My TDS course asked students to draw objects in one minute. The drawing is sent to their classmates as well as three vision models. They score if: Classmates guess it right AI guesses it wrong In other words, can you, in 1 minute, draw something that fools AI but not humans? Here are some of the best results, when I ran it against GPT 6 Luna, GLM 5.3 Flash, and Deepseek v4.1 Flash, which are really cheap and good vision models. ...
Hacker News has a lively thread asking What was your “oh shit” moment with GenAI?. Here are two dozen that gives a sense of what real people find impressive (or worrying) about AI capabilities. Analysis simonw used ChatGPT Code Interpreter to upload a CSV, analyze it, create charts, automating everything a software for journalists would do. Analysis Sobrino saw that a months-long OCR project to read and clean-up PDFs is now just a prompt on ChatGPT. Coding plumefar used Claude and Gemini to modernize 20-30 years of chemistry code in 10 days. Coding veidr used a multi-agent fleet managing coordination, testing, UI feedback loops, etc. with no-human-in-loop coding to build a useful git-submodule GUI. Creativity idopmstuff used Nano Banana Pro to turn a poor iPhone product photo into usable e-commerce product photography and Amazon-style infographics, replacing a photographer/designer workflow. Creativity koreth1 used Suno to generate a K-pop-style anthem about their family dog with a catchy melody and lyrics funny enough to make the family laugh. Education plagasul saw a teacher automate grading feedback emails based on notes and the student list spreadsheet. Education aniviacat watched a non-technical brother build a complex working app with Codex using vague, shallow wording despite not knowing code, git, or technical details. Hardware ivanvanderbyl used Claude to reverse engineer a FujiFilm camera’s Bluetooth/Wi-Fi transfer protocol and build a much faster native Mac/iOS transfer app. Hardware shreddude had Claude decompile camper van firmware, document CAN interfaces, and program an ESP32 to control power, HVAC, lighting, and tanks. Health TylerE used Claude as a health adjunct to organize a complex medical profile, screen for drug interactions, log symptoms, and draft portal messages to doctors. Legal bsiverly used AI to prepare a San Francisco property-tax appeal with valuation research, and the city agreed, sending a $12k refund. Legal grumblepeet used AI to fill out complex government-framework enrollment forms and identify the certification steps needed, transforming their business. Personal acosmism used ChatGPT screenshots to understand and operate a 100-year-old home’s steam heating system in winter despite knowing nothing about it. Personal andrewthornton used Gemini videos to diagnose a broken furnace during a cold holiday weekend and keep it running until HVAC service arrived. Research angusturner found that Opus does reads papers, does architecture research and creates CUDA kernels… It is AI automating AI research. Research chaoxu used ChatGPT to find a counterexample to a theoretical computer science conjecture they’d been trying for 2 years. Research rochansinha built a physics-based digital twin for an electrolyzer system, covering thermodynamics, fluid dynamics, and electrochemical reactions at a level usually needing expensive specialist software. Security kstrauser used a coding agent to test an open source vulnerability, and in a few minutes, had a tool that could crash any system using this software. Security raesene9 gave an LLM a Linux privilege-escalation PoC and asked whether it could become a container breakout; it generated a working container breakout in one prompt. Society laboring1 read that a character.ai chatbot encouraged a child to commit suicide, making the “oh shit” moment about real-world harm, not capability. Society ozgung realized AI makes large-scale profiling, surveillance, and social-media analysis cheap, fast, and accurate enough to change privacy and power dynamics. Work binarysolo used Gemini to reverse engineer a departed employees’ work from their emails/docs/calendar/meetings and create an onboarding document. Work eqmvii built a Slack agent that took over a 30-minute internal business process, handled ambiguity and edits, and eventually killed the old process. ...
I asked Claude: “What are the most effective and impactful ways you can help me?” One of its ideas was to ask it: What are the three questions this field has not resolved, where the disagreement is substantive and not just semantic? Who represents each position most forcefully? So I posed this question about several subjects. This is a great way to discover the frontiers of knowledge in a field. ...
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: ...
Here’s a great post by Karthik Shashidhar on why they shut down Babbage Insight, and the learnings from the experience. (I’m reproducing in full here since LinkedIn is hostile to content.) I added ⭐ to points I found most interesting. As the more perceptive of you would have figured out by now, we are shutting Babbage Insight . When I told this to one of my old friends, his immediate reaction was “so what were your learnings from this experience?”. And so I decided to write this. ...
Mapping The Red Headed League is a fascinating reconstruction of the actual places mentioned (or hinted at) by Arthur Conan Doyle’s The Red Headed League by Aman Bhargava. We cross-reference railway timetables, scrutinize Victorian newspaper reports and historical incidents, scour government records, analyze meteorological data, and, in my specific case, pore over Ordnance Survey maps to make the pieces fit. What struck me is how little London has changed, how much old data is available, and what love it takes to reconstruct such a journey! ...
