uses FastAPI server as intermediary. Series includes upcoming guides on Flowise AI deployment, LLM cost-performance tradeoffs, and security considerations." }, { "id": "flowise-is-my-goto-platform-for-genai-llm-app-development", "slug": "flowise-is-my-goto-platform-for-genai-llm-app-development", "title": "Flowise is my goto platform for GenAI and LLM apps", "date_published": "2024-07-27T10:00:00.000Z", "date_updated": null, "last_modified": "2024-07-27T10:00:00.000Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/flowise-is-my-goto-platform-for-genai-llm-app-development", "thumbnail": null, "status": "published", "tags": [ "database-ai", "custom-gpt" ], "markdown_file": "output/markdown-manually-fixed/flowise-is-my-goto-platform-for-genai-llm-app-development.md", "html_file": "output/html-pages-manually-fixed/flowise-is-my-goto-platform-for-genai-llm-app-development.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-01-12T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Overview of FlowiseAI as a preferred platform for GenAI and LLM app development. Highlights include integrated RAG with LangChain and LlamaIndex, 10-15+ vector database integrations (Pinecone, Vectara), Custom Tool for API calls, multi-LLM support including Groq and Gemini free tier, and Make.com/Zapier automation workflows. Open source, Y Combinator backed, 21K GitHub stars." }, { "id": "xlwings-utils-secure-cloud-access-vba-bridge", "slug": "xlwings-utils-secure-cloud-access-vba-bridge", "title": "xlwings_utils: Secure Cloud Access & VBA Bridge", "date_published": "2026-01-11T10:00:00.000Z", "date_updated": null, "last_modified": "2026-01-11T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/xlwings-utils-secure-cloud-access-vba-bridge", "thumbnail": "/images/blog/xlwingsUtilsRudd.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/markdown-manually-fixed/xlwings-utils-secure-cloud-access-vba-bridge.md", "html_file": "output/html-pages-manually-fixed/xlwings-utils-secure-cloud-access-vba-bridge.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-11T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Review of xlwings_utils package by Ruud van der Ham solving two xlwings Lite limitations. First: secure OAuth token-based access to Dropbox, Google Drive, and GitHub (tested by author) instead of shareable URLs with CORS proxy. Second: VBA bridge for local filesystem access using base64 encoding through Excel sheets, enabling PDF export to local folders, batch image processing, and zip file handling. Also includes stdout capture and openpyxl integration utilities." }, { "id": "duckdb-meets-excel-xlwings-lite-data-tools", "slug": "duckdb-meets-excel-xlwings-lite-data-tools", "title": "DuckDB Meets Excel: xlwings Lite Data Tools", "date_published": "2026-01-09T06:27:00.000Z", "date_updated": null, "last_modified": "2026-01-09T06:27:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/duckdb-meets-excel-xlwings-lite-data-tools", "thumbnail": "/images/blog/duckitDiagram.png", "status": "published", "tags": [ "duckdb", "xlwings-lite", "python-in-excel" ], "markdown_file": "output/markdown-manually-fixed/duckdb-meets-excel-xlwings-lite-data-tools.md", "html_file": "output/html-pages-manually-fixed/duckdb-meets-excel-xlwings-lite-data-tools.html", "content_length": 0, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2026-01-09T06:27:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Two tools for getting large datasets into xlwings Lite. DuckIt: browser-based file converter that turns CSVs into DuckDB or Parquet with shareable links (tested up to 1.5GB, 10M+ records). xlwings Lite Data Importer: pulls files from URLs into Excel, supports DuckDB, SQLite, CSV, Parquet, JSON (tested up to 1.2GB, 20M+ records). Uses Cloudflare Worker for CORS bypass. Both open source." }, { "id": "mistakes-i-made-building-text-to-sql-agents-live-projects-2025-learnings", "slug": "mistakes-i-made-building-text-to-sql-agents-live-projects-2025-learnings", "title": "Mistakes I Made Building Text-to-SQL Agents in Live Projects. My 2025 Learnings", "date_published": "2025-12-26T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-26T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/mistakes-i-made-building-text-to-sql-agents-live-projects-2025-learnings", "thumbnail": null, "status": "published", "tags": [ "text-to-sql", "database-ai" ], "markdown_file": "output/markdown-manually-fixed/mistakes-i-made-building-text-to-sql-agents-live-projects-2025-learnings.md", "html_file": "output/html-pages-manually-fixed/mistakes-i-made-building-text-to-sql-agents-live-projects-2025-learnings.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-26T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Documents mistakes from building production text-to-SQL agents over one year. Covers architecture (over-engineering for analytics when users need operations), context sharing (schema in YAML, business rules, categorical distributions), agent constraints (LIMIT rules, query caps, debug protocols, NULLIF for division), model selection (GPT-4.1-mini for routine, reserve premium for hard tasks), cache hit monitoring, and security (write access controls). Reduced cost from $20+ to under $2 per 100 queries." }, { "id": "biggest-lesson-2025-ai-writes-better-code-when-you-dont-let-it-code", "slug": "biggest-lesson-2025-ai-writes-better-code-when-you-dont-let-it-code", "title": "Biggest lesson from 2025: AI writes better code when you don't let it code", "date_published": "2025-12-25T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-25T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/biggest-lesson-2025-ai-writes-better-code-when-you-dont-let-it-code", "thumbnail": null, "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/markdown-manually-fixed/biggest-lesson-2025-ai-writes-better-code-when-you-dont-let-it-code.md", "html_file": "output/html-pages-manually-fixed/biggest-lesson-2025-ai-writes-better-code-when-you-dont-let-it-code.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-25T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Argues that AI coding quality improves dramatically when preceded by extensive planning. Author spent nearly 2 days discussing, evaluating, and planning with AI before writing any code for a client project (React, PHP, MySQL, LLM integration). Process: describe requirements with business context, interrogate every component and trade-off, produce detailed phase-wise implementation plan with success criteria, then execute phase by phase." }, { "id": "2025-transformational-year-gratitude-platform-builders", "slug": "2025-transformational-year-gratitude-platform-builders", "title": "2025 has been a transformational year for me. Deep gratitude to the platform builders and engineers who made it possible.", "date_published": "2025-12-23T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-23T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/2025-transformational-year-gratitude-platform-builders", "thumbnail": null, "status": "published", "tags": [ "ai-coders", "infrastructure" ], "markdown_file": "output/markdown-manually-fixed/2025-transformational-year-gratitude-platform-builders.md", "html_file": "output/html-pages-manually-fixed/2025-transformational-year-gratitude-platform-builders.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-23T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Retrospective crediting platforms that enabled transition from analytics to building 30+ open-source apps. Key tools: Claude Code and Cursor for AI coding, Render then Hetzner+Coolify for hosting, Vercel for frontends, Neon for instant PostgreSQL, FlowiseAI for multi-agent setups, xlwings Lite for Python in Excel, Mito AI for Jupyter, OpenAI Custom GPTs for no-UI automation, and Llama Parse for PDF processing." }, { "id": "quants-agent-llm-choices-technical-analysis-reports", "slug": "quants-agent-llm-choices-technical-analysis-reports", "title": "Quants Agent: Now with LLM Choices for Technical Analysis Reports", "date_published": "2025-12-21T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-21T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/quants-agent-llm-choices-technical-analysis-reports", "thumbnail": null, "status": "published", "tags": [ "technical-analysis", "portfolio-analytics" ], "markdown_file": "output/markdown-manually-fixed/quants-agent-llm-choices-technical-analysis-reports.md", "html_file": "output/html-pages-manually-fixed/quants-agent-llm-choices-technical-analysis-reports.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-21T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Quants agent update adding LLM model selection for AI technical analysis reports. Pipeline: fetches OHLCV from Yahoo Finance via yfinance, calculates EMAs, MACD, RSI, ROC, Bollinger Bands via finta, generates matplotlib charts, sends to chosen LLM (including GPT-5.2, Claude Sonnet 4.5) via OpenRouter for interpretation, converts to styled PDF and interactive HTML via markdown-to-PDF service. Open source." }, { "id": "large-file-upload-for-database-ai-text-to-sql-apps", "slug": "large-file-upload-for-database-ai-text-to-sql-apps", "title": "Large File Upload for Database AI Text-to-SQL Apps: A Practical Guide", "date_published": "2025-12-12T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-12T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/large-file-upload-for-database-ai-text-to-sql-apps", "thumbnail": "/images/blog/00_large_file_upload_snapshot.png", "status": "published", "tags": [ "database-ai", "text-to-sql", "fastapi" ], "markdown_file": "output/markdown-manually-fixed/large-file-upload-for-database-ai-text-to-sql-apps.md", "html_file": "output/html-pages-manually-fixed/large-file-upload-for-database-ai-text-to-sql-apps.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-12T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Comprehensive guide documenting 30+ patterns for handling large file uploads (up to 1.6GB) through FastAPI backends. Covers chunked streaming (94% memory reduction at 1GB), Neon instant database provisioning, async handling with thread pools, Polars over Pandas, PostgreSQL COPY command (10-100x faster than INSERT), connection pooling with stale detection, timeout configuration, rate limiting, delimiter detection, and column sanitization. Tested with 11.8M rows." }, { "id": "releasing-rex2-ai-decision-intelligence", "slug": "releasing-rex2-ai-decision-intelligence", "title": "Releasing REX-2: AI Decision Intelligence", "date_published": "2024-11-24T11:32:24.220Z", "date_updated": null, "last_modified": "2025-12-13T10:00:00.000Z", "source": "manual", "original_url": "https://www.tigzig.com/post/releasing-rex2-ai-decision-intelligence", "thumbnail": null, "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/markdown-manually-fixed/releasing-rex2-ai-decision-intelligence.md", "html_file": "output/html-pages-manually-fixed/releasing-rex2-ai-decision-intelligence.html", "content_length": 0, "has_images": false, "has_videos": true, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-13T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "LoE64UBgz3s", "summary": "REX-2 is an open-source AI decision intelligence tool featuring natural language to SQL and Python, interactive tables with stats, PDF report generation, and connections to any MySQL/PostgreSQL database. Supports temporary on-the-fly databases via Neon. Built with React, FastAPI, FlowiseAI, Auth0. Includes complex analysis capabilities like cards segment profiling and statistical charts via E2B sandbox. Four GitHub repos with video build guide." }, { "id": "duckdb-isn-t-just-fast-sql-it-s-python-sql-and-compression-all-in-one-box", "slug": "duckdb-isn-t-just-fast-sql-it-s-python-sql-and-compression-all-in-one-box", "title": "DuckDB isn't just fast SQL. It's Python, SQL and compression all in one box.", "date_published": "2025-12-09T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-10T09:23:30.164226Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/duckdb-isn-t-just-fast-sql-it-s-python-sql-and-compression-all-in-one-box", "thumbnail": "/images/blog/00_DuckDB_inline-lightmode.png", "status": "published", "tags": [ "duckdb" ], "markdown_file": "output/markdown-manually-fixed/duckdb-isn-t-just-fast-sql-it-s-python-sql-and-compression-all-in-one-box.md", "html_file": "output/html-pages-manually-fixed/duckdb-isn-t-just-fast-sql-it-s-python-sql-and-compression-all-in-one-box.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-10T09:23:30.164226Z", "scraped_from": "output/jina-ai-raw/duckdb-isn-t-just-fast-sql-it-s-python-sql-and-compression-all-in-one-box.md", "round": 2 }, "youtube_ids": "", "summary": "Highlights lesser-known DuckDB SQL features discovered through Jasja De Vries's 30-day series. Includes SELECT EXCLUDE, prefix aliasing, reusable aliases across clauses, LIMIT with percentages, QUALIFY for window function filtering, GROUP BY ALL, LIST comprehensions with Python-like syntax, lambda functions in SQL, dot operator chaining, glob patterns for multi-file queries, and direct file queries without CREATE TABLE." }, { "id": "the-xlwings-lite-ai-coder-instruction-file-december-2025-release", "slug": "the-xlwings-lite-ai-coder-instruction-file-december-2025-release", "title": "The xlwings Lite AI Coder Instruction File - December 2025 Release", "date_published": "2025-12-07T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-10T09:23:30.164226Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/the-xlwings-lite-ai-coder-instruction-file-december-2025-release", "thumbnail": "/images/blog/00_xlwings_logo-lite-light.svg", "status": "published", "tags": [ "xlwings-lite", "python-in-excel", "ai-coders" ], "markdown_file": "output/markdown-manually-fixed/the-xlwings-lite-ai-coder-instruction-file-december-2025-release.md", "html_file": "output/html-pages-manually-fixed/the-xlwings-lite-ai-coder-instruction-file-december-2025-release.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-10T09:23:30.164226Z", "scraped_from": "output/jina-ai-raw/the-xlwings-lite-ai-coder-instruction-file-december-2025-release.md", "round": 2 }, "youtube_ids": "", "summary": "1,867-line instruction file for AI coders writing xlwings Lite scripts. Contains 21 golden rules (e.g., never use .expand() on just-written data), InvalidArgument troubleshooting guide, custom function patterns, API stability workarounds, and complete limitation documentation. Built from five months of client work. Includes five production app examples: AI web scraper, technical analyst, MF holdings analyzer, database connector, and EDA+ML workflow." }, { "id": "try-text-to-sql-on-real-data-gb-files-multi-million-rows", "slug": "try-text-to-sql-on-real-data-gb-files-multi-million-rows", "title": "Try Text-to-SQL on Real Data - Multi-Million Rows & GB+ Sizes", "date_published": "2025-12-05T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-10T09:23:30.164226Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/try-text-to-sql-on-real-data-gb-files-multi-million-rows", "thumbnail": "https://static.wixstatic.com/media/ef0c19_aa27c441863b4bf69ed4b85a014ab325~mv2.png", "status": "published", "tags": [ "text-to-sql", "database-ai", "duckdb" ], "markdown_file": "output/markdown-manually-fixed/try-text-to-sql-on-real-data-gb-files-multi-million-rows.md", "html_file": "output/html-pages-manually-fixed/try-text-to-sql-on-real-data-gb-files-multi-million-rows.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-10T09:23:30.164226Z", "scraped_from": "output/jina-ai-raw/try-text-to-sql-on-real-data-gb-files-multi-million-rows.md", "round": 2 }, "youtube_ids": "", "summary": "DATS-4 text-to-SQL app with sample datasets from 64 rows (14KB) to 11.8M rows (1.6GB). Two-click setup creates temporary Postgres database via Neon API, uploads data, and connects AI agent. Nine LLM options from Gemini 2.0 Flash to Claude 4.5 Sonnet. Features dual agents (general and advanced), file uploads, working tables, CSV export, interactive table viewer, Python charts via E2B, and PDF output. Open source with seven GitHub repos." }, { "id": "think-about-it-one-of-the-world-s-top-ai-researchers-is-building-tools-deploying-them-live", "slug": "think-about-it-one-of-the-world-s-top-ai-researchers-is-building-tools-deploying-them-live", "title": "Think about it. One of the world's top AI researchers is building tools. Deploying them live.", "date_published": "2025-12-02T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-10T09:23:30.164226Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/think-about-it-one-of-the-world-s-top-ai-researchers-is-building-tools-deploying-them-live", "thumbnail": "https://static.wixstatic.com/media/ef0c19_a75e097c82e84a918b7842fb98333638~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/markdown-manually-fixed/think-about-it-one-of-the-world-s-top-ai-researchers-is-building-tools-deploying-them-live.md", "html_file": "output/html-pages-manually-fixed/think-about-it-one-of-the-world-s-top-ai-researchers-is-building-tools-deploying-them-live.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-10T09:23:30.164226Z", "scraped_from": "output/jina-ai-raw/think-about-it-one-of-the-world-s-top-ai-researchers-is-building-tools-deploying-them-live.md", "round": 2 }, "youtube_ids": "", "summary": "Commentary on Andrew Ng releasing an Agentic Reviewer for research papers, arguing that AI coders (Claude Code, Cursor) have removed barriers for domain experts to build and deploy tools. Author built 30+ apps at tigzig.com using AI coders over two years. Recommends starting with Claude Code ($20/month) or free Google Antigravity, with YouTube learning resources from Volo Builds, Leon Van Zyl, and Mark Kashef." }, { "id": "bitcoin-down-nearly-30-in-25-days-what-does-ai-technical-analysis-say", "slug": "bitcoin-down-nearly-30-in-25-days-what-does-ai-technical-analysis-say", "title": "Bitcoin Down nearly 30% in 25 days. What Does AI Technical Analysis Say?", "date_published": "2025-12-02T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-10T09:23:30.164226Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/bitcoin-down-nearly-30-in-25-days-what-does-ai-technical-analysis-say", "thumbnail": "https://static.wixstatic.com/media/ef0c19_54058c6a45c24bed8c4c4de481e5fb3d~mv2.png", "status": "published", "tags": [ "technical-analysis" ], "markdown_file": "output/markdown-manually-fixed/bitcoin-down-nearly-30-in-25-days-what-does-ai-technical-analysis-say.md", "html_file": "output/html-pages-manually-fixed/bitcoin-down-nearly-30-in-25-days-what-does-ai-technical-analysis-say.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-10T09:23:30.164226Z", "scraped_from": "output/jina-ai-raw/bitcoin-down-nearly-30-in-25-days-what-does-ai-technical-analysis-say.md", "round": 2 }, "youtube_ids": "", "summary": "Demonstrates the quants agent at quants.tigzig.com generating AI technical analysis for Bitcoin during a 30% decline. Default model Gemini 2.5 Flash produces reports in 30-45 seconds; premium models (GPT 5.1, Claude Sonnet 4.5) take 2-4 minutes but catch more patterns. Reports include daily and weekly charts with AI interpretation. Author notes disagreement with the AI's outlook as example of using it as one input among many. Open source." }, { "id": "andrew-ng-is-using-claude-code-openai-codex-gemini-cli", "slug": "andrew-ng-is-using-claude-code-openai-codex-gemini-cli", "title": "Andrew Ng is using Claude Code, OpenAI Codex, Gemini CLI.", "date_published": "2025-12-02T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-10T09:23:30.164226Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/andrew-ng-is-using-claude-code-openai-codex-gemini-cli", "thumbnail": "https://static.wixstatic.com/media/ef0c19_3f0a7e8087b74cffab39e5375c8c79a0~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/markdown-manually-fixed/andrew-ng-is-using-claude-code-openai-codex-gemini-cli.md", "html_file": "output/html-pages-manually-fixed/andrew-ng-is-using-claude-code-openai-codex-gemini-cli.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-10T09:23:30.164226Z", "scraped_from": "output/jina-ai-raw/andrew-ng-is-using-claude-code-openai-codex-gemini-cli.md", "round": 2 }, "youtube_ids": "", "summary": "AI coding tools for analysts and data scientists, referencing Andrew Ng's adoption of Claude Code, OpenAI Codex, and Gemini CLI. Covers what agentic coding tools enable (React apps, database backends, dashboards), recommended tools (Cursor, Claude Code, Gemini CLI, Mito AI), free starting options via Google Antigravity, and practical YouTube learning resources." }, { "id": "gold-up-2-3x-past-2-years-but-what-about-the-10-years-drawdown-in-between-as-buffett-says", "slug": "gold-up-2-3x-past-2-years-but-what-about-the-10-years-drawdown-in-between-as-buffett-says", "title": "Gold up 2.3X past 2 years. But what about the 10 years drawdown in between.", "date_published": "2025-12-02T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-10T09:23:30.164226Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/gold-up-2-3x-past-2-years-but-what-about-the-10-years-drawdown-in-between-as-buffett-says", "thumbnail": "https://static.wixstatic.com/media/ef0c19_73cae4873d7d416c9f62c955372d148a~mv2.png", "status": "published", "tags": [ "portfolio-analytics" ], "markdown_file": "output/markdown-manually-fixed/gold-up-2-3x-past-2-years-but-what-about-the-10-years-drawdown-in-between-as-buffett-says.md", "html_file": "output/html-pages-manually-fixed/gold-up-2-3x-past-2-years-but-what-about-the-10-years-drawdown-in-between-as-buffett-says.