My wife’s sambar tastes different from my mother’s. And mine, too. When I cooked as a bachelor, my neighbour would pop by, taste the sambar, and exclaim, “Rasam super!” Surbhi’s Day 5 of the 30-day challenge was about Sambar which inspired me to take her dataset and create a decision tree for which state a sambar recipe is from based on its ingredients. ChatGPT started with 68 recipes and built a tree at 41% accuracy. As we added more recipes: ...

In Panchayat Season 1 Episode 7 Ladka Tez Hai Lekin…, at around 17:00, Pradhan asks Abhishek to solve problem 42. 42. A takes 5 days more than B to do a certain job and 9 days more than C. A and B together can do the job in the same time as C. How many days would A take to do it? (a) 16 days (b) 18 days (c) 15 days (d) 20 days The correct answer is (c) 15 days. But interestingly, ChatGPT got it wrong the first time too. It said (a) 15 days instead of (c) 15 days, and required a fact-check to correct itself. ...

I updated my AI Advice page by: Transcribing my calls in the last 2 months (Gemini 3.1 Pro, “Transcribe this call recording…”) Extracting AI advice (Gemini 3 Flash, “Summarize ALL AI-related advice … into 1-sentence bullets”) Asking Claude, ChatGPT, and Gemini to document what’s new / changed. I added this request: But, and this is IMPORTANT, analyze my original writing style, write it exactly in that style, and then verify to make sure it follows the same style (correcting where required.) ...

For a typical GDPVal style task, humans take ~7 hours and the brain consumes ~135 Wh. Frontier LLM agents spend 50-500 Wh. So, we may already be 3x more or less efficient than the brain. Roughly in the same ballbark! ...

I use ChatGPT to recommend which restaurant I should eat at and what food I should eat. So often that I decided to share a profile of my eating preferences. But rather than think about it and type it myself, I asked it to Efficiently interview me to identify my food preferences. Document it for AI agents to help me pick restaurants. Plan like an expert. (Knowing ChatGPT, I also had add “efficiently” - otherwise it would give me a huge list of questions! Which it did that anyway…) ...

Increasingly, I’m using Codex (or other AI coding agents) as the “operating system” to run programs. That is, rather than directly run programs, I have the coding agent run the program. Advantage: If the program breaks, or needs a configuration change, the coding agent debugs it and fixes it. I don’t need to do anything. This is particularly useful for installation. For example: Install demucs and run it against my music folder. ...

The natural capability of Generative AI is to generate stuff - and Gemini’s particularly good with media. For example, we can take any document, like this MasterCard report on The State of Open Finance 2026, and generate videos, podcasts, sketchnotes, songs, and more from it. How? I uploaded the PDF to NotebookLM and created a 20-minute podcast by clicking on Generate Audio Overview - Deep Dive - English - Default. Listen to the English podcast It supports multiple languages, so I generated a Chinese and Filipino version as well. ...

This is surprising because… well, we’re just sitting and the vehicle’s doing the work, right? But: Vehicles accelerate, brake, bump, turn, vibrate, … and our muscles micro-adjust continously so we sit upright. Over hours hours, that’s a lot of energy. We feel like we’re still. But the inner-ear fluids, eyes, etc. constantly get feedback about motion. That mentally drains us (and causes motion sickness). Noise from vehicles, traffic, … triggers cortisol, a stress hormone. That drains us. Sitting in one place restricts blood flow and it pools in our legs, making the heart work harder. In flights, the air pressure is low, lowering oxygen levels. The dehydration thickens our blood, making pumping harder. What helps is: ...

I have a bunch of coding agent skills I’ve accumulated over the last few months. Here’s how often my sessions use them: Skill Claude Codex Copilot Overall code 6.1% 69.1% 37.5% 51.5% data-story 48.7% 16.4% 37.5% 28.0% data-analysis 2.6% 35.2% 7.8% 21.8% design 25.5% 23.6% 14.1% 21.8% plan 8.5% 11.8% 14.1% 11.8% agent-friendly-cli 3.7% 13.8% 11.1% 11.2% devtools 20.4% 7.3% 9.4% 10.0% llm 2.5% 8.7% 7.8% 7.4% pdf 0.0% 7.9% 7.8% 6.6% linkedin-cdp 14.3% 0.0% 5.6% 5.3% uv-uvx 0.0% 9.5% 0.0% 4.9% interactive-storytelling 7.1% 2.7% 7.1% 4.6% demos 8.5% 2.8% 1.6% 3.5% cloudflare 0.0% 4.3% 3.1% 3.3% melt-mlt 0.0% 2.5% 1.6% 1.8% vector-art 2.5% 2.4% 0.0% 1.7% vitest-dom 0.0% 2.2% 0.0% 1.4% memorable-explanations 2.6% 1.6% 0.0% 1.3% npm-packages 0.0% 0.6% 0.0% 0.3% Here are my observations, with surprises highlighted as ⁉️ ...

