October 2025

I charted the OpenAI GDPVal paper with industry compensation as the size and AI augmentation as color. Big green areas are we’re paying people where AI does better. Click here to see the interactive visualization. Clicking to see some actual tasks compared. I use this to check whom to ask advice: AI or professional. AI beats Personal Financial Advisors ~64% of the time. So I invested half my money using ChatGPT’s recommendation. (UTI Nifty 50, if you’re curious.) ...

I don’t charge for workshops. Altruism? No: it’s self-interest. “If you’re not paying for it, you’re not the customer; you’re the product being sold.” Andrew Lewis, via Tim O’Reilly, 2010. My workshop process is designed to benefit me first. I pick topics I want to learn, not stuff useful to the audience. Example: I picked DuckDB for my PyCon India 2025 talk to learn it. ...

I was testing LLMs’ sense of Tamil humor with this quote: Extend this post with more funny Tamil words that end with .ai - mentioning why they’re funny. Chenn.ai is the artificial intelligence capital of India. Kadal.ai Kad.ai Dos.ai Vad.ai Ad.ai Thal.ai Mallig.ai Aratt.ai And finally Podad.ai All spoken in namma bash.ai 😅 The Chinese models didn’t fare well. DeepSeek made up words. Mood.ai - An AI that perfectly captures your mood. Sokk.ai - The AI for when you’re bored. Thanni.ai - A hydration assistant. Qwen too. ...

Can ChatGPT give teachers data-driven heuristics on student grades? I uploaded last term’s scores from about 1,700 students in my Tools in Data Science course and asked ChatGPT: This sheet contains the scores of students … (and explained the columns). I want to find out what are the best predictors of the total plus bonus… (and explained how scores are calculated). I am looking for simple statements with 80%+ correctness along the lines of: ...

Last weekend, I fed Codex my browser history and said “explore.” It found a pattern I call rabbit holes – three ways we browse: Linear spiral - one page > next page > next. E.g. filing income tax, clicking “next” on the PyCon schedule. Hub & spoke - hub > open tabs > back to hub. E.g. exploring DHH’s Ubuntu setup, checking Firebase config. Wide survey - source > many, many pages. E.g. clearing inbox, scanning news. Then Claude Code built this lovely data story. ...

Yesterday, I discovered how much reasoning improves model quality. My Tools in Data Science assignment asks students to draft an llms.txt file for ipify and auto-checks with GPT-5 Nano - a fast, cheap reasoning model. I set reasoning_effort to minimal and ran this checklist: 1. Starts with "# ipify" and explains ipify. 2. Markdown sections on API access, support (e.g. GitHub, libraries). 3. Covers API endpoints (IPv4, IPv6, universal) and formats (text, JSON, JSONP). 4. Mentions free, no-auth usage, availability, open-source, safeguards. 5. Has maintenance metadata (e.g. "Last updated: <Month YYYY>"). 6. Mentions robots.txt alignment. Stay concise (no filler, <= ~15 links). If even one checklist item is missing or wrong, fail it. Respond with EXACTLY one line: PASS - <brief justification> or FAIL - <brief explanation of the first failed item>. With a perfect llms.txt, it claimed “Metadata section is missing” and “JSONP not mentioned” – though both were present. ...