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-10T09:23:30.164226Z", "scraped_from": "output/jina-ai-raw/gold-up-2-3x-past-2-years-but-what-about-the-10-years-drawdown-in-between-as-buffett-says.md", "round": 2 }, "youtube_ids": "", "summary": "Analysis of gold's historical drawdown risk using the open-source Quants Agent at quants.tigzig.com. Despite gold's 2.3X rise over 2 years, the post highlights the brutal 10-year drawdown from 2011 to 2020. References Buffett's 2011 letter on gold as a non-productive asset and Goldman Sachs forecasts. Includes SPR report generation instructions." }, { "id": "enhancement-ai-technical-analysis-now-supports-multiple-llm-choices", "slug": "enhancement-ai-technical-analysis-now-supports-multiple-llm-choices", "title": "[ENHANCEMENT] AI Technical Analysis Now Supports Multiple LLM Choices", "date_published": "2025-11-28T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-10T09:23:30.164226Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/enhancement-ai-technical-analysis-now-supports-multiple-llm-choices", "thumbnail": null, "status": "published", "tags": [ "technical-analysis" ], "markdown_file": "output/markdown-manually-fixed/enhancement-ai-technical-analysis-now-supports-multiple-llm-choices.md", "html_file": "output/html-pages-manually-fixed/enhancement-ai-technical-analysis-now-supports-multiple-llm-choices.html", "content_length": 0, "has_images": false, "has_videos": true, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-10T09:23:30.164226Z", "scraped_from": "output/jina-ai-raw/enhancement-ai-technical-analysis-now-supports-multiple-llm-choices.md", "round": 2 }, "youtube_ids": "nLUoS_l0KRo", "summary": "Feature update to the AI Technical Analysis tool at quants-suite.tigzig.com adding multiple LLM options across three tiers: fast (Gemini 2.5 Flash/Lite, ~30 seconds), mid (GPT 4.1/Mini/Nano, Haiku 4.5, ~1 min), and premium (GPT 5.1, Claude Sonnet 4.5, 2-5 mins). Generates daily and weekly technical analysis reports in PDF and HTML." }, { "id": "run-advanced-analytics-locally-in-your-browser-no-server-no-remote-database-no-it-approvals", "slug": "run-advanced-analytics-locally-in-your-browser-no-server-no-remote-database-no-it-approvals", "title": "Segment 1M customers from 10M transactions (640MB CSV) with natural language queries / Text-to-SQL - entirely in your browser. No server. No remote database. No IT approvals.", "date_published": "2025-11-26T06:57:31.030Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.653724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/run-advanced-analytics-locally-in-your-browser-no-server-no-remote-database-no-it-approvals", "thumbnail": "https://static.wixstatic.com/media/ef0c19_26f12f145f344dafb1b79d5dbf80df9b~mv2.png", "status": "published", "tags": [ "duckdb", "text-to-sql" ], "markdown_file": "output/staging-markdown/run-advanced-analytics-locally-in-your-browser-no-server-no-remote-database-no-it-approvals.md", "html_file": "output/html-pages/run-advanced-analytics-locally-in-your-browser-no-server-no-remote-database-no-it-approvals.html", "content_length": 9082, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 9863, "html": 12717 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.197466Z", "scraped_from": "output/jina-ai-raw/run-advanced-analytics-locally-in-your-browser-no-server-no-remote-database-no-it-approvals.md" }, "youtube_ids": "2j1DziSQb3c", "summary": "Browser-based analytics tool (DABX-1) using DuckDB-WASM and text-to-SQL AI for processing multi-GB files locally. Demonstrated segmenting 1M customers from 10M transactions (640MB CSV) entirely in-browser. Built on SQL Rooms framework. Available as a 3.5MB single HTML file. Supports CSV, TSV, Parquet. Data never leaves the machine." }, { "id": "google-antigravity-just-launched-for-analysts-and-data-scientists-worth-adding-to-your-toolkit", "slug": "google-antigravity-just-launched-for-analysts-and-data-scientists-worth-adding-to-your-toolkit", "title": "Google Antigravity just launched. For analysts and data scientists: Worth adding to your toolkit", "date_published": "2025-11-24T14:18:43.128Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.642099Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/google-antigravity-just-launched-for-analysts-and-data-scientists-worth-adding-to-your-toolkit", "thumbnail": null, "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/google-antigravity-just-launched-for-analysts-and-data-scientists-worth-adding-to-your-toolkit.md", "html_file": "output/html-pages/google-antigravity-just-launched-for-analysts-and-data-scientists-worth-adding-to-your-toolkit.html", "content_length": 2499, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2993, "html": 4862 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.155271Z", "scraped_from": "output/jina-ai-raw/google-antigravity-just-launched-for-analysts-and-data-scientists-worth-adding-to-your-toolkit.md" }, "youtube_ids": "", "summary": "Review of Google Antigravity, a free agentic IDE built on VS Code with Gemini 3 Pro, Claude Sonnet 4.5, and GPT-OSS built in. Tested for low-to-medium complexity coding tasks. Compared against Cursor and Claude Code as primary tools. Covers rate limits encountered and practical positioning as a supplementary AI coding tool." }, { "id": "execute-asap-approval-granted-google-vs-microsoft-meta", "slug": "execute-asap-approval-granted-google-vs-microsoft-meta", "title": "Execute ASAP. Approval granted. Google - against Microsoft & Meta past 15 years, benchmark vs. S&P 500, technicals & quarterlies for all three.", "date_published": "2025-11-23T08:49:23.134Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.638926Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/execute-asap-approval-granted-google-vs-microsoft-meta", "thumbnail": null, "status": "published", "tags": [ "portfolio-analytics", "technical-analysis" ], "markdown_file": "output/staging-markdown/execute-asap-approval-granted-google-vs-microsoft-meta.md", "html_file": "output/html-pages/execute-asap-approval-granted-google-vs-microsoft-meta.html", "content_length": 1880, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 3488, "html": 7130 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.147388Z", "scraped_from": "output/jina-ai-raw/execute-asap-approval-granted-google-vs-microsoft-meta.md" }, "youtube_ids": "", "summary": "Demonstration of the Quants Agent at quants.tigzig.com generating a full financial report pack (Security Performance Report, AI Technical Analysis, QuantStats, Financials) for Google vs Microsoft vs Meta benchmarked against S&P 500 over 15 years. Single natural language prompt produces PDF, HTML, and CSV outputs in approximately 2 minutes." }, { "id": "gemini-3-pro-added-to-database-ai-suite-tested-against-claude-sonnet-4-5-and-gpt-5-1-results-claud", "slug": "gemini-3-pro-added-to-database-ai-suite-tested-against-claude-sonnet-4-5-and-gpt-5-1-results-claud", "title": "Gemini 3 Pro on Database AI Suite - Claude 4.5 still leads", "date_published": "2025-11-21T04:56:27.578Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.641097Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/gemini-3-pro-added-to-database-ai-suite-tested-against-claude-sonnet-4-5-and-gpt-5-1-results-claud", "thumbnail": "https://static.wixstatic.com/media/ef0c19_e17e736acee842c0a2b911a91846456b~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/gemini-3-pro-added-to-database-ai-suite-tested-against-claude-sonnet-4-5-and-gpt-5-1-results-claud.md", "html_file": "output/html-pages/gemini-3-pro-added-to-database-ai-suite-tested-against-claude-sonnet-4-5-and-gpt-5-1-results-claud.html", "content_length": 3216, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 3997, "html": 6248 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.152388Z", "scraped_from": "output/jina-ai-raw/gemini-3-pro-added-to-database-ai-suite-tested-against-claude-sonnet-4-5-and-gpt-5-1-results-claud.md" }, "youtube_ids": "", "summary": "Benchmark of Gemini 3 Pro against Claude Sonnet 4.5 and GPT-5.1 for multi-step database analysis in DATS-4. Scoring: Claude 115, GPT-5.1 100, Gemini 3 Pro 90. Tested on 1M customer + 10M transaction credit card analysis on AWS RDS MySQL. Includes detailed cost breakdown per 100 questions across reasoning and execution tiers." }, { "id": "two-models-added-to-database-ai-suite-this-week-gpt-5-1-and-kimi-2-thinking", "slug": "two-models-added-to-database-ai-suite-this-week-gpt-5-1-and-kimi-2-thinking", "title": "Two models added to Database AI Suite this week: GPT-5.1 and KIMI 2 Thinking.", "date_published": "2025-11-18T12:54:23.995Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.655725Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/two-models-added-to-database-ai-suite-this-week-gpt-5-1-and-kimi-2-thinking", "thumbnail": "https://static.wixstatic.com/media/ef0c19_e9bb0b27e5624f6bad07eda0cee3bd34~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/two-models-added-to-database-ai-suite-this-week-gpt-5-1-and-kimi-2-thinking.md", "html_file": "output/html-pages/two-models-added-to-database-ai-suite-this-week-gpt-5-1-and-kimi-2-thinking.html", "content_length": 1886, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2442, "html": 4187 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.203464Z", "scraped_from": "output/jina-ai-raw/two-models-added-to-database-ai-suite-this-week-gpt-5-1-and-kimi-2-thinking.md" }, "youtube_ids": "4Fnjt_L2S1g", "summary": "Evaluation of GPT-5.1 and KIMI 2 Thinking added to DATS-4 Database AI Suite. Covers reasoning model recommendations for advanced analysis planning and execution cost breakdown. GPT-5.1 is 20% cheaper than GPT-5 with reduced token bloat. GPT-4.1-mini recommended as execution workhorse. Multi-step workflows cost approximately $15 per 100 questions." }, { "id": "bundle-your-ai-app-or-react-dashboard-into-a-single-file", "slug": "bundle-your-ai-app-or-react-dashboard-into-a-single-file", "title": "Bundle your AI app or React dashboard into a single file.", "date_published": "2025-11-18T12:50:19.192Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.634411Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/bundle-your-ai-app-or-react-dashboard-into-a-single-file", "thumbnail": "https://static.wixstatic.com/media/ef0c19_a4d7b66f1fee433b84a66dbfbf8e3ccd~mv2.png", "status": "published", "tags": [ "infrastructure", "react" ], "markdown_file": "output/staging-markdown/bundle-your-ai-app-or-react-dashboard-into-a-single-file.md", "html_file": "output/html-pages/bundle-your-ai-app-or-react-dashboard-into-a-single-file.html", "content_length": 1240, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 1706, "html": 3449 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.129885Z", "scraped_from": "output/jina-ai-raw/bundle-your-ai-app-or-react-dashboard-into-a-single-file.md" }, "youtube_ids": "", "summary": "Guide to bundling React apps and AI tools into single portable HTML files using vite-plugin-singlefile. No server, hosting, or IT approvals needed. Two live examples: a 3.5MB Database AI app with DuckDB for multi-GB file analysis, and a 150KB mutual fund analysis dashboard. Covers practical applications for sharing prototypes and internal dashboards." }, { "id": "ai-coders-are-here-the-edge-now-is-domain-execution-not-vibing", "slug": "ai-coders-are-here-the-edge-now-is-domain-execution-not-vibing", "title": "AI Coders are here. The edge now is domain + execution. Not vibing.", "date_published": "2025-11-18T12:45:13.735Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.629424Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-coders-are-here-the-edge-now-is-domain-execution-not-vibing", "thumbnail": null, "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/ai-coders-are-here-the-edge-now-is-domain-execution-not-vibing.md", "html_file": "output/html-pages/ai-coders-are-here-the-edge-now-is-domain-execution-not-vibing.html", "content_length": 1742, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2146, "html": 3942 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.108400Z", "scraped_from": "output/jina-ai-raw/ai-coders-are-here-the-edge-now-is-domain-execution-not-vibing.md" }, "youtube_ids": "", "summary": "Opinion piece arguing that domain expertise and execution, not prompt engineering, provide the real competitive edge with AI coders. Synthesizes three perspectives: an investor (Francois Arbour on the 10x lie), a strategist (Saanya Ojha on domain specialists), and hands-on practitioner experience. Recommends Cursor, Claude Code, Gemini CLI over ChatGPT copy-paste." }, { "id": "the-google-machine-continues-to-roll-will-it-do-to-ai-what-it-did-to-search", "slug": "the-google-machine-continues-to-roll-will-it-do-to-ai-what-it-did-to-search", "title": "The Google Machine continues to roll. Will it do to AI what it did to search?", "date_published": "2025-11-18T12:40:05.685Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.654724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/the-google-machine-continues-to-roll-will-it-do-to-ai-what-it-did-to-search", "thumbnail": "https://static.wixstatic.com/media/ef0c19_82b1b0c8da894614b8336f799958c47d~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/the-google-machine-continues-to-roll-will-it-do-to-ai-what-it-did-to-search.md", "html_file": "output/html-pages/the-google-machine-continues-to-roll-will-it-do-to-ai-what-it-did-to-search.html", "content_length": 2651, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 3189, "html": 4935 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.201464Z", "scraped_from": "output/jina-ai-raw/the-google-machine-continues-to-roll-will-it-do-to-ai-what-it-did-to-search.md" }, "youtube_ids": "", "summary": "Assessment of Google's AI tool ecosystem and competitive position. Covers practical tools in use: Gemini 2.5 Flash Lite for automations, AI Studio for prototyping, Gemini CLI as an AI coder, MCP for Chrome DevTools, MCP Toolbox for databases, and File Search API. Notes Google leads in the workhorse tier but trails Claude/OpenAI at the frontier." }, { "id": "instant-database-setup-for-ai-apps-with-neon-com", "slug": "instant-database-setup-for-ai-apps-with-neon-com", "title": "Instant Database Setup for AI Apps. With Neon.com", "date_published": "2025-11-18T12:31:51.358Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.645948Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/instant-database-setup-for-ai-apps-with-neon-com", "thumbnail": null, "status": "published", "tags": [ "database-ai", "infrastructure" ], "markdown_file": "output/staging-markdown/instant-database-setup-for-ai-apps-with-neon-com.md", "html_file": "output/html-pages/instant-database-setup-for-ai-apps-with-neon-com.html", "content_length": 1492, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 1845, "html": 3653 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.166301Z", "scraped_from": "output/jina-ai-raw/instant-database-setup-for-ai-apps-with-neon-com.md" }, "youtube_ids": "", "summary": "Guide to using Neon.com for instant Postgres database provisioning via API in under 1 second. Used in the DATS-4 app for on-demand database creation when users upload CSV files. Covers the full workflow from CSV upload to AI-ready database. Notes Neon's free tier supports up to 30 projects with 15GB total storage. References Replit, Retool, and Vercel as large-scale users." }, { "id": "ai-coders-give-you-the-edge-the-6-rules-i-follow-when-working-with-ai-coders", "slug": "ai-coders-give-you-the-edge-the-6-rules-i-follow-when-working-with-ai-coders", "title": "AI Coders give you the edge.The 6 Rules I Follow When Working with AI Coders.", "date_published": "2025-11-18T12:24:59.197Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.630426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-coders-give-you-the-edge-the-6-rules-i-follow-when-working-with-ai-coders", "thumbnail": null, "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/ai-coders-give-you-the-edge-the-6-rules-i-follow-when-working-with-ai-coders.md", "html_file": "output/html-pages/ai-coders-give-you-the-edge-the-6-rules-i-follow-when-working-with-ai-coders.html", "content_length": 2469, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3358, "html": 5373 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.109389Z", "scraped_from": "output/jina-ai-raw/ai-coders-give-you-the-edge-the-6-rules-i-follow-when-working-with-ai-coders.md" }, "youtube_ids": "", "summary": "Six practical rules for working with AI coding tools (Cursor, Claude Code, Gemini CLI, Mito AI): provide full context, state requirements clearly, demand plans before execution, iterate step by step, validate and stress-test outputs, and accept the grind remains. Based on 25+ years of coding experience transitioning to AI-assisted development." }, { "id": "coding-by-hand-is-becoming-obsolete-andrew-ng-i-disagree", "slug": "coding-by-hand-is-becoming-obsolete-andrew-ng-i-disagree", "title": "Coding by hand is becoming obsolete - Andrew Ng. I disagree.", "date_published": "2025-11-05T06:34:38.583Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.636921Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/coding-by-hand-is-becoming-obsolete-andrew-ng-i-disagree", "thumbnail": "https://static.wixstatic.com/media/ef0c19_0382fc9d461042f4958113df679d43e3~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/coding-by-hand-is-becoming-obsolete-andrew-ng-i-disagree.md", "html_file": "output/html-pages/coding-by-hand-is-becoming-obsolete-andrew-ng-i-disagree.html", "content_length": 1999, "has_images": false, "has_videos": true, "file_sizes": { "markdown": 2497, "html": 4295 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.136346Z", "scraped_from": "output/jina-ai-raw/coding-by-hand-is-becoming-obsolete-andrew-ng-i-disagree.md" }, "youtube_ids": "", "summary": "Argument that manual coding is already obsolete for analysts and data scientists, building on Andrew Ng's statement. Covers the transition from SAS/Python/SQL/VBA to AI-assisted coding with Claude Code, Cursor, and Gemini CLI. Recommends starting with ChatGPT or Google AI Studio, then moving to VS Code + Gemini CLI, Cursor, or Claude Code." }, { "id": "intelligent-ai-web-scraper-in-excel-with-python-xlwings-lite", "slug": "intelligent-ai-web-scraper-in-excel-with-python-xlwings-lite", "title": "Intelligent AI Web Scraper in Excel with Python (xlwings Lite)", "date_published": "2025-11-03T08:57:18.328Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.645948Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/intelligent-ai-web-scraper-in-excel-with-python-xlwings-lite", "thumbnail": "https://static.wixstatic.com/media/ef0c19_13de74fb48054981af8505551bfef87e~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/intelligent-ai-web-scraper-in-excel-with-python-xlwings-lite.md", "html_file": "output/html-pages/intelligent-ai-web-scraper-in-excel-with-python-xlwings-lite.html", "content_length": 1684, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2169, "html": 3864 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.167301Z", "scraped_from": "output/jina-ai-raw/intelligent-ai-web-scraper-in-excel-with-python-xlwings-lite.md" }, "youtube_ids": "41ZX46DibV4", "summary": "AI-powered web scraper built in Excel using xlwings Lite, Jina.ai for content extraction, and Gemini for intelligent data transformation. Accepts natural language instructions for filtering, selecting, normalizing, and deriving fields. Configurable LLM parameters (topP, temperature, thinking budget) and scrape settings. Outputs structured data with status logs and a 30-KPI dashboard." }, { "id": "database-ai-built-for-day-to-day-work-five-categories-ten-micro-apps-live-open-source-free", "slug": "database-ai-built-for-day-to-day-work-five-categories-ten-micro-apps-live-open-source-free", "title": "Database AI, built for day-to-day work. Five categories, ten micro apps. Live, open source, free.", "date_published": "2025-10-23T13:09:40.551Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.637926Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/database-ai-built-for-day-to-day-work-five-categories-ten-micro-apps-live-open-source-free", "thumbnail": null, "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/database-ai-built-for-day-to-day-work-five-categories-ten-micro-apps-live-open-source-free.md", "html_file": "output/html-pages/database-ai-built-for-day-to-day-work-five-categories-ten-micro-apps-live-open-source-free.html", "content_length": 2337, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2889, "html": 5035 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.142839Z", "scraped_from": "output/jina-ai-raw/database-ai-built-for-day-to-day-work-five-categories-ten-micro-apps-live-open-source-free.md" }, "youtube_ids": "", "summary": "Overview of 10 open-source Database AI micro-apps across 5 categories: Custom Builds, Rapid Deploy, ChatGPT, Realtime Voice, and xlwings Lite. All support natural language to SQL for Postgres, MySQL, and DuckDB. Includes chart visualization, table transformation, and on-the-fly database connections. Modular architecture allows mixing frontend and backend components." }, { "id": "run-a-full-ai-database-app-as-a-single-html-file-no-server-no-remote-db", "slug": "run-a-full-ai-database-app-as-a-single-html-file-no-server-no-remote-db", "title": "Run a Full AI Database App as a Single HTML File. No Server. No Remote