I use this prompt to generate sketchnotes on Gemini: Draw this as a visually rich, intricately detailed, colorful, and funny, sketchnote. Below that, I paste (or attach) whatever content I want it to draw. I also turn on “Create Images” and switch the model to “Pro” (for better thinking.) Here are some examples of how to use it. Summarize articles. Pick email, report, news, or website. Here’s a sketchnote for this article: How to use AI for research. I used the prompt above and pasted the article text. ...

To teach a mindshift change like AI adoption, I’ve tried to: Workshop: get them to do it. “Let’s try something. Can you share your screen?” Live-code: show them how. “I’ll share screens and tyep this.” Demo: show what’s possible. “Here’s what I built.” Talk: explain it. “Here’s something we can build.” Interview: ask them about it. “What do you think?” Listen: let them yap. The most effective are on top. ...

Lyria (Gemini’s new “Create Song” feature) is helping me in new ways. Earlier this week, it created a jingle for my talk. Yesterday I ran an AI Workshop for IAS officers. As part of that, I asked Gemini: Create a soulful vote of thanks (with patriotic Indian music playing in the background) naming each of these people. … and listed each person in the workshop. The song began… (Listen to the song) … with these lyrics: ...

I deliver about 3-5 talks a month and usually prepare for them. Thanks to AI (but even otherwise), I have a steady stream of new content. So, I just to assemble the story. For example, in my TEDx Whitefield talk “Prisoners of Birth”, I shared the impact of name, gender, lineage, place, and time of birth. I didn’t execute any new analysis. I just cherry-picked disparate analyses into a theme. (Took me three days to plan, though.) ...

At Changi Airport, I arrived 2.5 hours early and was worried that the flight was boarding on time - because I wanted to charge my laptop so it would work longer on a 6-hour flight to Delhi. I was also sad that it was only a 6-hour flight Delhi - it won’t be enough to read all my pending reading material. The only time I get to read stuff (instead of vibe-coding) is on a flight, with no WiFi. ...

Tools in Data Science has a remote online exam (ROE). It has a tough reputation. We conducted one today. Here’s how today’s ROE unfolded. The TAs had created 13 questions and shared it with me yesterday. This morning, I tried solving them. At first glance, it looked scarily hard! But I just jumpted down a few questions, and found that five questions were trivial, i.e. I just used the “Ask AI” button to copy the question into ChatGPT and it gave me the answer. ...

I asked ChatGPT to research universities’ AI policies. Here is the report Here are the four lessons I learned from that - about how to use AI for research. 1. Show examples of failures to avoid. Jivraj’s earlier research kept surfacing AI policies universities had researched, not written for themselves!. So I told ChatGPT to: … double-check that they ARE, in fact, about their own use of AI - not policies they’re proposing for others or are researching. ...

I researched the AI policies across 25 universities. In the last 6 months, I conducted sessions at three of these Universities: IIT Madras, Singapore University of Technology and Design, and Ashoka University. Interestingly, these are the three lowest ranked universities in my analysis of AI policies. This is where I’m glad that correlation does not imply causation.

TDS Project 1 wasn’t just a student project. It was a research and social experiment, too. We tested two skills - analytics and design. The design tests were diverse – and students fared worse there. Design may matter more in the AI era, and I’m glad some designs are brilliant. (But not diverse/creative enough.) I also learnt that Gemini beats Midjourney, which beats ChatGPT for image generation. I asked them to contribute to open source. Most PRs were trivial. But five students made a real difference. For example, this PR to Marimo is excellent! ...

I was watching Vaa Vaathiyar which has a short clip of MGR speaking. It’s either AI-generated or mimic-ed and it wasn’t bad. I used ffmpeg to record the audio from the film, transcribed it via Gemini 3 Pro on AI Studio with the prompt: Transcribe this into Tamil … which gave me: ராமு… என்ன செய்திருக்கிறாய் நீ… வாத்தியார் கேட்கிறேன் சொல் நிமிர்ந்து பார்க்க கூட தைரியம் இல்லையா… ஓடாதே… நில்… Translation: Ramu… What have you done… Vaathiyar (MGR) is asking, tell me Don’t you have the courage to stand up and look at me… Don’t run… stop… ...