{ "metadata": { "generated_at": "2025-12-26T10:00:00.000Z", "version": "1.0", "total_posts": 174, "migrated_posts": 108, "fresh_posts": 12, "posts_with_images": 44, "posts_with_videos": 7, "youtube_extraction_date": "2025-12-10T09:22:58Z", "posts_with_youtube": 1 }, "posts": [ { "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": "published", "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": "pending", "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 } }, { "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": "pending", "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 } }, { "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": "pending" }, { "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": "pending" }, { "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": "pending" }, { "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": "pending" }, { "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: Combining the 4 for power impact.", "date_published": "2026-03-04T10:00:00.000Z", "date_updated": null, "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": "Tool Builders Infra Guide - Part 5: Set Up Perimeter Security (Edge Defense) for Your Apps on Cloudflare's Free Plan", "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/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": "posted", "tweet_id": "2028845744330047544" }, { "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": "You can set a per-IP rate limit on Cloudflare free plan... stops an attack right at the edge before it touches your app. But not so straightforward...", "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": "pending", "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." }, { "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." }, { "id": "fast-tips-what-is-cors-and-how-to-fix-it", "slug": "fast-tips-what-is-cors-and-how-to-fix-it", "title": "Infra Guide for AI Tool Builders - Part 4: CORS in Simple Words: What It Is and How to Fix It", "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/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": "Explains CORS (Cross-Origin Resource Sharing) as a browser security feature, including preflight requests for non-simple HTTP methods. Covers three proxy solutions: Cloudflare Workers (free, pure pass-through), Vercel serverless functions (mini backend with 5-minute timeout), and FastAPI backend (for Python-heavy processing). Warns against the mode: 'no-cors' trap. Part 4 of the 2026 infrastructure guide series." }, { "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": "posted", "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." }, { "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": "posted", "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." }, { "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/c;laudeNifty50_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/c;laudeExcelRBIMacor_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 24GB RAM server free - what I call the 'VPS Lottery'. Problem - hard to get. Solution - automated scripts and patience.", "date_published": "2026-01-30T10:00:00.000Z", "date_updated": null, "last_modified": "2026-01-30T10:00:00.000Z", "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": null, "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": null, "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": null, "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": "self-hosting-infrastructure-small-business-2025", "slug": "self-hosting-infrastructure-small-business-2025", "title": "Building & Deploying AI Apps: Infrastructure Guide (VPS, Security, Monitoring, Costs)", "date_published": "2025-12-27T00:00:00.000Z", "date_updated": null, "last_modified": "2025-12-27T10:00:00.000Z", "source": "fresh", "original_url": "https://www.tigzig.com/post/self-hosting-infrastructure-small-business-2025", "thumbnail": null, "status": "draft", "tags": [ "infrastructure", "security" ], "markdown_file": "output/markdown-manually-fixed/self-hosting-infrastructure-small-business-2025.md", "html_file": "output/html-pages-manually-fixed/self-hosting-infrastructure-small-business-2025.html", "content_length": 0, "has_images": false, "has_videos": false, "file_sizes": { "markdown": 0, "html": 0 }, "migration_metadata": { "scraped_at": "2025-12-27T10:00:00.000Z", "scraped_from": null, "round": null }, "youtube_ids": "", "summary": "Comprehensive infrastructure guide covering Hetzner VPS (8GB RAM, 4 vCPUs, 5.99 EUR/month) hosting 30+ FastAPI backends, Coolify for deployment, fail2ban configuration after bot attack, SSH hardening, UFW firewall, Vercel for 40+ frontends, Cloudflare DNS with caching rules, custom API monitoring via PyPI package (tigzig-api-monitor), PostHog analytics, Brevo emails, and Auth0 authentication. Total cost under $10/month plus Claude Code." }, { "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://app.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 Added to Database AI Suite. Tested Against Claude Sonnet 4.5 and GPT-5.1.Results: Claude still leads. GPT-5.1 is solid. Gemini 3 Pro lands third.", "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 app.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. AI, Scraping, APIs, EDA, DB, Charts, PDFs, Automations", "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": "LLM App | FastAPI Server | Web", "date_published": "2024-03-20T13:07:45.498Z", "date_updated": null, "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." } ] }