DB.", "date_published": "2025-10-19T07:08:32.487Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.653724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/run-a-full-ai-database-app-as-a-single-html-file-no-server-no-remote-db", "thumbnail": "https://static.wixstatic.com/media/ef0c19_3f103056302941aa8b53a675a14cc8c1~mv2.png", "status": "published", "tags": [ "database-ai", "duckdb" ], "markdown_file": "output/staging-markdown/run-a-full-ai-database-app-as-a-single-html-file-no-server-no-remote-db.md", "html_file": "output/html-pages/run-a-full-ai-database-app-as-a-single-html-file-no-server-no-remote-db.html", "content_length": 1648, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2191, "html": 4035 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.195464Z", "scraped_from": "output/jina-ai-raw/run-a-full-ai-database-app-as-a-single-html-file-no-server-no-remote-db.md" }, "youtube_ids": "VxyZiK87vik", "summary": "Single-file deployment of a full AI database app based on SQL Rooms and DuckDB-WASM. The entire React application compiles into a portable 3.5MB HTML file. Demonstrated importing 1.6GB / 11M-row files for in-browser analysis. Built using vite-plugin-singlefile. Supports Gemini API for natural language querying. No backend or server required." }, { "id": "chat-query-and-transform-multi-gb-files-in-natural-language-right-in-your-browser-with-duckdb", "slug": "chat-query-and-transform-multi-gb-files-in-natural-language-right-in-your-browser-with-duckdb", "title": "Chat, Query, and Transform Multi-GB Files - In Natural Language, Right in Your Browser with DuckDB.", "date_published": "2025-10-17T10:55:38.288Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.635916Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/chat-query-and-transform-multi-gb-files-in-natural-language-right-in-your-browser-with-duckdb", "thumbnail": "https://static.wixstatic.com/media/ef0c19_8a0bdc92053c43cbb1b02d0098423d16~mv2.png", "status": "published", "tags": [ "duckdb", "text-to-sql" ], "markdown_file": "output/staging-markdown/chat-query-and-transform-multi-gb-files-in-natural-language-right-in-your-browser-with-duckdb.md", "html_file": "output/html-pages/chat-query-and-transform-multi-gb-files-in-natural-language-right-in-your-browser-with-duckdb.html", "content_length": 2172, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2858, "html": 4814 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.131885Z", "scraped_from": "output/jina-ai-raw/chat-query-and-transform-multi-gb-files-in-natural-language-right-in-your-browser-with-duckdb.md" }, "youtube_ids": "9hLGjWzVYs0", "summary": "In-browser analytics tool using DuckDB-WASM and SQL Rooms for querying multi-GB files via natural language. Demonstrated analyzing a 1.6GB, 11M-row CSV file entirely locally. Supports CSV, TSV, pipe-delimited, and Parquet files. Data stays in browser; only schema and sample rows sent to LLM. Supports Gemini, OpenAI, and Claude APIs. Open source." }, { "id": "automated-analytics-reporting-with-python-in-excel-xlwings-lite-build-once-reuse-anywhere", "slug": "automated-analytics-reporting-with-python-in-excel-xlwings-lite-build-once-reuse-anywhere", "title": "Automated Analytics & Reporting with Python in Excel (xlwings Lite). Build Once - Reuse Anywhere.", "date_published": "2025-10-12T11:17:33.630Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.632426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/automated-analytics-reporting-with-python-in-excel-xlwings-lite-build-once-reuse-anywhere", "thumbnail": "https://static.wixstatic.com/media/ef0c19_6cc929cc0b4843e1864f515272246f4a~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel", "technical-analysis" ], "markdown_file": "output/staging-markdown/automated-analytics-reporting-with-python-in-excel-xlwings-lite-build-once-reuse-anywhere.md", "html_file": "output/html-pages/automated-analytics-reporting-with-python-in-excel-xlwings-lite-build-once-reuse-anywhere.html", "content_length": 1406, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2010, "html": 3910 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.118387Z", "scraped_from": "output/jina-ai-raw/automated-analytics-reporting-with-python-in-excel-xlwings-lite-build-once-reuse-anywhere.md" }, "youtube_ids": "yK3vHkw-8XQ", "summary": "Reusable architecture for automated PDF and HTML report generation using xlwings Lite. Demonstrates an AI-driven technical analysis report for Yahoo Finance symbols in 45 seconds. Covers 9 reusable building blocks: API data fetching, Python transformations, Matplotlib charting, multi-modal AI calls, markdown-to-report assembly, and backend URL generation." }, { "id": "new-open-source-tool-mutual-funds-holdings-analyzer-python-in-excel-xlwings-lite-now-live", "slug": "new-open-source-tool-mutual-funds-holdings-analyzer-python-in-excel-xlwings-lite-now-live", "title": "New Open Source Tool. Mutual Funds Holdings Analyzer. Python in Excel (xlwings Lite). Now Live.", "date_published": "2025-10-12T11:05:33.328Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.648202Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/new-open-source-tool-mutual-funds-holdings-analyzer-python-in-excel-xlwings-lite-now-live", "thumbnail": "https://static.wixstatic.com/media/ef0c19_e4b591035caa429090def6980368732a~mv2.png", "status": "published", "tags": [ "mutual-funds", "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/new-open-source-tool-mutual-funds-holdings-analyzer-python-in-excel-xlwings-lite-now-live.md", "html_file": "output/html-pages/new-open-source-tool-mutual-funds-holdings-analyzer-python-in-excel-xlwings-lite-now-live.html", "content_length": 1608, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2192, "html": 3927 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.174303Z", "scraped_from": "output/jina-ai-raw/new-open-source-tool-mutual-funds-holdings-analyzer-python-in-excel-xlwings-lite-now-live.md" }, "youtube_ids": "hJPkjVZAreE", "summary": "Open-source xlwings Lite tool for standardizing and comparing Indian mutual fund portfolio disclosure data. Handles ISIN-based name standardization, corporate action merging, and human-in-the-loop data quality review. Two-stage pipeline: Stage 1 generates data quality reports, Stage 2 produces final analysis after manual review. Built with Gemini CLI." }, { "id": "quants-suite-5-reports-performance-risk-technical-analytics", "slug": "quants-suite-5-reports-performance-risk-technical-analytics", "title": "Quants Suite. 5 Reports. Performance, Risk & Technical Analytics.", "date_published": "2025-10-12T10:51:10.695Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.651211Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/quants-suite-5-reports-performance-risk-technical-analytics", "thumbnail": "https://static.wixstatic.com/media/ef0c19_383df4f60f4c43bbbabfbb03f378ed53~mv2.png", "status": "published", "tags": [ "portfolio-analytics", "technical-analysis" ], "markdown_file": "output/staging-markdown/quants-suite-5-reports-performance-risk-technical-analytics.md", "html_file": "output/html-pages/quants-suite-5-reports-performance-risk-technical-analytics.html", "content_length": 2150, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2765, "html": 4682 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.187464Z", "scraped_from": "output/jina-ai-raw/quants-suite-5-reports-performance-risk-technical-analytics.md" }, "youtube_ids": "", "summary": "Five-report quantitative analytics suite at quants-suite.tigzig.com covering stocks, indices, commodities, and crypto. Reports include: Security Performance (CAGR, Sharpe, Sortino, drawdowns), AI Technical Analysis (EMAs, Bollinger, MACD, RSI with Gemini analysis), QuantStats (70+ metrics), Yahoo Finance financials (P&L, B/S, cash flow), and price data downloads." }, { "id": "connect-chatgpt-to-multiple-databases", "slug": "connect-chatgpt-to-multiple-databases", "title": "Connect ChatGPT to Multiple Remote Databases", "date_published": "2025-10-12T10:40:57.252Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.636921Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/connect-chatgpt-to-multiple-databases", "thumbnail": "https://static.wixstatic.com/media/ef0c19_4344f5bff19644ae8fcecdf7487824ad~mv2.png", "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/connect-chatgpt-to-multiple-databases.md", "html_file": "output/html-pages/connect-chatgpt-to-multiple-databases.html", "content_length": 2135, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2614, "html": 4513 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.138713Z", "scraped_from": "output/jina-ai-raw/connect-chatgpt-to-multiple-databases.md" }, "youtube_ids": "hY4X2NkPG4Q", "summary": "Architecture for connecting a Custom GPT to multiple remote Postgres databases (Supabase, Neon, Aiven) via FastAPI. Uses function calling with OpenAPI schema, YAML semantic layer for schema context, and routing rules. Demonstrated querying 2.5M rows across 340MB of cricket (ODI/T20) and Tour de France data spanning 122 years. Open source with full repo." }, { "id": "sonnet-4-5-released-yesterday-now-live-on-dats-4-sql-agent-suite-solid-upgrade-but-more-4-2-than", "slug": "sonnet-4-5-released-yesterday-now-live-on-dats-4-sql-agent-suite-solid-upgrade-but-more-4-2-than", "title": "Sonnet 4.5. Released yesterday. Now live on DATS-4 SQL Agent Suite. Solid upgrade, but more 4.2 than 4.5.", "date_published": "2025-10-12T10:19:35.772Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.654724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/sonnet-4-5-released-yesterday-now-live-on-dats-4-sql-agent-suite-solid-upgrade-but-more-4-2-than", "thumbnail": "https://static.wixstatic.com/media/ef0c19_298bc1be8d0445b8868c73015fb57d4e~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/sonnet-4-5-released-yesterday-now-live-on-dats-4-sql-agent-suite-solid-upgrade-but-more-4-2-than.md", "html_file": "output/html-pages/sonnet-4-5-released-yesterday-now-live-on-dats-4-sql-agent-suite-solid-upgrade-but-more-4-2-than.html", "content_length": 1475, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2640, "html": 4510 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.199464Z", "scraped_from": "output/jina-ai-raw/sonnet-4-5-released-yesterday-now-live-on-dats-4-sql-agent-suite-solid-upgrade-but-more-4-2-than.md" }, "youtube_ids": "", "summary": "Evaluation of Claude Sonnet 4.5 for multi-step database analysis in DATS-4. Scores: Sonnet 4.5 (115), Sonnet 4 (100), GPT-5 (95), Qwen3 Max (90), DeepSeek R1 (85). Same cost as Sonnet 4. Tested on credit card data mart builds (1M customers, 10M transactions on AWS RDS MySQL) and RBI weighted scoring. Includes per-100-question cost analysis." }, { "id": "which-ai-coder-should-you-use-for-xlwings-lite-python-in-excel", "slug": "which-ai-coder-should-you-use-for-xlwings-lite-python-in-excel", "title": "Which AI Coder should you use for xlwings Lite (Python in Excel)?", "date_published": "2025-10-07T03:17:16.862Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.657724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/which-ai-coder-should-you-use-for-xlwings-lite-python-in-excel", "thumbnail": "https://static.wixstatic.com/media/ef0c19_009b173e541b4012bd70eb5114227975~mv2.png", "status": "published", "tags": [ "ai-coders", "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/which-ai-coder-should-you-use-for-xlwings-lite-python-in-excel.md", "html_file": "output/html-pages/which-ai-coder-should-you-use-for-xlwings-lite-python-in-excel.html", "content_length": 1658, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2152, "html": 3875 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.207464Z", "scraped_from": "output/jina-ai-raw/which-ai-coder-should-you-use-for-xlwings-lite-python-in-excel.md" }, "summary": "Recommendations for AI coding tools for xlwings Lite development. Beginners: Gemini 2.5 Pro on aistudio.google.com (free, 1M context). Heavy work: Claude Code or Cursor. Default choice: Gemini CLI for its strong free tier and simplicity. Emphasizes using a 1,855-line AI Coder Instruction File for reliable code generation. Compares ChatGPT, Claude, and Gemini CLI tradeoffs." }, { "id": "analysis-as-app-inside-india-s-top-midcap-funds-buys-sells-entries-and-exits-interactive-dashbo", "slug": "analysis-as-app-inside-india-s-top-midcap-funds-buys-sells-entries-and-exits-interactive-dashbo", "title": "Analysis-as-App: Inside India’s Top Midcap Funds: Buys, Sells, Entries and Exits. Interactive Dashboard Release (Analysis-as-App)", "date_published": "2025-09-27T11:26:21.501Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.631426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/analysis-as-app-inside-india-s-top-midcap-funds-buys-sells-entries-and-exits-interactive-dashbo", "thumbnail": "https://static.wixstatic.com/media/ef0c19_58969665c68442a8af1bbe9d5e5caaaf~mv2.png", "status": "published", "tags": [ "mutual-funds" ], "markdown_file": "output/staging-markdown/analysis-as-app-inside-india-s-top-midcap-funds-buys-sells-entries-and-exits-interactive-dashbo.md", "html_file": "output/html-pages/analysis-as-app-inside-india-s-top-midcap-funds-buys-sells-entries-and-exits-interactive-dashbo.html", "content_length": 1379, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2281, "html": 4219 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.116391Z", "scraped_from": "output/jina-ai-raw/analysis-as-app-inside-india-s-top-midcap-funds-buys-sells-entries-and-exits-interactive-dashbo.md" }, "youtube_ids": "gCrOn5U7o5o", "summary": "Interactive dashboard analyzing portfolio changes across India's top 5 midcap mutual funds (Axis, HDFC, Kotak, Motilal, Nippon) managing ~54% of category AUM. Compares May vs August 2025 holdings. Built as a single 150KB HTML file using the MF Processor pipeline with GPT for ISIN standardization. Tracks buys, sells, entries, and exits." }, { "id": "google-the-old-edge-is-back-by-dec-24-in-ai-i-had-written-google-off-now-the-balance-has-shif", "slug": "google-the-old-edge-is-back-by-dec-24-in-ai-i-had-written-google-off-now-the-balance-has-shif", "title": "Google - The old edge is back.By Dec ’24, in AI, I had written Google off. Now, the balance has shifted", "date_published": "2025-09-23T13:13:57.341Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.643100Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/google-the-old-edge-is-back-by-dec-24-in-ai-i-had-written-google-off-now-the-balance-has-shif", "thumbnail": "https://static.wixstatic.com/media/ef0c19_90dff108966d407b85dc619091f9a7fc~mv2.jpg", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/google-the-old-edge-is-back-by-dec-24-in-ai-i-had-written-google-off-now-the-balance-has-shif.md", "html_file": "output/html-pages/google-the-old-edge-is-back-by-dec-24-in-ai-i-had-written-google-off-now-the-balance-has-shif.html", "content_length": 4631, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 5360, "html": 7432 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.158295Z", "scraped_from": "output/jina-ai-raw/google-the-old-edge-is-back-by-dec-24-in-ai-i-had-written-google-off-now-the-balance-has-shif.md" }, "youtube_ids": "QDUyH7BYkAw", "summary": "Assessment of Google's AI comeback from late 2024, when Gemini 2.0 Flash went from 50% error to 100% accuracy in schema detection tests. Covers practical Google tools in use: AI Studio, Gemini CLI (75K GitHub stars), Flash-2.5/Pro-2.5, NotebookLM, Google Search AI Mode, Opal, Database Toolbox, Code Interpreter, and ADK." }, { "id": "automated-quant-reports-with-gpt-run-a-stock-index-etf-commodity-or-crypto-get-3-formatted-re", "slug": "automated-quant-reports-with-gpt-run-a-stock-index-etf-commodity-or-crypto-get-3-formatted-re", "title": "Automated Quant Reports with GPT: Run a stock, index, ETF, commodity, or crypto → get 3 formatted reports in minutes.", "date_published": "2025-09-21T06:59:43.393Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.633412Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/automated-quant-reports-with-gpt-run-a-stock-index-etf-commodity-or-crypto-get-3-formatted-re", "thumbnail": "https://static.wixstatic.com/media/ef0c19_def00a07bd9344b489e4866d9d9f5266~mv2.png", "status": "published", "tags": [ "portfolio-analytics", "technical-analysis", "custom-gpt" ], "markdown_file": "output/staging-markdown/automated-quant-reports-with-gpt-run-a-stock-index-etf-commodity-or-crypto-get-3-formatted-re.md", "html_file": "output/html-pages/automated-quant-reports-with-gpt-run-a-stock-index-etf-commodity-or-crypto-get-3-formatted-re.html", "content_length": 2272, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 3264, "html": 5197 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.119878Z", "scraped_from": "output/jina-ai-raw/automated-quant-reports-with-gpt-run-a-stock-index-etf-commodity-or-crypto-get-3-formatted-re.md" }, "youtube_ids": "ev7nx8wCn_o", "summary": "Custom GPT generating three automated quantitative reports for any Yahoo Finance symbol: AI Technicals (daily/weekly charts with Gemini Flash analysis), Security Performance Report (CAGR, Sharpe, Sortino, drawdowns, monthly returns), and QuantStats (60+ KPIs, 10+ charts). All powered by open-source FastAPI-MCP backend servers. Outputs in PDF, HTML, and CSV." }, { "id": "monthly-mf-portfolio-files-hours-wasted-re-formatting-here-s-a-tool-that-fixes-it", "slug": "monthly-mf-portfolio-files-hours-wasted-re-formatting-here-s-a-tool-that-fixes-it", "title": "Monthly MF portfolio files = hours wasted re-formatting. Here’s a tool that fixes it", "date_published": "2025-09-19T13:23:15.876Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.647181Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/monthly-mf-portfolio-files-hours-wasted-re-formatting-here-s-a-tool-that-fixes-it", "thumbnail": null, "status": "published", "tags": [ "mutual-funds", "converters-tools" ], "markdown_file": "output/staging-markdown/monthly-mf-portfolio-files-hours-wasted-re-formatting-here-s-a-tool-that-fixes-it.md", "html_file": "output/html-pages/monthly-mf-portfolio-files-hours-wasted-re-formatting-here-s-a-tool-that-fixes-it.html", "content_length": 1266, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2024, "html": 3944 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.170301Z", "scraped_from": "output/jina-ai-raw/monthly-mf-portfolio-files-hours-wasted-re-formatting-here-s-a-tool-that-fixes-it.md" }, "youtube_ids": "I9MeITWJfqk", "summary": "Converter utility for Indian mutual fund monthly portfolio disclosure Excel files. Uses AI-powered schema detection to automatically identify data layouts, cross-validates with multiple models, and outputs clean CSVs with ISIN mapping and standardized names. Includes append, transpose, and audit trail utilities. Available at www.tigzig.com/mf-files-ai." }, { "id": "cricket-odi-t20-tour-de-france-stats-from-a-custom-gpt-connected-to-3-live-databases", "slug": "cricket-odi-t20-tour-de-france-stats-from-a-custom-gpt-connected-to-3-live-databases", "title": "Cricket (ODI/T20) & Tour de France stats from a Custom GPT connected to 3 live databases.", "date_published": "2025-09-17T09:22:00.650Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.637926Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/cricket-odi-t20-tour-de-france-stats-from-a-custom-gpt-connected-to-3-live-databases", "thumbnail": null, "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/cricket-odi-t20-tour-de-france-stats-from-a-custom-gpt-connected-to-3-live-databases.md", "html_file": "output/html-pages/cricket-odi-t20-tour-de-france-stats-from-a-custom-gpt-connected-to-3-live-databases.html", "content_length": 1788, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2853, "html": 4747 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.141643Z", "scraped_from": "output/jina-ai-raw/cricket-odi-t20-tour-de-france-stats-from-a-custom-gpt-connected-to-3-live-databases.md" }, "youtube_ids": "hY4X2NkPG4Q", "summary": "Custom GPT connected to three live Postgres databases (Supabase, Neon, Aiven) for querying 2.5M rows of cricket ODI/T20 ball-by-ball data (2003-2025) and 122 years of Tour de France history. Dynamically routes queries to the correct database. Computes player stats, rankings, charts. Open-source FastAPI backend with OAuth support." }, { "id": "can-an-ai-sql-agent-build-a-weighted-scoring-system-from-scratch", "slug": "can-an-ai-sql-agent-build-a-weighted-scoring-system-from-scratch", "title": "Can an AI SQL Agent build a weighted scoring system from scratch?", "date_published": "2025-09-15T11:57:27.508Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.634411Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/can-an-ai-sql-agent-build-a-weighted-scoring-system-from-scratch", "thumbnail": "https://static.wixstatic.com/media/ef0c19_c4ce94777ded4295a990e02e3167e355~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/can-an-ai-sql-agent-build-a-weighted-scoring-system-from-scratch.md", "html_file": "output/html-pages/can-an-ai-sql-agent-build-a-weighted-scoring-system-from-scratch.html", "content_length": 1921, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3096, "html": 4936 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.130885Z", "scraped_from": "output/jina-ai-raw/can-an-ai-sql-agent-build-a-weighted-scoring-system-from-scratch.md" }, "youtube_ids": "Q2-8Ky4Fp7g", "summary": "Walkthrough of using DATS-4 SQL Agent to build a weighted composite scoring system for ranking Indian banks on credit cards using RBI monthly data. Covers the full process: load data to temporary Postgres, instruct agent to derive variables and design scoring, review SQL reasoning, iterate on weights, and export as PDF report." }, { "id": "go-from-a-200mb-flat-file-with-1-5m-records-to-analysis-in-minutes-with-my-open-source-ai-sql-app", "slug": "go-from-a-200mb-flat-file-with-1-5m-records-to-analysis-in-minutes-with-my-open-source-ai-sql-app", "title": "Go from a 200MB flat file with 1.5M records to analysis in minutes with my open-source AI-SQL App", "date_published": "2025-09-15T11:55:04.690Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.642099Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/go-from-a-200mb-flat-file-with-1-5m-records-to-analysis-in-minutes-with-my-open-source-ai-sql-app", "thumbnail": "https://static.wixstatic.com/media/ef0c19_5a2359acd8894f598672fcc7eeab6e36~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/go-from-a-200mb-flat-file-with-1-5m-records-to-analysis-in-minutes-with-my-open-source-ai-sql-app.md", "html_file": "output/html-pages/go-from-a-200mb-flat-file-with-1-5m-records-to-analysis-in-minutes-with-my-open-source-ai-sql-app.html", "content_length": 1557, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2685, "html": 4514 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.154264Z", "scraped_from": "output/jina-ai-raw/go-from-a-200mb-flat-file-with-1-5m-records-to-analysis-in-minutes-with-my-open-source-ai-sql-app.md" }, "youtube_ids": "vrGSzzGmZDA", "summary": "Step-by-step guide to loading a 200MB, 1.5M-record ODI cricket dataset into a free Neon Postgres database and querying it via DATS-4 with natural language. Covers database setup, file upload, and natural language querying with charting. Notes that production use requires data engineering, semantic layers, and cleaning beyond the demo workflow." }, { "id": "qwen3-max-now-live-on-dats-4-sql-agent-suite-for-advanced-analysis-better-than-deepseek-r1-closer-t", "slug": "qwen3-max-now-live-on-dats-4-sql-agent-suite-for-advanced-analysis-better-than-deepseek-r1-closer-t", "title": "Qwen3 Max now live on DATS-4 SQL Agent Suite for Advanced Analysis Better than DeepSeek R1, closer to Claude Sonnet 4 - at a lower cost.", "date_published": "2025-09-10T06:59:08.865Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.651211Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/qwen3-max-now-live-on-dats-4-sql-agent-suite-for-advanced-analysis-better-than-deepseek-r1-closer-t", "thumbnail": "https://static.wixstatic.com/media/ef0c19_abb051293f5b4c9c863448ec509ec657~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/qwen3-max-now-live-on-dats-4-sql-agent-suite-for-advanced-analysis-better-than-deepseek-r1-closer-t.md", "html_file": "output/html-pages/qwen3-max-now-live-on-dats-4-sql-agent-suite-for-advanced-analysis-better-than-deepseek-r1-closer-t.html", "content_length": 2241, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 3345, "html": 5247 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.189466Z", "scraped_from": "output/jina-ai-raw/qwen3-max-now-live-on-dats-4-sql-agent-suite-for-advanced-analysis-better-than-deepseek-r1-closer-t.md" }, "youtube_ids": "KkE7IgqXtuU", "summary": "Benchmark of Qwen3 Max added to DATS-4 SQL Agent Suite. Quality scores: Claude Sonnet 4 (100), GPT-5 (95), Qwen3 Max (90), DeepSeek R1 (85). Cost per 100 advanced analysis questions ranges from $14.25 (Gemini 2.5 Flash) to $27.50 (GPT-5). Each advanced question triggers 7-10 SQL queries. Tested on credit card data mart and RBI ranking tasks." }, { "id": "google-tools-i-use-on-live-projects-analysis-automation-building-micro-apps", "slug": "google-tools-i-use-on-live-projects-analysis-automation-building-micro-apps", "title": "Google Tools I Use on Live Projects — Analysis, Automation & Building Micro-Apps", "date_published": "2025-09-09T15:15:53.981Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.644110Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/google-tools-i-use-on-live-projects-analysis-automation-building-micro-apps", "thumbnail": "https://static.wixstatic.com/media/ef0c19_85825f4c08ff45f199fadb9cdd6f759c~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/google-tools-i-use-on-live-projects-analysis-automation-building-micro-apps.md", "html_file": "output/html-pages/google-tools-i-use-on-live-projects-analysis-automation-building-micro-apps.html", "content_length": 2445, "has_images": false, "has_videos": true, "file_sizes": { "markdown": 3324, "html": 6175 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.159371Z", "scraped_from": "output/jina-ai-raw/google-tools-i-use-on-live-projects-analysis-automation-building-micro-apps.md" }, "youtube_ids": "hQDQ18dxPIM", "summary": "Practical inventory of Google AI tools used in production: Gemini Build for rapid React prototyping, AI Studio with Gemini 2.5 Pro for code generation, Gemini 2.0/2.5 Flash APIs for backend automation, Gemini CLI for Python/FastAPI/xlwings work, NotebookLM for video-to-notes extraction, and Opal for workflow automation. Includes starter YouTube links." }, { "id": "python-in-excel-xlwings-lite-with-natural-language-instructions", "slug": "python-in-excel-xlwings-lite-with-natural-language-instructions", "title": "Python in Excel (xlwings Lite) with Natural Language Instructions.", "date_published": "2025-09-06T10:39:29.643Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.650215Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/python-in-excel-xlwings-lite-with-natural-language-instructions", "thumbnail": null, "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/python-in-excel-xlwings-lite-with-natural-language-instructions.md", "html_file": "output/html-pages/python-in-excel-xlwings-lite-with-natural-language-instructions.html", "content_length": 2157, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3312, "html": 5281 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.184648Z", "scraped_from": "output/jina-ai-raw/python-in-excel-xlwings-lite-with-natural-language-instructions.md" }, "youtube_ids": "rHXte26r3BY", "summary": "Workflow for AI-assisted xlwings Lite code generation using voice dictation and natural language. Covers 5 rules: be specific, iterate one step at a time, demand pseudocode plans, validate rigorously, and run AI audit passes. Recommends Gemini 2.5 Pro on AI Studio as primary tool. Includes a 1,855-line AI Coder instruction file for reliable output." }, { "id": "python-in-excel-field-guide-practice-lab-for-ai-assisted-xlwings-lite", "slug": "python-in-excel-field-guide-practice-lab-for-ai-assisted-xlwings-lite", "title": "Python in Excel: Field Guide & Practice Lab for AI-assisted xlwings Lite.", "date_published": "2025-09-03T11:29:24.790Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.650215Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/python-in-excel-field-guide-practice-lab-for-ai-assisted-xlwings-lite", "thumbnail": "https://static.wixstatic.com/media/ef0c19_5f24e7475b604da3a792c3538fac916d~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel", "ai-coders" ], "markdown_file": "output/staging-markdown/python-in-excel-field-guide-practice-lab-for-ai-assisted-xlwings-lite.md", "html_file": "output/html-pages/python-in-excel-field-guide-practice-lab-for-ai-assisted-xlwings-lite.html", "content_length": 1223, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2373, "html": 4150 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.182639Z", "scraped_from": "output/jina-ai-raw/python-in-excel-field-guide-practice-lab-for-ai-assisted-xlwings-lite.md" }, "youtube_ids": "", "summary": "Comprehensive practice lab for AI-assisted xlwings Lite development containing a 1,855-line AI Coder instruction file, three hands-on modules (data manipulation, cleaning, campaign build) with workbooks and guides, and live apps (web scrapers, database connectors, ML models). Core protocol: Show context, Tell instructions, Inspect and validate all output." }, { "id": "two-of-the-best-resources-i-ve-seen-on-building-agentic-ai-one-from-manus-one-from-anthropic", "slug": "two-of-the-best-resources-i-ve-seen-on-building-agentic-ai-one-from-manus-one-from-anthropic", "title": "Two of the best resources I've seen on building agentic AI. One from Manus, one from Anthropic.", "date_published": "2025-09-01T13:36:24.900Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.656724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/two-of-the-best-resources-i-ve-seen-on-building-agentic-ai-one-from-manus-one-from-anthropic", "thumbnail": "https://static.wixstatic.com/media/ef0c19_92a98314e84c477ab8cd761dcab4ebc5~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/two-of-the-best-resources-i-ve-seen-on-building-agentic-ai-one-from-manus-one-from-anthropic.md", "html_file": "output/html-pages/two-of-the-best-resources-i-ve-seen-on-building-agentic-ai-one-from-manus-one-from-anthropic.html", "content_length": 1400, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1979, "html": 3742 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.204464Z", "scraped_from": "output/jina-ai-raw/two-of-the-best-resources-i-ve-seen-on-building-agentic-ai-one-from-manus-one-from-anthropic.md" }, "youtube_ids": "", "summary": "Curated recommendation of two practical guides on building agentic AI systems: Anthropic's Multi-Agent Research System documentation and Manus's Context Engineering for AI Agents blog post. Both cover lessons from production agent deployments. Also references the author's own Database AI field guide for text-to-SQL agent architecture." }, { "id": "database-ai-sql-now-choose-you-llm-gpt-5-deepseek-qwen-3-thinking-live-open-source", "slug": "database-ai-sql-now-choose-you-llm-gpt-5-deepseek-qwen-3-thinking-live-open-source", "title": "Database AI & SQL - Now choose you LLM: GPT-5, Deepseek, Qwen 3 Thinking. Live. Open Source.", "date_published": "2025-09-01T13:30:01.294Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.638926Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/database-ai-sql-now-choose-you-llm-gpt-5-deepseek-qwen-3-thinking-live-open-source", "thumbnail": null, "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/database-ai-sql-now-choose-you-llm-gpt-5-deepseek-qwen-3-thinking-live-open-source.md", "html_file": "output/html-pages/database-ai-sql-now-choose-you-llm-gpt-5-deepseek-qwen-3-thinking-live-open-source.html", "content_length": 1818, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2286, "html": 4172 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.146388Z", "scraped_from": "output/jina-ai-raw/database-ai-sql-now-choose-you-llm-gpt-5-deepseek-qwen-3-thinking-live-open-source.md" }, "youtube_ids": "IVF35pEpCQs", "summary": "DATS-4 Database AI Suite update adding LLM selection: Claude Sonnet 4, GPT-5, DeepSeek, and Qwen 3 Thinking. Demonstrates self-healing agent behavior (recovers from SQL errors by inspecting data). Full-stack features include on-the-fly database connect, instant Postgres creation, agent reasoning traces, and PDF outputs. Built on 15+ months of live client operations." }, { "id": "database-ai-sql-agent-connect-to-any-database-on-the-fly-live-open-source", "slug": "database-ai-sql-agent-connect-to-any-database-on-the-fly-live-open-source", "title": "Database AI & SQL Agent - Connect to any database on-the-fly. Live. Open Source", "date_published": "2025-09-01T13:16:34.112Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.638926Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/database-ai-sql-agent-connect-to-any-database-on-the-fly-live-open-source", "thumbnail": null, "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/database-ai-sql-agent-connect-to-any-database-on-the-fly-live-open-source.md", "html_file": "output/html-pages/database-ai-sql-agent-connect-to-any-database-on-the-fly-live-open-source.html", "content_length": 1552, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1989, "html": 3874 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.144382Z", "scraped_from": "output/jina-ai-raw/database-ai-sql-agent-connect-to-any-database-on-the-fly-live-open-source.md" }, "youtube_ids": "nBGUt9Ny1rQ", "summary": "Demo of DATS-4 core text-to-SQL workflow: paste database credentials (MySQL/Postgres) in any format, AI parses them to valid JSON, then query in natural language. Agent shows full transparency: reasoning, generated SQL, and results in tables and charts. Supports data transformation, table creation, merges, and CSV export. Open source." }, { "id": "free-production-grade-databases-get-setup-in-minutes-great-for-testing-and-development", "slug": "free-production-grade-databases-get-setup-in-minutes-great-for-testing-and-development", "title": "Free, Production-Grade Databases. Get setup in minutes. Great for testing and development", "date_published": "2025-09-01T13:04:14.092Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.640100Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/free-production-grade-databases-get-setup-in-minutes-great-for-testing-and-development", "thumbnail": "https://static.wixstatic.com/media/ef0c19_05a0769bed9e4183bc7c4997913e2914~mv2.png", "status": "published", "tags": [ "database-ai", "infrastructure" ], "markdown_file": "output/staging-markdown/free-production-grade-databases-get-setup-in-minutes-great-for-testing-and-development.md", "html_file": "output/html-pages/free-production-grade-databases-get-setup-in-minutes-great-for-testing-and-development.html", "content_length": 1955, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2529, "html": 4346 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.150391Z", "scraped_from": "output/jina-ai-raw/free-production-grade-databases-get-setup-in-minutes-great-for-testing-and-development.md" }, "youtube_ids": "", "summary": "Comparison of three free database providers for AI app development: Neon (sub-1-second Postgres via API, best for AI apps), Supabase (auth integration), and Aiven (5GB free tier, supports both Postgres and MySQL). Used across DATS-4, Custom GPT, and Realtime Voice AI deployments. Includes a spec sheet comparing features." }, { "id": "database-sql-ai-on-the-fly-database-transformation-with-natural-language-connect-transform-and", "slug": "database-sql-ai-on-the-fly-database-transformation-with-natural-language-connect-transform-and", "title": "Database & SQL AI: On-the-fly database transformation with natural language. Connect, transform, and export instantly.", "date_published": "2025-09-01T12:29:06.960Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.638926Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/database-sql-ai-on-the-fly-database-transformation-with-natural-language-connect-transform-and", "thumbnail": null, "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/database-sql-ai-on-the-fly-database-transformation-with-natural-language-connect-transform-and.md", "html_file": "output/html-pages/database-sql-ai-on-the-fly-database-transformation-with-natural-language-connect-transform-and.html", "content_length": 1599, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2271, "html": 4153 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.146388Z", "scraped_from": "output/jina-ai-raw/database-sql-ai-on-the-fly-database-transformation-with-natural-language-connect-transform-and.md" }, "youtube_ids": "J18DeYIewtM", "summary": "DATS-4 Database AI Suite workflow for on-the-fly data transformation: connect to any MySQL/Postgres database, instruct the agent in natural language to create derived variables and transformations, review agent reasoning and SQL, and export results to CSV. Also supports direct CSV uploads and temporary Postgres instances. Open source." }, { "id": "security-performance-report-for-investors-ai-quant-agent-live-open-source-free", "slug": "security-performance-report-for-investors-ai-quant-agent-live-open-source-free", "title": "Security Performance Report for Investors. AI Quant Agent. Live. Open Source. Free.", "date_published": "2025-09-01T12:02:05.083Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.653724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/security-performance-report-for-investors-ai-quant-agent-live-open-source-free", "thumbnail": null, "status": "published", "tags": [ "portfolio-analytics" ], "markdown_file": "output/staging-markdown/security-performance-report-for-investors-ai-quant-agent-live-open-source-free.md", "html_file": "output/html-pages/security-performance-report-for-investors-ai-quant-agent-live-open-source-free.html", "content_length": 1873, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2756, "html": 4627 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.198464Z", "scraped_from": "output/jina-ai-raw/security-performance-report-for-investors-ai-quant-agent-live-open-source-free.md" }, "youtube_ids": "", "summary": "Open-source AI Quants Agent generating Security Performance Reports with CAGR, Sharpe, Sortino ratios, max drawdown analysis (7 worst periods per security), cumulative returns charts, monthly returns breakdown, and an Anxiety Index. Outputs interactive HTML and CSV downloads. Uses Yahoo Finance tickers. Full methodology documentation with validations included." }, { "id": "ai-technical-report-for-traders-an-open-source-tool", "slug": "ai-technical-report-for-traders-an-open-source-tool", "title": "AI Technical Report for Traders- An Open Source Tool", "date_published": "2025-08-30T13:17:16.635Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.631426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-technical-report-for-traders-an-open-source-tool", "thumbnail": null, "status": "published", "tags": [ "technical-analysis" ], "markdown_file": "output/staging-markdown/ai-technical-report-for-traders-an-open-source-tool.md", "html_file": "output/html-pages/ai-technical-report-for-traders-an-open-source-tool.html", "content_length": 1799, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2278, "html": 4065 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.115387Z", "scraped_from": "output/jina-ai-raw/ai-technical-report-for-traders-an-open-source-tool.md" }, "youtube_ids": "fOzCEG-4hc8", "summary": "Open-source AI technical analysis report generator for stocks, crypto, and commodities. Produces 6-section PDF/HTML reports covering daily and weekly timeframes with AI-driven outlook. Uses indicators including EMA, MACD, RSI, ROC, and Bollinger Bands. Built on FastAPI-MCP server with Gemini Flash. Designed as a second-opinion tool against manual chart analysis." }, { "id": "ai-for-databases-field-guide-live-apps-lessons", "slug": "ai-for-databases-field-guide-live-apps-lessons", "title": "AI for Databases: Field Guide, Live Apps & Lessons", "date_published": "2025-08-10T13:23:11.228Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.630426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-for-databases-field-guide-live-apps-lessons", "thumbnail": "https://static.wixstatic.com/media/ef0c19_4bc13cd0df5e45279260efd5c55cb825~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/ai-for-databases-field-guide-live-apps-lessons.md", "html_file": "output/html-pages/ai-for-databases-field-guide-live-apps-lessons.html", "content_length": 2051, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2530, "html": 4483 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.111388Z", "scraped_from": "output/jina-ai-raw/ai-for-databases-field-guide-live-apps-lessons.md" }, "youtube_ids": "", "summary": "50-page practitioner's field guide on AI for databases based on 15+ months of live client deployments. Covers security checklists, datamart design, 3-agent orchestration architecture, LLM recommendation matrix, cost analysis (simple vs advanced queries), usage patterns, and platform stack. Includes 8 live apps across 4 variants plus full source code." }, { "id": "build-full-campaign-in-excel-with-python-xlwings-lite-ai", "slug": "build-full-campaign-in-excel-with-python-xlwings-lite-ai", "title": "Build Full Campaign in Excel with Python , xlwings Lite & AI", "date_published": "2025-07-23T09:10:18.008Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.634411Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/build-full-campaign-in-excel-with-python-xlwings-lite-ai", "thumbnail": "https://static.wixstatic.com/media/ef0c19_e5ffa43ca7144c7fb445f6ad8f264550~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/build-full-campaign-in-excel-with-python-xlwings-lite-ai.md", "html_file": "output/html-pages/build-full-campaign-in-excel-with-python-xlwings-lite-ai.html", "content_length": 2043, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2549, "html": 4362 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.123878Z", "scraped_from": "output/jina-ai-raw/build-full-campaign-in-excel-with-python-xlwings-lite-ai.md" }, "youtube_ids": "", "summary": "Module 03 field guide for building marketing campaigns in Excel using xlwings Lite and AI. Covers waterfall execution, rule-based segmentation, stratified test/control setup with statistical checks, and validation/audit reports. Includes a 1,855-line AI Coder instruction file, practice workbooks, and completed examples. Based on live SMB client campaign work." }, { "id": "releasing-module-02-practitioner-s-series-on-xlwings-lite-python-in-excel-data-cleaning-rule-b", "slug": "releasing-module-02-practitioner-s-series-on-xlwings-lite-python-in-excel-data-cleaning-rule-b", "title": "Releasing Module 02 — Practitioner’s Series on xlwings Lite. Python in Excel. Data Cleaning & Rule Based Transformation", "date_published": "2025-07-06T10:39:13.109Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.652719Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/releasing-module-02-practitioner-s-series-on-xlwings-lite-python-in-excel-data-cleaning-rule-b", "thumbnail": "https://static.wixstatic.com/media/ef0c19_da9cc8b6d30b420eb7111a626b4dc257~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/releasing-module-02-practitioner-s-series-on-xlwings-lite-python-in-excel-data-cleaning-rule-b.md", "html_file": "output/html-pages/releasing-module-02-practitioner-s-series-on-xlwings-lite-python-in-excel-data-cleaning-rule-b.html", "content_length": 1852, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2473, "html": 4253 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.191463Z", "scraped_from": "output/jina-ai-raw/releasing-module-02-practitioner-s-series-on-xlwings-lite-python-in-excel-data-cleaning-rule-b.md" }, "youtube_ids": "", "summary": "Module 02 guide on data cleaning with xlwings Lite using a real-world mobile number example. Covers multi-step rule-based transformations, AI instruction methodology, context file usage, and output validation. Emphasizes that validation is non-negotiable regardless of AI assistance. Includes practice workbook and AI Coder instruction file." }, { "id": "a-1-450-line-context-file-to-ensure-clean-efficient-xlwings-lite-code-ge", "slug": "a-1-450-line-context-file-to-ensure-clean-efficient-xlwings-lite-code-ge", "title": "Tool: A 1,450-line context file. Purpose: To ensure clean, efficient xlwings Lite code generation.", "date_published": "2025-07-06T09:46:30.445Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.628397Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/a-1-450-line-context-file-to-ensure-clean-efficient-xlwings-lite-code-ge", "thumbnail": "https://static.wixstatic.com/media/ef0c19_b41a7fe22ef64da98f9f16b9c6a03155~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel", "ai-coders" ], "markdown_file": "output/staging-markdown/a-1-450-line-context-file-to-ensure-clean-efficient-xlwings-lite-code-ge.md", "html_file": "output/html-pages/a-1-450-line-context-file-to-ensure-clean-efficient-xlwings-lite-code-ge.html", "content_length": 1888, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2611, "html": 4619 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.102762Z", "scraped_from": "output/jina-ai-raw/a-1-450-line-context-file-to-ensure-clean-efficient-xlwings-lite-code-ge.md" }, "youtube_ids": "FbJMRCGPjwM", "summary": "A 1,450-line AI context file for reliable xlwings Lite code generation. Contains 5 golden rules for preventing common script failures, 13 sections covering interface and API integration, and 6 advanced examples (database connections, web scraping, XGBoost). Addresses specific xlwings Lite requirements like @script decorator, Pyodide-compatible packages, and CORS-enabled endpoints." }, { "id": "a-free-hands-on-guide-for-excel-professionals", "slug": "a-free-hands-on-guide-for-excel-professionals", "title": "xlwings Lite Practice Lab - a free, hands-on guide for Excel professionals", "date_published": "2025-06-29T12:20:07.512Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.628397Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/a-free-hands-on-guide-for-excel-professionals", "thumbnail": "https://static.wixstatic.com/media/ef0c19_c58907ec757548beaef91af449a95ef0~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/a-free-hands-on-guide-for-excel-professionals.md", "html_file": "output/html-pages/a-free-hands-on-guide-for-excel-professionals.html", "content_length": 1760, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2625, "html": 4558 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.104143Z", "scraped_from": "output/jina-ai-raw/a-free-hands-on-guide-for-excel-professionals.md" }, "youtube_ids": "", "summary": "Launch of xlwings Lite Practice Lab for Excel professionals with zero coding experience. Module 01 starter kit includes AI Coder guidelines, pre-built example workbook, and visual step-by-step guide covering data manipulation, visualization, and AI-driven variables. xlwings Lite capabilities extend to database integration, web scraping, and ML models." }, { "id": "ai-technical-analysis-tool", "slug": "ai-technical-analysis-tool", "title": "AI Technical Analysis Tool", "date_published": "2025-06-24T06:49:06.275Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.631426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-technical-analysis-tool", "thumbnail": "https://static.wixstatic.com/media/ef0c19_03b9603ae1ce40e78f57af6c794af2fb~mv2.png", "status": "published", "tags": [ "technical-analysis" ], "markdown_file": "output/staging-markdown/ai-technical-analysis-tool.md", "html_file": "output/html-pages/ai-technical-analysis-tool.html", "content_length": 1007, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 1388, "html": 2999 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.114388Z", "scraped_from": "output/jina-ai-raw/ai-technical-analysis-tool.md" }, "youtube_ids": "4xbXGxyXFB8", "summary": "AI-powered technical analysis tool for generating unbiased second-opinion reports on any Yahoo Finance security. Covers multi-timeframe trends, support/resistance levels, EMA, MACD, RSI, and ROC indicators. Outputs clean PDF or HTML reports powered by Gemini 2.5 Flash. Designed for traders who want to challenge their own chart analysis. Open source and customizable." }, { "id": "that-9x-return-from-nifty-midcap-is-irrelevant-if-you-couldn-t-survive-the-73-of-time-it-was-in-dra", "slug": "that-9x-return-from-nifty-midcap-is-irrelevant-if-you-couldn-t-survive-the-73-of-time-it-was-in-dra", "title": "That 9X return from Nifty Midcap is irrelevant if you couldn't survive the 73% of time it was in drawdown", "date_published": "2025-06-22T05:17:00.179Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.654724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/that-9x-return-from-nifty-midcap-is-irrelevant-if-you-couldn-t-survive-the-73-of-time-it-was-in-dra", "thumbnail": "https://static.wixstatic.com/media/ef0c19_c6cf988467844644b0e8c8bebdfba028~mv2.png", "status": "published", "tags": [ "portfolio-analytics" ], "markdown_file": "output/staging-markdown/that-9x-return-from-nifty-midcap-is-irrelevant-if-you-couldn-t-survive-the-73-of-time-it-was-in-dra.md", "html_file": "output/html-pages/that-9x-return-from-nifty-midcap-is-irrelevant-if-you-couldn-t-survive-the-73-of-time-it-was-in-dra.html", "content_length": 1560, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2173, "html": 3959 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.201464Z", "scraped_from": "output/jina-ai-raw/that-9x-return-from-nifty-midcap-is-irrelevant-if-you-couldn-t-survive-the-73-of-time-it-was-in-dra.md" }, "youtube_ids": "Whn57GEccEk", "summary": "Analysis of drawdown psychology across Nifty 50, L&T, and Nifty Midcap 100 over 18 years using the custom Anxiety Index metric. Nifty Midcap's 9X return came with 73% of time in drawdown. L&T had a single 2,377-day drawdown period. Calculations performed with the free TIGZIG Quants tool. Explains Anxiety Index methodology." }, { "id": "tigzig-quants-gpt-30-second-financial-analysis-custom-gpt", "slug": "tigzig-quants-gpt-30-second-financial-analysis-custom-gpt", "title": "TIGZIG Quants GPT: 30-Second Financial Analysis Custom GPT", "date_published": "2025-06-20T12:56:54.235Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.655725Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/tigzig-quants-gpt-30-second-financial-analysis-custom-gpt", "thumbnail": "https://static.wixstatic.com/media/ef0c19_7328f6b119a2404e8f5bfeb81bd15b7a~mv2.png", "status": "published", "tags": [ "custom-gpt", "portfolio-analytics" ], "markdown_file": "output/staging-markdown/tigzig-quants-gpt-30-second-financial-analysis-custom-gpt.md", "html_file": "output/html-pages/tigzig-quants-gpt-30-second-financial-analysis-custom-gpt.html", "content_length": 1199, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 1674, "html": 3354 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.202464Z", "scraped_from": "output/jina-ai-raw/tigzig-quants-gpt-30-second-financial-analysis-custom-gpt.md" }, "youtube_ids": "", "summary": "Custom GPT for 30-second cross-asset financial analysis comparing stocks, indices, commodities, and crypto. Generates daily returns charts, drawdown analysis, CAGR, Sharpe ratios, and CSV downloads. Available in three interfaces: Suite (fast, needs Yahoo symbols), Agent (smart assist), and ChatGPT (familiar interface, free users included). FastAPI-MCP backend." }, { "id": "open-source-asset-comparison-tool-compare-stocks-indices-crypto-commodities-in-one-dashboard", "slug": "open-source-asset-comparison-tool-compare-stocks-indices-crypto-commodities-in-one-dashboard", "title": "Open Source Asset Comparison Tool: Compare Stocks, Indices, Crypto & Commodities in One Dashboard", "date_published": "2025-06-18T15:28:44.881Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.649211Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/open-source-asset-comparison-tool-compare-stocks-indices-crypto-commodities-in-one-dashboard", "thumbnail": "https://static.wixstatic.com/media/ef0c19_9c66904c6454473aa7ea0de9d8e23fd4~mv2.png", "status": "published", "tags": [ "portfolio-analytics" ], "markdown_file": "output/staging-markdown/open-source-asset-comparison-tool-compare-stocks-indices-crypto-commodities-in-one-dashboard.md", "html_file": "output/html-pages/open-source-asset-comparison-tool-compare-stocks-indices-crypto-commodities-in-one-dashboard.html", "content_length": 1559, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2167, "html": 4104 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.178303Z", "scraped_from": "output/jina-ai-raw/open-source-asset-comparison-tool-compare-stocks-indices-crypto-commodities-in-one-dashboard.md" }, "youtube_ids": "TFA1aNamLrM", "summary": "Open-source cross-asset comparison tool (TIGZIG Quants) for comparing stocks, indices, crypto, and commodities in a single dashboard. Generates daily returns charts, drawdowns, CAGR, Sharpe, Sortino ratios, and date-aligned CSV downloads within 30 seconds. Two interfaces: Agent (smart assist) and Suite (fast, no-frills). Built with MCP servers, n8n, and React." }, { "id": "open-so", "slug": "open-so", "title": "Live Portfolio Analytics - Powered by MCP Servers - Open Source", "date_published": "2025-05-09T13:44:54.753Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.648202Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/open-so", "thumbnail": "https://static.wixstatic.com/media/ef0c19_85de661e38614e6ba5300a74480aabca~mv2.png", "status": "published", "tags": [ "portfolio-analytics", "mcp" ], "markdown_file": "output/staging-markdown/open-so.md", "html_file": "output/html-pages/open-so.html", "content_length": 22122, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 22930, "html": 26601 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.176301Z", "scraped_from": "output/jina-ai-raw/open-so.md" }, "youtube_ids": "wtOSF5dVL2E", "summary": "Modular live portfolio analytics stack powered by MCP-FastAPI servers. Delivers 70+ KPIs, 15+ charts, AI technical analysis, and PDF/HTML reports across 6 interfaces: React, NextJS, ChatGPT, Flowise, xlwings Lite, and forms. Backend uses QuantStats, yfinance, Finta, Gemini Vision, and ReportLab. Three public MCP servers available for plug-and-play integration." }, { "id": "live-python-in-excel-with-xlwings-lite", "slug": "live-python-in-excel-with-xlwings-lite", "title": "Live Python-in-Excel systems built with xlwings Lite", "date_published": "2025-05-09T11:55:15.480Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.645948Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/live-python-in-excel-with-xlwings-lite", "thumbnail": "https://static.wixstatic.com/media/ef0c19_781bd76d918d4db78c223d4215ebcef8~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/live-python-in-excel-with-xlwings-lite.md", "html_file": "output/html-pages/live-python-in-excel-with-xlwings-lite.html", "content_length": 1770, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2872, "html": 5541 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.169301Z", "scraped_from": "output/jina-ai-raw/live-python-in-excel-with-xlwings-lite.md" }, "youtube_ids": "", "summary": "Roundup of six Python-in-Excel apps built with xlwings Lite covering AI web scraping (Jina AI + Gemini), technical analysis with PDF/web reports, AI schema detection, remote database connectivity via FastAPI, EDA with XGBoost modeling, and Yahoo Finance data extraction. Each app includes downloadable Excel files, source code, and setup guides. Demonstrates API calls, LLM integration, and automation workflows running entirely inside Excel." }, { "id": "chatgpt-connected-fastapi-mcp-servers-technical-analysis-ta-report-stocks-crypto", "slug": "chatgpt-connected-fastapi-mcp-servers-technical-analysis-ta-report-stocks-crypto", "title": "ChatGPT Connected to integrated FastAPI-MCP Servers.. Technical Analysis (TA) report. From stocks to crypto.", "date_published": "2025-04-28T17:33:13.134Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.635916Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/chatgpt-connected-fastapi-mcp-servers-technical-analysis-ta-report-stocks-crypto", "thumbnail": "https://static.wixstatic.com/media/ef0c19_c2362b1e2bee4ccb8e12118d4454db42~mv2.png", "status": "published", "tags": [ "custom-gpt", "mcp", "technical-analysis" ], "markdown_file": "output/staging-markdown/chatgpt-connected-fastapi-mcp-servers-technical-analysis-ta-report-stocks-crypto.md", "html_file": "output/html-pages/chatgpt-connected-fastapi-mcp-servers-technical-analysis-ta-report-stocks-crypto.html", "content_length": 1301, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2112, "html": 4662 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.134347Z", "scraped_from": "output/jina-ai-raw/chatgpt-connected-fastapi-mcp-servers-technical-analysis-ta-report-stocks-crypto.md" }, "youtube_ids": "XIQcobZ5yxI", "summary": "Connecting ChatGPT to integrated FastAPI-MCP servers for generating technical analysis reports on stocks, crypto, and commodities via Yahoo Finance symbols. Backend uses FastAPI with MCP server (Tadata's FastAPI-MCP), serving multiple interfaces: n8n, Flask UI, Next.js, ChatGPT, and xlwings Lite. Outputs formatted PDF and web reports with Gemini Vision chart analysis. Includes OpenAPI schema setup for Custom GPT actions and public source code." }, { "id": "build-ai-workflows-mcp-servers-n8n-technical-analysis", "slug": "build-ai-workflows-mcp-servers-n8n-technical-analysis", "title": "Build AI Workflows with MCP Servers + n8n", "date_published": "2025-04-22T07:38:26.465Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.633412Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/build-ai-workflows-mcp-servers-n8n-technical-analysis", "thumbnail": "https://static.wixstatic.com/media/ef0c19_7251258e3cc540c3b7cdcef5109fbbdf~mv2.png", "status": "published", "tags": [ "mcp", "technical-analysis" ], "markdown_file": "output/staging-markdown/build-ai-workflows-mcp-servers-n8n-technical-analysis.md", "html_file": "output/html-pages/build-ai-workflows-mcp-servers-n8n-technical-analysis.html", "content_length": 1600, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2270, "html": 4161 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.122879Z", "scraped_from": "output/jina-ai-raw/build-ai-workflows-mcp-servers-n8n-technical-analysis.md" }, "youtube_ids": "GE3HexsI5-s", "summary": "Building AI workflows by connecting MCP servers to n8n for automated technical analysis. Uses Tadata's FastAPI-MCP to mount MCP on existing FastAPI servers and n8n's MCP Client node for SSE connections. Pipeline pulls Yahoo Finance data, computes indicators, sends charts to Gemini Vision for AI analysis, and outputs PDF/web reports. Includes Docker deployment setup, public MCP server URLs, n8n schemas, and full source code." }, { "id": "ai-powered-dynamic-web-scraper-in-excel-python-ai-xlwings-lite-part-6", "slug": "ai-powered-dynamic-web-scraper-in-excel-python-ai-xlwings-lite-part-6", "title": "AI Powered Dynamic Web Scraper in Excel | Python+AI in Excel | xlwings Lite - Part 6.", "date_published": "2025-04-15T04:39:08.611Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.630426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-powered-dynamic-web-scraper-in-excel-python-ai-xlwings-lite-part-6", "thumbnail": "https://static.wixstatic.com/media/ef0c19_2f665fdad9ed4644a90187df33999022~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/ai-powered-dynamic-web-scraper-in-excel-python-ai-xlwings-lite-part-6.md", "html_file": "output/html-pages/ai-powered-dynamic-web-scraper-in-excel-python-ai-xlwings-lite-part-6.html", "content_length": 1632, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2854, "html": 5356 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.112387Z", "scraped_from": "output/jina-ai-raw/ai-powered-dynamic-web-scraper-in-excel-python-ai-xlwings-lite-part-6.md" }, "youtube_ids": "H3q6TdTL9Vw", "summary": "AI-powered web scraper running inside Excel via xlwings Lite. Users define URLs, target columns, and extraction rules in a spreadsheet. Backend uses Jina AI API for markdown extraction and Gemini API for structured data output via auto-generated JSON schemas. Outputs formatted Excel tables with detailed logs. Practical for lead generation, market research, and real estate analysis. Includes downloadable Excel template and setup guide." }, { "id": "9e37b53b", "slug": "9e37b53b", "title": "AI-Powered Technical Analysis in Excel - with Python & Gemini Vision", "date_published": "2025-04-09T07:47:11.605Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.628397Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/9e37b53b", "thumbnail": "https://static.wixstatic.com/media/ef0c19_bd1b561270cb443f960a7cc973920225~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel", "technical-analysis" ], "markdown_file": "output/staging-markdown/9e37b53b.md", "html_file": "output/html-pages/9e37b53b.html", "content_length": 1570, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2334, "html": 4749 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.101768Z", "scraped_from": "output/jina-ai-raw/9e37b53b.md" }, "youtube_ids": "BKWEvglkB-c", "summary": "Technical analysis system running inside Excel via xlwings Lite (Part 5). Pulls Yahoo Finance OHLCV data, computes indicators with Finta, resamples to weekly timeframes with pandas, builds multi-subplot Matplotlib charts (EMA, Bollinger Bands, MACD, RSI, ROC), sends chart images to Gemini Vision API for AI analysis, and generates PDF/web reports via FastAPI. Single downloadable Excel file with free Gemini API key." }, { "id": "stock-data-to-ai-reports-python-in-excel-xlwings-lite-part-4", "slug": "stock-data-to-ai-reports-python-in-excel-xlwings-lite-part-4", "title": "Stock Data to AI Reports | Python-in-Excel | xlwings Lite - Part 4", "date_published": "2025-04-07T11:19:23.523Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.654724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/stock-data-to-ai-reports-python-in-excel-xlwings-lite-part-4", "thumbnail": "https://static.wixstatic.com/media/ef0c19_bc64cc0559434371bb7b5ad2ffef5ba7~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel", "technical-analysis" ], "markdown_file": "output/staging-markdown/stock-data-to-ai-reports-python-in-excel-xlwings-lite-part-4.md", "html_file": "output/html-pages/stock-data-to-ai-reports-python-in-excel-xlwings-lite-part-4.html", "content_length": 2635, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3777, "html": 6206 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.200464Z", "scraped_from": "output/jina-ai-raw/stock-data-to-ai-reports-python-in-excel-xlwings-lite-part-4.md" }, "youtube_ids": "nnsO8XmLYuk", "summary": "xlwings Lite Part 4: pulling stock data from Yahoo Finance, computing technical indicators, sending to Gemini for AI insights, and generating PDF technical analysis reports directly from Excel. Also extracts multi-period financials (P&L, balance sheet, cash flows, quarterlies). Uses a FastAPI backend that doubles as an MCP server. Includes Excel template, GitHub repo, 8-minute video walkthrough, and build guide." }, { "id": "ai-python-excel-xlwings-lite-llm-api-calls-part-3", "slug": "ai-python-excel-xlwings-lite-llm-api-calls-part-3", "title": "AI + Python in Excel with xlwings Lite - LLM API Calls | Part 3", "date_published": "2025-04-07T11:07:15.504Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.631426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-python-excel-xlwings-lite-llm-api-calls-part-3", "thumbnail": "https://static.wixstatic.com/media/ef0c19_9f43c73b857c4099be317a4151b438e5~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/ai-python-excel-xlwings-lite-llm-api-calls-part-3.md", "html_file": "output/html-pages/ai-python-excel-xlwings-lite-llm-api-calls-part-3.html", "content_length": 2382, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3285, "html": 5526 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.113389Z", "scraped_from": "output/jina-ai-raw/ai-python-excel-xlwings-lite-llm-api-calls-part-3.md" }, "youtube_ids": "lAADII7ZDuM", "summary": "xlwings Lite Part 3: making LLM API calls from Excel to Gemini 2.0 Flash and GPT-4o for schema detection (column names, types) with structured JSON output. Demonstrates automated workflows using detected schemas for EDA tables, plots, and downstream processing. Covers API calls via requests/httpx, comparison of LLM vs Python schema detection, and practical automation use cases including web scraping, text classification, and text-to-SQL." }, { "id": "python-in-excel-with-xlwings-lite-part-2-connect-to-remote-databases", "slug": "python-in-excel-with-xlwings-lite-part-2-connect-to-remote-databases", "title": "xlwings lite |Connect to Remote Databases", "date_published": "2025-04-07T11:00:49.002Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.650215Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/python-in-excel-with-xlwings-lite-part-2-connect-to-remote-databases", "thumbnail": "https://static.wixstatic.com/media/ef0c19_6861071587c04e86bc744c1093482a12~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel", "database-ai" ], "markdown_file": "output/staging-markdown/python-in-excel-with-xlwings-lite-part-2-connect-to-remote-databases.md", "html_file": "output/html-pages/python-in-excel-with-xlwings-lite-part-2-connect-to-remote-databases.html", "content_length": 3503, "has_images": false, "has_videos": true, "file_sizes": { "markdown": 4521, "html": 6533 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.183651Z", "scraped_from": "output/jina-ai-raw/python-in-excel-with-xlwings-lite-part-2-connect-to-remote-databases.md" }, "youtube_ids": "6Vmzd4sHcxY", "summary": "xlwings Lite Part 2: connecting Excel to remote PostgreSQL databases via a custom FastAPI web layer. Demonstrates exploring tables, pulling records, running custom SQL, then performing EDA with descriptive stats, frequency tables, distribution plots, and building an XGBoost response model with evaluation metrics, decile table, and ROC/Gains chart. Includes FastAPI server source code, Render deployment guide, and 20-minute video walkthrough." }, { "id": "python-workflows-inside-excel-with-xlwings-lite-free", "slug": "python-workflows-inside-excel-with-xlwings-lite-free", "title": "Python Workflows. Inside Excel. With xlwings Lite (free) - Powerful.", "date_published": "2025-03-23T17:02:00.113Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.650215Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/python-workflows-inside-excel-with-xlwings-lite-free", "thumbnail": "https://static.wixstatic.com/media/ef0c19_3713c088e8574a3898fc7a20be954a65~mv2.png", "status": "published", "tags": [ "xlwings-lite", "python-in-excel" ], "markdown_file": "output/staging-markdown/python-workflows-inside-excel-with-xlwings-lite-free.md", "html_file": "output/html-pages/python-workflows-inside-excel-with-xlwings-lite-free.html", "content_length": 2003, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2953, "html": 5247 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.185648Z", "scraped_from": "output/jina-ai-raw/python-workflows-inside-excel-with-xlwings-lite-free.md" }, "youtube_ids": "6QEMuN295Mo", "summary": "Introduction to xlwings Lite, a free Python-in-Excel add-in with built-in code editor, console, and Excel object model integration. Demonstrates running XGBoost model builds, feature engineering, basic statistics, and data transformations directly inside Excel. Covers key features: Excel object manipulation, custom functions (@func/@script decorators), Web API calls, and package management via requirements.txt. No Python installation needed." }, { "id": "7d905dcc", "slug": "7d905dcc", "title": "AI-Powered Automation: Connect ChatGPT to n8n", "date_published": "2025-03-21T10:37:49.418Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.627143Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/7d905dcc", "thumbnail": "https://static.wixstatic.com/media/ef0c19_eddd6107a212436fb4d79cbaa5c5400d~mv2.png", "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/7d905dcc.md", "html_file": "output/html-pages/7d905dcc.html", "content_length": 3515, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 4468, "html": 6790 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.099763Z", "scraped_from": "output/jina-ai-raw/7d905dcc.md" }, "youtube_ids": "WPpr8NEw-Ng", "summary": "Connecting ChatGPT to n8n workflows for automation including backend database updates (Postgres, Google Sheets), Python processing via FastAPI (markdown-to-PDF), Google Apps Script automation (formatted PDFs, slides, emails), dynamic database connections, and AI content enhancement. Setup uses Custom GPT with OpenAPI action schema connecting to n8n webhook endpoints. Includes n8n workflow JSON, FastAPI server repos, and video walkthrough." }, { "id": "quick-deploy-advanced-analysis-multi-agent-with-flowise", "slug": "quick-deploy-advanced-analysis-multi-agent-with-flowise", "title": "Quick Deploy Advanced Analysis Multi-Agent with Flowise", "date_published": "2025-03-21T10:34:19.777Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.651211Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/quick-deploy-advanced-analysis-multi-agent-with-flowise", "thumbnail": "https://static.wixstatic.com/media/ef0c19_b248a6f4558d4e059c064279899b5def~mv2.png", "status": "published", "tags": [ "database-ai" ], "markdown_file": "output/staging-markdown/quick-deploy-advanced-analysis-multi-agent-with-flowise.md", "html_file": "output/html-pages/quick-deploy-advanced-analysis-multi-agent-with-flowise.html", "content_length": 1837, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3297, "html": 5499 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.187464Z", "scraped_from": "output/jina-ai-raw/quick-deploy-advanced-analysis-multi-agent-with-flowise.md" }, "youtube_ids": "rfV48lct1IE", "summary": "Four-step quick deployment guide for a multi-agent advanced analytics system using Flowise AI. Import agent schemas, update credentials, deploy a FastAPI SQL connector, and adjust security settings. Supports reasoning models (Deepseek, Gemini, Sonnet 3.7) with a sequential agent architecture. Tips cover free database setup (Neon, Aiven, Supabase), adding new reasoning models via OpenRouter, and customizing agent routing." }, { "id": "ff7ee13c", "slug": "ff7ee13c", "title": "Google Colab Data Science Agent vs. Mito-AI Jupyter Copilot. How do they compare? When to use which?", "date_published": "2025-03-09T10:58:33.119Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.640100Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ff7ee13c", "thumbnail": "https://static.wixstatic.com/media/ef0c19_3a5863f66a564317aa7b5419a25d89b3~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/ff7ee13c.md", "html_file": "output/html-pages/ff7ee13c.html", "content_length": 1767, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2946, "html": 5521 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.149388Z", "scraped_from": "output/jina-ai-raw/ff7ee13c.md" }, "youtube_ids": "wSdUmr8i6j4", "summary": "Comparison of Google Colab Data Science Agent (DSA) and Mito-AI Jupyter Copilot for data science workflows. Colab DSA excels at multi-step autonomous analysis with free GPU access and code sharing. Mito-AI provides a Cursor-like copilot experience with auto-schema detection, conversation memory, and voice coding support (Win+H). Covers pricing (Colab free, Mito-AI $20/month or open-source with own API key) and when to use each tool." }, { "id": "google-on-a-roll-launches-dsa-data-science-agent-on-colab-first-impression-just-brilliant", "slug": "google-on-a-roll-launches-dsa-data-science-agent-on-colab-first-impression-just-brilliant", "title": "Google on a roll — launches DSA — Data Science Agent on Colab. First impression = just brilliant.", "date_published": "2025-03-04T14:00:50.668Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.643100Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/google-on-a-roll-launches-dsa-data-science-agent-on-colab-first-impression-just-brilliant", "thumbnail": "https://static.wixstatic.com/media/ef0c19_5906747f58ff43aabb14a87bc0261157~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/google-on-a-roll-launches-dsa-data-science-agent-on-colab-first-impression-just-brilliant.md", "html_file": "output/html-pages/google-on-a-roll-launches-dsa-data-science-agent-on-colab-first-impression-just-brilliant.html", "content_length": 1766, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3096, "html": 5557 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.157714Z", "scraped_from": "output/jina-ai-raw/google-on-a-roll-launches-dsa-data-science-agent-on-colab-first-impression-just-brilliant.md" }, "youtube_ids": "WQ3IQBfF0G0", "summary": "First impressions of Google Colab's Data Science Agent (DSA) that plans and executes multi-step analysis autonomously. Compares with Mito-AI Copilot for Jupyter which offers Cursor-like chat, auto-schema detection, and conversation memory. Colab DSA stronger for one-shot full workflows; Mito-AI better for iterative step-by-step work requiring validation. Includes pricing details and guidance on when each tool fits best." }, { "id": "ai-co-analyst-live-multi-agent-app-cost-quality-reliability", "slug": "ai-co-analyst-live-multi-agent-app-cost-quality-reliability", "title": "AI Co-Analyst — Live Multi-Agent App. Cost, quality, reliability — what works? what doesn’t?", "date_published": "2025-03-02T13:34:27.717Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.629424Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-co-analyst-live-multi-agent-app-cost-quality-reliability", "thumbnail": "https://static.wixstatic.com/media/ef0c19_e92580b739ec4c05b0b42e733754b127~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/ai-co-analyst-live-multi-agent-app-cost-quality-reliability.md", "html_file": "output/html-pages/ai-co-analyst-live-multi-agent-app-cost-quality-reliability.html", "content_length": 4272, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 6245, "html": 8981 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.107388Z", "scraped_from": "output/jina-ai-raw/ai-co-analyst-live-multi-agent-app-cost-quality-reliability.md" }, "youtube_ids": "hqn3zrdXVSQ", "summary": "Detailed benchmarking of LLM models (Sonnet 3.7, Deepseek-R1, Gemini 2.0 Flash, o3-mini) for AI co-analyst use cases. Covers quality rankings, cost per query (8.5c to 20.5c for reasoning queries), latency ranges (1-10+ minutes), and API reliability. Architecture uses Flowise sequential agents (LangGraph) with router, reasoning, and executor (GPT-4o) agents. Includes live demo app, 5 repos, 7 Flowise schemas, and video build guide." }, { "id": "vibe_coding_andrej_karpahty_mito_ai", "slug": "vibe_coding_andrej_karpahty_mito_ai", "title": "Vibe coding (Andrej Karpathy) in Jupyter with Mito-AI — the Cursor for data scientists. My top 8 Tips", "date_published": "2025-02-25T13:43:37.304Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.656724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/vibe_coding_andrej_karpahty_mito_ai", "thumbnail": "https://static.wixstatic.com/media/ef0c19_6ce2043ef56944fca770f1cc7a267d69~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/vibe_coding_andrej_karpahty_mito_ai.md", "html_file": "output/html-pages/vibe_coding_andrej_karpahty_mito_ai.html", "content_length": 2303, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 4208, "html": 6919 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.205465Z", "scraped_from": "output/jina-ai-raw/vibe_coding_andrej_karpahty_mito_ai.md" }, "youtube_ids": "5h0xDRUWlSM", "summary": "Practical guide to vibe coding (voice-driven AI coding, per Andrej Karpathy) using Mito-AI Copilot in Jupyter notebooks. Eight tips covering voice input (Win+H), one-chunk-at-a-time prompting, auto-schema detection for dataframes, conversation history referencing, Mitosheet visual spreadsheet view, open-source vs premium pricing ($20/month), API key setup via PowerShell environment variables, and validation practices for modeling and automation." }, { "id": "multi-agents-sequential-reasoning-connect-database-o3-mini-deepseek-r1-flash-2-0-flowise", "slug": "multi-agents-sequential-reasoning-connect-database-o3-mini-deepseek-r1-flash-2-0-flowise", "title": "Multi-Agents (Sequential) with Reasoning – Connect to any database - o3-mini / Deepseek-R1 / Flash-2.0. Built with Flowise.", "date_published": "2025-02-23T15:24:57.306Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.647181Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/multi-agents-sequential-reasoning-connect-database-o3-mini-deepseek-r1-flash-2-0-flowise", "thumbnail": "https://static.wixstatic.com/media/ef0c19_a1968a36e8fa4cbeae06a2ca803cd072~mv2.png", "status": "published", "tags": [ "database-ai" ], "markdown_file": "output/staging-markdown/multi-agents-sequential-reasoning-connect-database-o3-mini-deepseek-r1-flash-2-0-flowise.md", "html_file": "output/html-pages/multi-agents-sequential-reasoning-connect-database-o3-mini-deepseek-r1-flash-2-0-flowise.html", "content_length": 2485, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3821, "html": 5947 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.171301Z", "scraped_from": "output/jina-ai-raw/multi-agents-sequential-reasoning-connect-database-o3-mini-deepseek-r1-flash-2-0-flowise.md" }, "youtube_ids": "B5LJyUA1iaU", "summary": "Multi-agent sequential architecture built with Flowise (LangGraph backend) offering 6 database agent options with o3-mini, Deepseek-R1, and Gemini Flash 2.0. Flow: router agent directs to general analyst or advanced route where reasoning LLM generates analysis plan and SQL, then GPT-4o executor verifies and runs queries. Supports any database connection, NL-to-SQL, NL-to-Python charts. Includes Flowise agent templates and tool schemas." }, { "id": "google-gemini-2-0-flash-api-performance-quality-cheaper-gpt-4o-mini", "slug": "google-gemini-2-0-flash-api-performance-quality-cheaper-gpt-4o-mini", "title": "Google Gemini 2.0 Flash — solid API performance, great quality, and cheaper than GPT-4-mini. The new workhorse?", "date_published": "2025-02-23T15:21:49.322Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.643100Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/google-gemini-2-0-flash-api-performance-quality-cheaper-gpt-4o-mini", "thumbnail": "https://static.wixstatic.com/media/ef0c19_2dfd996ce5dd4204a04a302dd42aa22f~mv2.png", "status": "published", "tags": [ "database-ai" ], "markdown_file": "output/staging-markdown/google-gemini-2-0-flash-api-performance-quality-cheaper-gpt-4o-mini.md", "html_file": "output/html-pages/google-gemini-2-0-flash-api-performance-quality-cheaper-gpt-4o-mini.html", "content_length": 879, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1419, "html": 3164 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.156272Z", "scraped_from": "output/jina-ai-raw/google-gemini-2-0-flash-api-performance-quality-cheaper-gpt-4o-mini.md" }, "youtube_ids": "O91tyoaW4a8", "summary": "Evaluation of Gemini 2.0 Flash API for production LLM workloads. Benchmarks against GPT-4o-mini, GPT-4o, and Claude 3.5 Sonnet across automation, web scraping, structured output, and OCR tasks. Gemini 2.0 Flash shows quality matching GPT-4o, reasoning comparable to Deepseek-R1, and pricing below GPT-4o-mini ($0.10/$0.40 per million input/output tokens) with a generous free tier (15 req/min, 1500 req/day)." }, { "id": "ai-driven-advanced-analytics-reasoning-based-sequential-agents-connect-to-any-database-o3-mini-d", "slug": "ai-driven-advanced-analytics-reasoning-based-sequential-agents-connect-to-any-database-o3-mini-d", "title": "AI Driven Advanced Analytics. Reasoning based Sequential Agents. Connect to any database — o3-mini/deepseek-r1 / gemini-flash-2.0.", "date_published": "2025-02-14T16:57:23.806Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.630426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-driven-advanced-analytics-reasoning-based-sequential-agents-connect-to-any-database-o3-mini-d", "thumbnail": "https://static.wixstatic.com/media/ef0c19_e8b2091cf19f4b75b4d32290d4ae2930~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/ai-driven-advanced-analytics-reasoning-based-sequential-agents-connect-to-any-database-o3-mini-d.md", "html_file": "output/html-pages/ai-driven-advanced-analytics-reasoning-based-sequential-agents-connect-to-any-database-o3-mini-d.html", "content_length": 3264, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 5345, "html": 7582 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.110387Z", "scraped_from": "output/jina-ai-raw/ai-driven-advanced-analytics-reasoning-based-sequential-agents-connect-to-any-database-o3-mini-d.md" }, "youtube_ids": "hqn3zrdXVSQ", "summary": "Open-source advanced analytics app using sequential agents (LangGraph via Flowise) with reasoning models (o3-mini, Deepseek-R1, Gemini Flash 2.0) and GPT-4o executor. Features NL-to-SQL, NL-to-Python charts, file upload with on-the-fly Postgres DB creation (Neon), execution logs, and agent reasoning view. Built with React/TypeScript/Vite, FastAPI backends, deployed on Vercel and Hetzner. Includes 1-hour video guide, 4 repos, and 6 Flowise schemas." }, { "id": "connect-chatgpt-to-supabase-in-10-mins", "slug": "connect-chatgpt-to-supabase-in-10-mins", "title": "Connect ChatGPT to Supabase in 10 mins.", "date_published": "2025-01-19T14:01:39.055Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.637926Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/connect-chatgpt-to-supabase-in-10-mins", "thumbnail": "https://static.wixstatic.com/media/ef0c19_abf2c928387944b9b5b038925d749e9d~mv2.png", "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/connect-chatgpt-to-supabase-in-10-mins.md", "html_file": "output/html-pages/connect-chatgpt-to-supabase-in-10-mins.html", "content_length": 1711, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2445, "html": 4452 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.139713Z", "scraped_from": "output/jina-ai-raw/connect-chatgpt-to-supabase-in-10-mins.md" }, "youtube_ids": "Zx5Noz2sFgA", "summary": "Step-by-step guide to connect a Custom GPT to a Supabase PostgreSQL database in 10 minutes. Deploy a FastAPI server (SQL Alchemy) on Render, create a Custom GPT with OpenAPI action schema, and enable NL-to-SQL, charts, and Python statistical analysis through ChatGPT. Same process works for any PostgreSQL or MySQL database. Includes GitHub repo, video demo, and API key security setup." }, { "id": "ai-automation-micro-app-mf-portfolio-files-processor-live-app-open-source", "slug": "ai-automation-micro-app-mf-portfolio-files-processor-live-app-open-source", "title": "AI automation micro-app: MF Portfolio Files Processor. Live app. Open source.", "date_published": "2025-01-19T13:59:11.976Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.629424Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-automation-micro-app-mf-portfolio-files-processor-live-app-open-source", "thumbnail": "https://static.wixstatic.com/media/ef0c19_0f65612d54c740159079a24d081ed72f~mv2.png", "status": "published", "tags": [ "mutual-funds", "converters-tools" ], "markdown_file": "output/staging-markdown/ai-automation-micro-app-mf-portfolio-files-processor-live-app-open-source.md", "html_file": "output/html-pages/ai-automation-micro-app-mf-portfolio-files-processor-live-app-open-source.html", "content_length": 760, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1270, "html": 2930 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.104143Z", "scraped_from": "output/jina-ai-raw/ai-automation-micro-app-mf-portfolio-files-processor-live-app-open-source.md" }, "youtube_ids": "tn21U60pA1E", "summary": "AI-powered micro-app for processing Indian mutual fund monthly portfolio Excel files into standardized CSV/database format. Handles varying Excel formats using LLM schema detection (GPT-4o-mini, GPT-4o, Gemini Flash) for column identification and market value extraction. Includes validation diagnostics and manual override. Built with vanilla JavaScript frontend, FastAPI proxy for LLM calls, and domain whitelisting. Open source with Power Pivot analysis example." }, { "id": "realtime-voice-ai-openai-webrtc-implementation-live-app-open-source", "slug": "realtime-voice-ai-openai-webrtc-implementation-live-app-open-source", "title": "Realtime voice AI - OpenAI WebRTC Implementation. Live app. Open source.", "date_published": "2025-01-19T13:49:37.888Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.651211Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/realtime-voice-ai-openai-webrtc-implementation-live-app-open-source", "thumbnail": "https://static.wixstatic.com/media/ef0c19_62b1b47abcf94249a27e1ca383470d9d~mv2.png", "status": "published", "tags": [ "voice-ai" ], "markdown_file": "output/staging-markdown/realtime-voice-ai-openai-webrtc-implementation-live-app-open-source.md", "html_file": "output/html-pages/realtime-voice-ai-openai-webrtc-implementation-live-app-open-source.html", "content_length": 1725, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2352, "html": 4070 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.190464Z", "scraped_from": "output/jina-ai-raw/realtime-voice-ai-openai-webrtc-implementation-live-app-open-source.md" }, "youtube_ids": "mUyxpldpfGc", "summary": "Implementation of real-time voice AI analytics using OpenAI's Realtime API with WebRTC. REX-RT app connects to PostgreSQL (1.5M cricket records) for voice-driven database queries using GPT-4o-mini function calling. Costs approximately $0.05-0.07 per minute. Built with vanilla JavaScript, FastAPI for DB connectivity, and Flowise for LLM agents. Compares with ElevenLabs widget approach (~$0.20/min). Open source with deployment guides." }, { "id": "real-time-voice-ai-from-cricket-to-credit-cards-live-app-open-source", "slug": "real-time-voice-ai-from-cricket-to-credit-cards-live-app-open-source", "title": "Real-time voice AI - from cricket to credit cards. Live app. Open source.", "date_published": "2025-01-19T13:44:32.721Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.651211Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/real-time-voice-ai-from-cricket-to-credit-cards-live-app-open-source", "thumbnail": "https://static.wixstatic.com/media/ef0c19_bad56bf2d73e4cd19dbc237fd78bbe33~mv2.png", "status": "published", "tags": [ "voice-ai" ], "markdown_file": "output/staging-markdown/real-time-voice-ai-from-cricket-to-credit-cards-live-app-open-source.md", "html_file": "output/html-pages/real-time-voice-ai-from-cricket-to-credit-cards-live-app-open-source.html", "content_length": 1351, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2082, "html": 3824 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.189466Z", "scraped_from": "output/jina-ai-raw/real-time-voice-ai-from-cricket-to-credit-cards-live-app-open-source.md" }, "youtube_ids": "0SAYC4GC6Vw", "summary": "Real-time voice AI assistant (REX-C) built with ElevenLabs Conversational AI widget, connected to PostgreSQL with 1.5M ODI cricket records for voice-to-SQL queries. Costs ~$0.20/min. Compares real-time voice options: OpenAI Realtime API (~10c/min), ElevenLabs (~20c/min), Hume AI (~7c/min), VAPI (~5c/min), and others. Lightweight ~80kb vanilla JavaScript implementation with FastAPI backend and Flowise LLM agent." }, { "id": "gemini-2-0-multimodal-how-to-use", "slug": "gemini-2-0-multimodal-how-to-use", "title": "Gemini 2.0 - Multimodal - How to use", "date_published": "2025-01-19T13:40:34.870Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.640100Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/gemini-2-0-multimodal-how-to-use", "thumbnail": "https://static.wixstatic.com/media/ef0c19_9d61a3b3a97942fd91c1db989dce640d~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/gemini-2-0-multimodal-how-to-use.md", "html_file": "output/html-pages/gemini-2-0-multimodal-how-to-use.html", "content_length": 866, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1258, "html": 2901 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.151387Z", "scraped_from": "output/jina-ai-raw/gemini-2-0-multimodal-how-to-use.md" }, "youtube_ids": "", "summary": "First impressions of Google Gemini 2.0 multimodal capabilities including real-time vision, video, and audio processing. Covers AI Studio for non-technical users and developers, React starter app with full API access, Python SDK, and generous free tier (10 RPM, 4M TPM, 1500 requests/day). Discusses potential for building multimodal AI apps and integration with real-time voice analytics applications." }, { "id": "building-ai-apps-with-natural-language-and-voice-top-9-tips", "slug": "building-ai-apps-with-natural-language-and-voice-top-9-tips", "title": "Building AI apps with natural language and voice: top 9 tips", "date_published": "2025-01-19T13:34:49.979Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.634411Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/building-ai-apps-with-natural-language-and-voice-top-9-tips", "thumbnail": "https://static.wixstatic.com/media/ef0c19_a29c210b1aa949d582d69d53dfb5e1ab~mv2.png", "status": "published", "tags": [ "voice-ai", "ai-coders" ], "markdown_file": "output/staging-markdown/building-ai-apps-with-natural-language-and-voice-top-9-tips.md", "html_file": "output/html-pages/building-ai-apps-with-natural-language-and-voice-top-9-tips.html", "content_length": 1133, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1609, "html": 3312 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.129885Z", "scraped_from": "output/jina-ai-raw/building-ai-apps-with-natural-language-and-voice-top-9-tips.md" }, "youtube_ids": "", "summary": "Brief overview post pointing to REX AI Decision Intelligence platform at tigzig.com, an open-source collection of micro-apps and tools for AI-driven analytics and data science. Covers building AI apps with natural language and voice interfaces." }, { "id": "chat-with-database-20-ai-platforms-you-need-to-know", "slug": "chat-with-database-20-ai-platforms-you-need-to-know", "title": "Chat with database: 20 AI platforms you need to know", "date_published": "2024-12-06T13:16:09.557Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.635916Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/chat-with-database-20-ai-platforms-you-need-to-know", "thumbnail": "https://static.wixstatic.com/media/ef0c19_fa2d51b711d94c689300c9413cd11e44~mv2.png", "status": "published", "tags": [ "database-ai" ], "markdown_file": "output/staging-markdown/chat-with-database-20-ai-platforms-you-need-to-know.md", "html_file": "output/html-pages/chat-with-database-20-ai-platforms-you-need-to-know.html", "content_length": 916, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1362, "html": 3018 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.134347Z", "scraped_from": "output/jina-ai-raw/chat-with-database-20-ai-platforms-you-need-to-know.md" }, "youtube_ids": "REw3y_Jv3Ig", "summary": "Survey of 20 AI platforms for database chat and analysis, covering production tools and prototypes from simple to complex. Platforms reviewed include Datalang, Ask-Your-Database, Blaze SQL, Wren AI, SQL Chat, Julius AI, Quills AI, Vanna AI, and others. Highlights unique features: SQL editors, data modeling, graph builders, RAG pipelines, API layers. Compares with REX platform features including file upload, on-the-fly DB creation, and real-time voice." }, { "id": "connect-any-database-with-chatgpt", "slug": "connect-any-database-with-chatgpt", "title": "Connect, Chat and Analyze Any Database with ChatGPTFast, Simple, and Powerful.", "date_published": "2025-01-19T13:03:05.351Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.636921Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/connect-any-database-with-chatgpt", "thumbnail": "https://static.wixstatic.com/media/ef0c19_94ca77783e41452f95990399a99f5c23~mv2.png", "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/connect-any-database-with-chatgpt.md", "html_file": "output/html-pages/connect-any-database-with-chatgpt.html", "content_length": 2289, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2951, "html": 5408 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.137347Z", "scraped_from": "output/jina-ai-raw/connect-any-database-with-chatgpt.md" }, "youtube_ids": "gm7nbZaqMOs", "summary": "Guide to connecting ChatGPT to any MySQL or PostgreSQL database using REX-2's FastAPI backend and Custom GPT actions. Setup involves creating a GPT with ready-to-use instructions and JSON schema, then deploying FastAPI on Render. Enables NL-to-SQL, voice-to-SQL, data transformation, statistical analysis, and Python charts through ChatGPT or REX UI. Includes instant database creation via Neon and file upload capabilities." }, { "id": "rex-2-your-ai-analyst-on-call", "slug": "rex-2-your-ai-analyst-on-call", "title": "REX-2: Your AI Analyst on Call", "date_published": "2025-01-19T12:57:35.317Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.652719Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/rex-2-your-ai-analyst-on-call", "thumbnail": "https://static.wixstatic.com/media/ef0c19_7d658ce775724f148734b506dbb8725d~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/rex-2-your-ai-analyst-on-call.md", "html_file": "output/html-pages/rex-2-your-ai-analyst-on-call.html", "content_length": 850, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1230, "html": 2864 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.193463Z", "scraped_from": "output/jina-ai-raw/rex-2-your-ai-analyst-on-call.md" }, "youtube_ids": "LoE64UBgz3s", "summary": "Demo of REX-2 AI analyst workflow: create a database on the fly, upload 100MB CSV/TXT files, and query with voice or text for analysis tables and charts in under 150 seconds. Features include NL-to-SQL, interactive tables with sorting/filtering/stats, statistical analysis, AI reports, and PDF generation. Supports connecting to existing MySQL/PostgreSQL warehouses. Uses Windows voice dictation (Win+H) for voice input." }, { "id": "rex-2-ai-driven-analytics-python-connect-to-any-database", "slug": "rex-2-ai-driven-analytics-python-connect-to-any-database", "title": "REX-2 : AI Driven Analytics", "date_published": "2025-01-19T12:48:29.543Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.652719Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/rex-2-ai-driven-analytics-python-connect-to-any-database", "thumbnail": "https://static.wixstatic.com/media/ef0c19_2ad5ffc05f904bcaa11274cd5f4e74ea~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/rex-2-ai-driven-analytics-python-connect-to-any-database.md", "html_file": "output/html-pages/rex-2-ai-driven-analytics-python-connect-to-any-database.html", "content_length": 896, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1329, "html": 2935 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.192468Z", "scraped_from": "output/jina-ai-raw/rex-2-ai-driven-analytics-python-connect-to-any-database.md" }, "youtube_ids": "LoE64UBgz3s", "summary": "Release notes for REX-2 AI decision intelligence platform. Features NL-to-SQL, NL-to-Python, statistical analysis, Python charts, interactive tables, PDF reports, connection to any MySQL/PostgreSQL warehouse, on-the-fly temporary database creation via Neon, and CSV/TXT file upload. Built with React/TypeScript/Vite/Shadcn, Flowise AI backend, E2B for Python execution, FastAPI for DB connectivity, deployed on Vercel and Hetzner via Coolify. Four GitHub repos included." }, { "id": "ai-analytics-assistant-5-part-implementation-guide", "slug": "ai-analytics-assistant-5-part-implementation-guide", "title": "AI Analytics Assistant: 5 Part Implementation Guide", "date_published": "2025-01-19T08:56:45.358Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.628397Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/ai-analytics-assistant-5-part-implementation-guide", "thumbnail": "https://static.wixstatic.com/media/ef0c19_2982bc90eeec463c8a54d683727c014d~mv2.png", "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/ai-analytics-assistant-5-part-implementation-guide.md", "html_file": "output/html-pages/ai-analytics-assistant-5-part-implementation-guide.html", "content_length": 967, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1412, "html": 3072 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.104143Z", "scraped_from": "output/jina-ai-raw/ai-analytics-assistant-5-part-implementation-guide.md" }, "youtube_ids": "aGQhncYbATM", "summary": "Five-part implementation guide (2+ hours video) for building an AI analytics assistant with voice and text input. Covers Flowise AI agent setup, Make.com workflow integration, Google Apps Script automation, custom React frontend development with Claude Dev and Cursor AI, and Vercel deployment. Each part includes hands-on walkthroughs with timestamps. Source code, JSON schemas, and blueprints available on GitHub." }, { "id": "connect-custom-gpt-to-live-data-warehouses-implementation-guide", "slug": "connect-custom-gpt-to-live-data-warehouses-implementation-guide", "title": "How to connect Custom GPT to live data warehouses. Implementation Guide", "date_published": "2024-10-24T06:59:04.300Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.637926Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/connect-custom-gpt-to-live-data-warehouses-implementation-guide", "thumbnail": null, "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/connect-custom-gpt-to-live-data-warehouses-implementation-guide.md", "html_file": "output/html-pages/connect-custom-gpt-to-live-data-warehouses-implementation-guide.html", "content_length": 2246, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 3679, "html": 5593 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.140713Z", "scraped_from": "output/jina-ai-raw/connect-custom-gpt-to-live-data-warehouses-implementation-guide.md" }, "youtube_ids": "vUPWdXqzJJM", "summary": "Implementation guide for connecting Custom GPT to live data warehouses (AWS RDS MySQL and Azure MySQL simultaneously) using OpenAPI custom actions. Covers FastAPI SQL connector deployment on Render, JSON action schema generation for OpenAI, and security/monitoring considerations. Enables querying, transformation, analysis, and charting via text or voice (including Hindi/Hinglish). Includes Medium article, GitHub repo, and cost analysis." }, { "id": "how-to-build-ai-action-agents-beyond-chat-with-voice-agents", "slug": "how-to-build-ai-action-agents-beyond-chat-with-voice-agents", "title": "How to update Excel, Google Sheet and backend Databases with Natural Language commands with Voice Agents", "date_published": "2024-10-24T06:40:28.123Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.644110Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/how-to-build-ai-action-agents-beyond-chat-with-voice-agents", "thumbnail": null, "status": "published", "tags": [ "voice-ai", "database-ai" ], "markdown_file": "output/staging-markdown/how-to-build-ai-action-agents-beyond-chat-with-voice-agents.md", "html_file": "output/html-pages/how-to-build-ai-action-agents-beyond-chat-with-voice-agents.html", "content_length": 2453, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3248, "html": 5374 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.161373Z", "scraped_from": "output/jina-ai-raw/how-to-build-ai-action-agents-beyond-chat-with-voice-agents.md" }, "youtube_ids": "fR0P-3DCReM", "summary": "Part 1 of a 5-part series on building voice-enabled LLM action agents (VTEXER). Demonstrates updating Excel, Google Sheets, and remote databases, generating PDF reports and slides, querying MySQL, and emailing results via natural language voice commands. Uses Flowise AI ReAct agents with function calling, Make.com automation workflows, Google Apps Script, and FastAPI backend. Built with React.js frontend, all code generated by AI tools." }, { "id": "how-to-update-excel-google-sheets-and-databases-with-ai-voice-agents", "slug": "how-to-update-excel-google-sheets-and-databases-with-ai-voice-agents", "title": "How to update Excel, Google Sheet and backend Databases with Natural Language commands with Voice Agents", "date_published": "2024-10-24T06:28:15.963Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.645110Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/how-to-update-excel-google-sheets-and-databases-with-ai-voice-agents", "thumbnail": null, "status": "published", "tags": [ "voice-ai", "database-ai" ], "markdown_file": "output/staging-markdown/how-to-update-excel-google-sheets-and-databases-with-ai-voice-agents.md", "html_file": "output/html-pages/how-to-update-excel-google-sheets-and-databases-with-ai-voice-agents.html", "content_length": 1828, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2401, "html": 4480 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.165301Z", "scraped_from": "output/jina-ai-raw/how-to-update-excel-google-sheets-and-databases-with-ai-voice-agents.md" }, "youtube_ids": "aGQhncYbATM", "summary": "Part 2 implementation guide for AI voice action agents. Hands-on 45-minute video showing how to set up Flowise ReAct agents and Make.com webhooks to update Excel, Google Sheets, and databases via voice commands. Covers two go-live scenarios: Flowise native UI and full custom voice bot UI. Deployable source code on GitHub produces a functional voice bot. Integrates with 1000+ platforms via Make.com connectors." }, { "id": "automate-tasks-with-ai-voice-agents-and-google-script", "slug": "automate-tasks-with-ai-voice-agents-and-google-script", "title": "How to set up, deploy, and connect Google Scripts to Make.com for task automation.", "date_published": "2024-10-24T06:24:08.886Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.632426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/automate-tasks-with-ai-voice-agents-and-google-script", "thumbnail": null, "status": "published", "tags": [ "voice-ai" ], "markdown_file": "output/staging-markdown/automate-tasks-with-ai-voice-agents-and-google-script.md", "html_file": "output/html-pages/automate-tasks-with-ai-voice-agents-and-google-script.html", "content_length": 1539, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2112, "html": 4134 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.118387Z", "scraped_from": "output/jina-ai-raw/automate-tasks-with-ai-voice-agents-and-google-script.md" }, "youtube_ids": "09GRS23FmHk", "summary": "Part 3: setting up Google Apps Script for task automation connected to Make.com and Flowise AI voice agents. Demonstrates automated report generation (Excel-to-PDF), slide creation, and email delivery triggered by voice commands. Uses React.js custom frontend, Flowise for LLM agents, Google Script for automation, and FastAPI for AWS MySQL database connectivity. Includes hands-on implementation guide, source code, and JSON schemas on GitHub." }, { "id": "build-ai-voice-action-agent-app-in-react-js-in-natural-language", "slug": "build-ai-voice-action-agent-app-in-react-js-in-natural-language", "title": "How to use AI Assisted Coding Tools like Claude Dev and Cursor AI to develop LLM Apps with natural language commands. And deploy to open internet.", "date_published": "2024-10-24T06:15:09.626Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.633412Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/build-ai-voice-action-agent-app-in-react-js-in-natural-language", "thumbnail": null, "status": "published", "tags": [ "voice-ai", "ai-coders" ], "markdown_file": "output/staging-markdown/build-ai-voice-action-agent-app-in-react-js-in-natural-language.md", "html_file": "output/html-pages/build-ai-voice-action-agent-app-in-react-js-in-natural-language.html", "content_length": 1299, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 1993, "html": 4108 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.121879Z", "scraped_from": "output/jina-ai-raw/build-ai-voice-action-agent-app-in-react-js-in-natural-language.md" }, "youtube_ids": "Efn2hdLOPiU", "summary": "Part 4: using AI-assisted coding tools (Claude Dev VS Code extension and Cursor AI) to build LLM voice agent apps with natural language instructions. Demonstrates building a React.js voice bot with voice-to-text, chat completion, and text-to-speech components, then deploying to Vercel. Covers GitHub-to-Vercel deployment pipeline, multilingual support, and API endpoint routing to Flowise LLM agents." }, { "id": "how-to-build-voice-based-ai-action-agents-app-to-execute-tasks-automate-reports-and-analyze-data", "slug": "how-to-build-voice-based-ai-action-agents-app-to-execute-tasks-automate-reports-and-analyze-data", "title": "How to build Voice-based AI Action Agents App to Execute Tasks, Automate Reports, and Analyze Data …and more.", "date_published": "2024-10-24T06:07:54.541Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.645110Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/how-to-build-voice-based-ai-action-agents-app-to-execute-tasks-automate-reports-and-analyze-data", "thumbnail": null, "status": "published", "tags": [ "voice-ai" ], "markdown_file": "output/staging-markdown/how-to-build-voice-based-ai-action-agents-app-to-execute-tasks-automate-reports-and-analyze-data.md", "html_file": "output/html-pages/how-to-build-voice-based-ai-action-agents-app-to-execute-tasks-automate-reports-and-analyze-data.html", "content_length": 7102, "has_images": false, "has_videos": true, "file_sizes": { "markdown": 8311, "html": 10827 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.163371Z", "scraped_from": "output/jina-ai-raw/how-to-build-voice-based-ai-action-agents-app-to-execute-tasks-automate-reports-and-analyze-data.md" }, "youtube_ids": "Efn2hdLOPiU", "summary": "Comprehensive 5-part series overview for building VTEXER, a voice-enabled LLM action agent app. Covers updating Excel/Google Sheets/databases, generating PDF reports and slides, querying MySQL, web search, and custom agent menu selection via voice. Architecture: React.js frontend, Flowise ReAct agents (LangGraph), Make.com workflows, Google Apps Script, FastAPI backend. All code AI-generated using Claude Dev and Cursor AI. Full source code, schemas, and 2+ hours of video guides." }, { "id": "analyze-pdf-with-notebooklm-visualize-with-napkin-ai", "slug": "analyze-pdf-with-notebooklm-visualize-with-napkin-ai", "title": "Analyze PDF with NotebookLM. Visualize with Napkin AI.", "date_published": "2024-10-24T05:53:22.552Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.632426Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/analyze-pdf-with-notebooklm-visualize-with-napkin-ai", "thumbnail": null, "status": "published", "tags": [ "converters-tools" ], "markdown_file": "output/staging-markdown/analyze-pdf-with-notebooklm-visualize-with-napkin-ai.md", "html_file": "output/html-pages/analyze-pdf-with-notebooklm-visualize-with-napkin-ai.html", "content_length": 1390, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1907, "html": 3679 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.117387Z", "scraped_from": "output/jina-ai-raw/analyze-pdf-with-notebooklm-visualize-with-napkin-ai.md" }, "youtube_ids": "", "summary": "Using Google NotebookLM for PDF analysis (uploaded 10Q reports from Amazon, Apple, Google, Meta for comparative and DuPont ratio analysis) and Napkin AI for generating visuals from data and text. NotebookLM supports PDFs, URLs, slides as sources with QnA, summarization, analysis, and auto-generated podcast features. Napkin AI creates visual representations from pasted data or complex text." }, { "id": "power-up-with-gen-ai-query-analyze-youtube-videos-with-google-notebooklm", "slug": "power-up-with-gen-ai-query-analyze-youtube-videos-with-google-notebooklm", "title": "POWER UP WITH GEN AI: Query & Analyze YouTube Videos with Google NotebookLM.", "date_published": "2024-10-24T05:12:02.261Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.649211Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/power-up-with-gen-ai-query-analyze-youtube-videos-with-google-notebooklm", "thumbnail": null, "status": "published", "tags": [ "converters-tools" ], "markdown_file": "output/staging-markdown/power-up-with-gen-ai-query-analyze-youtube-videos-with-google-notebooklm.md", "html_file": "output/html-pages/power-up-with-gen-ai-query-analyze-youtube-videos-with-google-notebooklm.html", "content_length": 1354, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1787, "html": 3613 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.179304Z", "scraped_from": "output/jina-ai-raw/power-up-with-gen-ai-query-analyze-youtube-videos-with-google-notebooklm.md" }, "youtube_ids": "", "summary": "Using Google NotebookLM to query and analyze YouTube videos as a data source alongside PDFs, text files, markdown, web URLs, and audio. Demonstrates use cases including earnings call summaries, detailed transcript extraction, step-by-step guide generation, and targeted question answering across multiple video sources. Includes sample prompts and a visual guide." }, { "id": "rex1-your-realtime-ai-analytics-agent-system-web-version", "slug": "rex1-your-realtime-ai-analytics-agent-system-web-version", "title": "Meet REX-1: Your Realtime AI Analytics Agent System (Web Version)", "date_published": "2024-10-24T05:01:19.580Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.652719Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/rex1-your-realtime-ai-analytics-agent-system-web-version", "thumbnail": null, "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/rex1-your-realtime-ai-analytics-agent-system-web-version.md", "html_file": "output/html-pages/rex1-your-realtime-ai-analytics-agent-system-web-version.html", "content_length": 3004, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 4172, "html": 6196 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.194463Z", "scraped_from": "output/jina-ai-raw/rex1-your-realtime-ai-analytics-agent-system-web-version.md" }, "youtube_ids": "99aD2tv8G-0", "summary": "REX-1 real-time AI analytics agent built on OpenAI's Realtime API (~$1/min). Connects to data warehouses (AWS, Azure, MySQL) for voice-driven text-to-SQL, statistical analysis, Python charts, web scraping, stock technical charts, and reporting automation. Backend uses Flowise AI agents, Make.com workflows, and custom FastAPI servers. Includes 90-minute build guide video, non-realtime free tier with voice input, and detailed architecture walkthrough." }, { "id": "genai-llm-app-analytics-assistant-aws-azure-mysql", "slug": "genai-llm-app-analytics-assistant-aws-azure-mysql", "title": "GenAI App | LLM Analytics Assistant: Simplifying Data Transformation & Insights. AWS & Azure MySQL DW Example", "date_published": "2024-07-27T12:28:22.798Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.641097Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/genai-llm-app-analytics-assistant-aws-azure-mysql", "thumbnail": null, "status": "published", "tags": [ "database-ai", "text-to-sql" ], "markdown_file": "output/staging-markdown/genai-llm-app-analytics-assistant-aws-azure-mysql.md", "html_file": "output/html-pages/genai-llm-app-analytics-assistant-aws-azure-mysql.html", "content_length": 2238, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3140, "html": 5199 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.153388Z", "scraped_from": "output/jina-ai-raw/genai-llm-app-analytics-assistant-aws-azure-mysql.md" }, "youtube_ids": "QJTyjdEnP4w", "summary": "LLM analytics assistant app demonstrating data transformation and analysis on AWS MySQL with million-to-10M-row datasets. Covers creating customer profiles, summary tables, merging data via natural language instructions through Flowise AI platform. Details architecture with modular FastAPI processing server, model selection trade-offs (GPT-3.5 at ~$1 for 478 queries over 10 hours), cost optimization strategies, and split workflow approaches to minimize LLM token ingestion costs." }, { "id": "voice-mode-query-analyze-database-aws-azure-custom-gpt", "slug": "voice-mode-query-analyze-database-aws-azure-custom-gpt", "title": "VOICE MODE - Querying & Analyzing Data with Custom GPT AWS - Azure Data Warehouse", "date_published": "2024-07-27T12:25:41.487Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.656724Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/voice-mode-query-analyze-database-aws-azure-custom-gpt", "thumbnail": null, "status": "published", "tags": [ "voice-ai", "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/voice-mode-query-analyze-database-aws-azure-custom-gpt.md", "html_file": "output/html-pages/voice-mode-query-analyze-database-aws-azure-custom-gpt.html", "content_length": 1230, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2005, "html": 3913 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.206468Z", "scraped_from": "output/jina-ai-raw/voice-mode-query-analyze-database-aws-azure-custom-gpt.md" }, "youtube_ids": "", "summary": "Demonstration of ChatGPT voice mode for querying and analyzing an Azure MySQL data warehouse via Custom GPT. Shows inserting conditional fields, creating distributions from calculated fields, generating charts, creating summary tables, merging datasets, and table operations via voice commands. Applications include senior leadership voice dashboards, ad-hoc query support, and rapid data transformations. Part 2 of the AWS/Azure data warehouse series." }, { "id": "leave-all-programming-to-ai-a-data-scientists-perspective", "slug": "leave-all-programming-to-ai-a-data-scientists-perspective", "title": "Maybe leave programming to AI. Coding by GPTs: A Data Scientist's Perspective", "date_published": "2024-07-27T12:18:43.231Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.645948Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/leave-all-programming-to-ai-a-data-scientists-perspective", "thumbnail": null, "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/leave-all-programming-to-ai-a-data-scientists-perspective.md", "html_file": "output/html-pages/leave-all-programming-to-ai-a-data-scientists-perspective.html", "content_length": 2221, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3380, "html": 5318 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.168301Z", "scraped_from": "output/jina-ai-raw/leave-all-programming-to-ai-a-data-scientists-perspective.md" }, "youtube_ids": "z_ry5lWVRLQ", "summary": "Data scientist's perspective on using GPT for coding after 20+ years of programming experience. Covers practical considerations: domain expertise still matters, iteration and debugging remain necessary, language understanding helps (Python fast, React slower), GPT makes mistakes needing human intervention, and code privacy challenges. Examples include VBA automation, Custom GPT Python processing, FastAPI servers, and LLM apps. All coded by ChatGPT 3.5/4 and Gemini." }, { "id": "how-to-summarize-analyze-youtube-videos-with-ai", "slug": "how-to-summarize-analyze-youtube-videos-with-ai", "title": "How to summarize & analyze YouTube videos with AI: Two FREE and EASY options", "date_published": "2024-07-27T12:14:24.090Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.645110Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/how-to-summarize-analyze-youtube-videos-with-ai", "thumbnail": null, "status": "published", "tags": [ "converters-tools" ], "markdown_file": "output/staging-markdown/how-to-summarize-analyze-youtube-videos-with-ai.md", "html_file": "output/html-pages/how-to-summarize-analyze-youtube-videos-with-ai.html", "content_length": 1223, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 1960, "html": 3836 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.164295Z", "scraped_from": "output/jina-ai-raw/how-to-summarize-analyze-youtube-videos-with-ai.md" }, "youtube_ids": "SaYvyz7SRMU", "summary": "Two free methods for summarizing and analyzing YouTube videos with AI. Method 1: download MP4 and upload to Gemini Pro 1.5 (1M token context, handles 1-hour videos). Method 2: extract transcript and upload to Claude, Mistral AI, or Gemini for analysis. Covers how to get MP4 downloads, transcript extraction (YouTube native or third-party tools), and speech-to-text fallback when transcripts are unavailable." }, { "id": "gpt-a-force-multiplier", "slug": "gpt-a-force-multiplier", "title": "GPT-4 is acting like a force-multiplier like I have never experienced before.", "date_published": "2024-07-27T11:31:32.803Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.644110Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/gpt-a-force-multiplier", "thumbnail": "https://static.wixstatic.com/media/ef0c19_599fdcac678342449ca9e3dfcea9ac22~mv2.png", "status": "published", "tags": [ "ai-coders" ], "markdown_file": "output/staging-markdown/gpt-a-force-multiplier.md", "html_file": "output/html-pages/gpt-a-force-multiplier.html", "content_length": 2081, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 2740, "html": 4536 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.160368Z", "scraped_from": "output/jina-ai-raw/gpt-a-force-multiplier.md" }, "youtube_ids": "", "summary": "Personal account of GPT-4 as a productivity multiplier for data scientists and analysts. Use cases: fixing code errors via screenshot uploads, VBA automation for client projects (weeks reduced to days), Custom GPT with frozen Python code for Excel file processing, annual report ratio analysis on 500+ page documents, FastAPI server development with API calls from GPT, and using ChatGPT as a universal UI and processing server." }, { "id": "powerbots-supercharge-your-business-with-no-code-ai-chatbots-a-practical-guide", "slug": "powerbots-supercharge-your-business-with-no-code-ai-chatbots-a-practical-guide", "title": "POWERBOTS : Supercharge Your Business with No-Code AI Chatbots. A Practical Guide", "date_published": "2024-04-06T00:00:00.000Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.649211Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/powerbots-supercharge-your-business-with-no-code-ai-chatbots-a-practical-guide", "thumbnail": "https://static.wixstatic.com/media/ef0c19_4167293ea7a346bf9f93ff2879413188~mv2.png", "status": "published", "tags": [ "custom-gpt" ], "markdown_file": "output/staging-markdown/powerbots-supercharge-your-business-with-no-code-ai-chatbots-a-practical-guide.md", "html_file": "output/html-pages/powerbots-supercharge-your-business-with-no-code-ai-chatbots-a-practical-guide.html", "content_length": 18369, "has_images": true, "has_videos": true, "file_sizes": { "markdown": 19079, "html": 23495 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.181480Z", "scraped_from": "output/jina-ai-raw/powerbots-supercharge-your-business-with-no-code-ai-chatbots-a-practical-guide.md" }, "youtube_ids": "T-D1OfcDW1M", "summary": "Comparison of four no-code AI chatbot platforms across categories: pure-play builders (Botsonic, Zapier), chatflow-based (Botpress), OpenAI Assistant wrappers, and LLM app platforms (Flowise). Evaluates pricing, quality, security, customization, RAG capabilities, and Zapier/Make.com automation integrations. Includes live prototype chatbots tested with Warren Buffett shareholder letter. Covers LLM cost analysis (GPT-4 vs 3.5 vs Gemini Pro) and deployment options." }, { "id": "blog-llm-app-get-yahoo-financials-flowise-fastapi", "slug": "blog-llm-app-get-yahoo-financials-flowise-fastapi", "title": "YFIN Bot: LLM App to Pull Yahoo Finance Financials (Flowise + FastAPI)", "date_published": "2024-03-20T13:07:45.498Z", "date_updated": "2026-07-08T10:00:00.000Z", "last_modified": "2025-11-27T16:53:01.633412Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/blog-llm-app-get-yahoo-financials-flowise-fastapi", "thumbnail": "https://static.wixstatic.com/media/ef0c19_12a748557f094ad795d2200a6a0b268d~mv2.png", "status": "published", "tags": [ "database-ai", "fastapi", "portfolio-analytics" ], "markdown_file": "output/staging-markdown/blog-llm-app-get-yahoo-financials-flowise-fastapi.md", "html_file": "output/html-pages/blog-llm-app-get-yahoo-financials-flowise-fastapi.html", "content_length": 3438, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 5548, "html": 7316 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.120878Z", "scraped_from": "output/jina-ai-raw/blog-llm-app-get-yahoo-financials-flowise-fastapi.md" }, "youtube_ids": "", "summary": "YFIN Bot: an LLM app built with Flowise AI and FastAPI for extracting Yahoo Finance data (balance sheet, P&L, cash flow, quarterly income, closing prices) for listed equities. Uses Langchain Function Agent with custom tool, GPT-3.5-Turbo, and a Python/yfinance FastAPI server deployed on Render. Available as web app and Custom GPT on GPT Store. All code generated by ChatGPT and Gemini." }, { "id": "llama-parse-pdf-analyze-with-chatgpt-rag", "slug": "llama-parse-pdf-analyze-with-chatgpt-rag", "title": "How to use Llama Parse to convert PDF to text and extract complex table data. For Annual Reports, 10Ks, Research Reports", "date_published": "2024-03-20T13:04:38.760Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.645948Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/llama-parse-pdf-analyze-with-chatgpt-rag", "thumbnail": "https://static.wixstatic.com/media/ef0c19_57466f41c7ed41068c567dfa665ee800f000.jpg", "status": "published", "tags": [ "converters-tools" ], "markdown_file": "output/staging-markdown/llama-parse-pdf-analyze-with-chatgpt-rag.md", "html_file": "output/html-pages/llama-parse-pdf-analyze-with-chatgpt-rag.html", "content_length": 1876, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 2560, "html": 4568 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.170301Z", "scraped_from": "output/jina-ai-raw/llama-parse-pdf-analyze-with-chatgpt-rag.md" }, "youtube_ids": "", "summary": "Guide to using LlamaParse by LlamaIndex for converting complex PDFs (annual reports, 10Ks, research reports) to text with accurate table extraction. Covers optimization tips: API calls faster than Python package, 50-page chunk size optimal for parsing speed, and chunking before parsing improves performance versus processing full files at once. Includes a live LlamaParse PDF-to-Markdown converter tool." }, { "id": "mutual-fund-analysis-custom-gpt-python-multiple-excel", "slug": "mutual-fund-analysis-custom-gpt-python-multiple-excel", "title": "Mutual Fund Allocation Analysis with GPT Power Tools. Custom GPT. Custom Python Code. Multiple Excels.", "date_published": "2024-03-20T00:00:00.000Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.647181Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/mutual-fund-analysis-custom-gpt-python-multiple-excel", "thumbnail": "https://static.wixstatic.com/media/ef0c19_c4e5d5091a0d44b5a9618d1c49ad7e76~mv2.png", "status": "published", "tags": [ "mutual-funds", "custom-gpt" ], "markdown_file": "output/staging-markdown/mutual-fund-analysis-custom-gpt-python-multiple-excel.md", "html_file": "output/html-pages/mutual-fund-analysis-custom-gpt-python-multiple-excel.html", "content_length": 1659, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 3600, "html": 5444 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.173301Z", "scraped_from": "output/jina-ai-raw/mutual-fund-analysis-custom-gpt-python-multiple-excel.md" }, "youtube_ids": "", "summary": "Custom GPT for tracking changes in mutual fund equity portfolio allocations across time periods by merging multiple Excel files (up to 10, each in different formats). Uses custom Python code within the GPT for consistent processing instead of ad-hoc approaches. Handles print-formatted Excel files with images. Includes validation summaries and pivot table output. Replicable for any Excel processing use case with minimal modification." }, { "id": "code-red-unprotected-gpts-ai-apps-exposed-by-simple-hacks", "slug": "code-red-unprotected-gpts-ai-apps-exposed-by-simple-hacks", "title": "Code Red: Unprotected GPTs & AI Apps exposed by simple hacks", "date_published": "2024-02-10T16:36:22.458Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.636921Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/code-red-unprotected-gpts-ai-apps-exposed-by-simple-hacks", "thumbnail": "https://static.wixstatic.com/media/ef0c19_0cafc586d4604b5b8b919da56e36ff0b~mv2.png", "status": "published", "tags": [ "security", "custom-gpt" ], "markdown_file": "output/staging-markdown/code-red-unprotected-gpts-ai-apps-exposed-by-simple-hacks.md", "html_file": "output/html-pages/code-red-unprotected-gpts-ai-apps-exposed-by-simple-hacks.html", "content_length": 13479, "has_images": true, "has_videos": true, "file_sizes": { "markdown": 14032, "html": 17123 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.135347Z", "scraped_from": "output/jina-ai-raw/code-red-unprotected-gpts-ai-apps-exposed-by-simple-hacks.md" }, "youtube_ids": "", "summary": "Security analysis of prompt injection vulnerabilities in Custom GPTs and AI chatbots. Documents hacking techniques: magic prompts, brute force, social engineering, image-embedded injections, malicious URL attacks, and code interpreter exploits. Covers countermeasures: security instruction prompts, disabling code interpreter, ML-based prompt filtering, and third-party security services (Lakera). Discusses trade-offs between security and GPT performance degradation. References OWASP Top 10 for LLMs." }, { "id": "build-machine-learning-model-with-chatgpt-exploratory-data-analysis-eda", "slug": "build-machine-learning-model-with-chatgpt-exploratory-data-analysis-eda", "title": "Building Machine Learning Models with ChatGPT - Part 2: Modeling Process Listing & EDA", "date_published": "2024-02-10T16:15:50.374Z", "date_updated": "2024-04-06T00:00:00Z", "last_modified": "2025-11-27T16:53:01.634411Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/build-machine-learning-model-with-chatgpt-exploratory-data-analysis-eda", "thumbnail": null, "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/build-machine-learning-model-with-chatgpt-exploratory-data-analysis-eda.md", "html_file": "output/html-pages/build-machine-learning-model-with-chatgpt-exploratory-data-analysis-eda.html", "content_length": 1978, "has_images": true, "has_videos": true, "file_sizes": { "markdown": 2989, "html": 5006 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.128879Z", "scraped_from": "output/jina-ai-raw/build-machine-learning-model-with-chatgpt-exploratory-data-analysis-eda.md" }, "youtube_ids": "4TUXDY5MaJ4", "summary": "Part 2 of building ML models with ChatGPT: getting detailed modeling process documentation and exploratory data analysis (EDA). Shows how prompting ChatGPT to share process steps, results, plots, and distributions before model building produces comprehensive EDA output. Demonstrates that specific instructions yield specific outputs, with video walkthrough of the iterative prompt refinement process." }, { "id": "fa18de05", "slug": "fa18de05", "title": "Mutual Fund Portfolio Analysis with ChatGPT: Merging and analyzing across multiple excel files", "date_published": "2024-02-10T16:01:26.248Z", "date_updated": "2024-03-22T00:00:00Z", "last_modified": "2025-11-27T16:53:01.640100Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/fa18de05", "thumbnail": null, "status": "published", "tags": [ "mutual-funds", "custom-gpt" ], "markdown_file": "output/staging-markdown/fa18de05.md", "html_file": "output/html-pages/fa18de05.html", "content_length": 1517, "has_images": true, "has_videos": false, "file_sizes": { "markdown": 1876, "html": 3753 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.148388Z", "scraped_from": "output/jina-ai-raw/fa18de05.md" }, "youtube_ids": "LLBQsE7oemk", "summary": "Using ChatGPT to merge and analyze multiple mutual fund portfolio Excel files (print-formatted, varying formats, containing images) for tracking equity allocation changes over time. Demonstrates merging 12 files in 30 seconds with a single prompt, including handling different filename formats. Covers voice-typed prompt preparation via Google Docs, validation against totals, and option to generate reusable Python code for larger datasets." }, { "id": "build-machine-learning-model-chatgpt", "slug": "build-machine-learning-model-chatgpt", "title": "Build Machine Learning Model with ChatGPT prompts: Random Forest example.", "date_published": "2024-02-10T00:00:00.000Z", "date_updated": null, "last_modified": "2025-11-27T16:53:01.634411Z", "source": "migrated", "original_url": "https://www.tigzig.com/post/build-machine-learning-model-chatgpt", "thumbnail": null, "status": "published", "tags": [ "custom-gpt", "database-ai" ], "markdown_file": "output/staging-markdown/build-machine-learning-model-chatgpt.md", "html_file": "output/html-pages/build-machine-learning-model-chatgpt.html", "content_length": 4355, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 5026, "html": 7215 }, "migration_metadata": { "scraped_at": "2025-11-27T16:51:57.124878Z", "scraped_from": "output/jina-ai-raw/build-machine-learning-model-chatgpt.md" }, "youtube_ids": "4TUXDY5MaJ4", "summary": "Building a Random Forest propensity model entirely through ChatGPT prompts: data preprocessing, model building, validation, decile tables, feature importance, and scoring code generation. Covers tips for managing GPT limits (start with low complexity: 30 trees, depth 3), avoiding overfitting, using Google Colab T4 GPU for intensive tasks, and data security (anonymize PI data, use scrambled data). Includes five sequential base prompts for replication." } ] }