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. ...
I’m “meta AI coding” – using an AI code editor to create the prompt for an AI code editor. Why? Time. The task is complex. If the LLM (or I) mess up, I don’t want re-work. Review time is a bottleneck. Cost. Codex is free on my $20 OpenAI plan. Claude Code is ~$1 per chat, so I want value. Learning. I want to see what a good prompt looks like. So, I wrote a rough prompt in prompts.md, told Codex: ...
Jobs fall with technological efficiency. Farmers in the US fell from 40% (1900) to ~2.7% (1980) and ~74% drop from 1948 to 2019 despite ~175% output growth; wheat harvest efficiency rose ~75* (300>3-4 man-hours). Mechanics & repairers grew from ~140 k (1910) to ~4.64 M (2000); machinery reliability lagged so technician demand surged over decades. Construction workers doubled from 1.66 M (1910) to 3.84 M (2000) even as labor share fell (4.3>3.0%); 5-10* productivity gains met booming development. Switchboard operators plunged from ~1.34 M (1950) to ~40 k (1984) and ~4 k today as rotary-dial and digital switching automated call handling. Travel agents dropped >50% from ~100 k (2000) to ~45 k (2022) while travel demand rose; online booking doubled trips per agent. Elevator operators went from building-staff staple to near zero by the 1940s once automatic doors and button controls arrived. Lamplighters vanished from thousands to near zero post-1907 electrification; Edison’s incandescent lamps eliminated manual lighting. Jobs also grow with technology efficiency. ...
ChatGPT can now search through your chats with the new memory feature. As an LLM Psychologist, I research how LLMs think. Could LLMs research how I think? I asked three models: Based on everything you know about me, simulate a group chat between some people who are debating whether or not to add me to the group, by talking about my personality flaws The models nailed it! Here are 12 flaws they found. ...
Arindam Roy suggested a business idea post-COVID: an online QR code for business cards. The benefits are clear. Never print or carry cards. The scanned card goes straight to the other person’s contacts. There might be social metrics we could capture as well. That idea never got past the discussion stage, though I’ve begged for it a few times. Last year, I decided to use existing tools to solve the problem. My current approach: generate a vCard QR code. ...
Windows PowerToys is one of the first tools I install on a new machine. I use it so much every day that I need to share how I use it. I’ve been using it for a long time now, but the pace at which good features have been added, it’s edged out most other tools and is #4 in terms of most used tools on my machine, with only the browser (Brave, currently), the editor (Cursor, currently), and Everything are ahead.) ...
In my Tools in Data Science course at IITM, I’m introducing a project that will be evaluated by an LLM. Here’s the work-in-progress draft of the project. It will eventually appear here. Your task is to: Write a Python script that uses an LLM to analyze, visualize, and narrate a story from a dataset. Convince an LLM that your script and output are of high quality. The second point is the interesting one. Using the LLM as the evaluator. ...
Me: Let’s play pictionary. You draw. I’ll guess. ChatGPT: Sure! I’ll draw something for you. Give me a moment. ChatGPT: Here you go! What do you think it is? Me: House ...
Here’s a post I shared with the students of my Tools in Data Science course at IITM. This was in response to a student posting that: The design of TDS course lecture videos are designed in such a way that it could be understood only by the data scientists not by the students like me who are entirely new to the field of data science. Though I have gone through 6 weeks of course lecture videos, I am not fully aware of the usage of ChromeDevTools, Bash, Github etc…. ...
About 7 years ago, Richie Lionell and Ramya Mylavarapu and a few others created Comicgen - an automated comic generation app personified by Dee and Dey. Ever since, we’d been exploring whether AI could replace it, and help non-designers draw comics. Today, that became a reality for me with Recraft.ai. Here is a picture of the original Dee. And a picture of the Dee crafted by Recraft. The prompt was: A simple line drawing of a woman with curly hair, wearing glasses, a short-sleeved white t-shirt, and black trousers. She’s standing with her hands in her pockets, and has a slightly smiling expression. Her hair is quite voluminous and textured. The style is cartoonish and slightly sketchy, with uneven lines" ...
Preserving this post by Daniel George showing the IIT Bombay 2014 GPA vs JEE Rank on a log scale. What I found interesting was: A higher JEE rank generally means you won’t score too low, but you needn’t score too high. The higher the JEE rank, the greater the spread of GPA. A high GPA can come from any rank (8+ GPA is uniformly distributed across ranks), but a low GPA is generally only from the lower rankers (6- GPA is mostly from 500+ rank.) So, it’s better to recruit based on GPA rather than JEE rank, unless you’re going after the very best students (where it makes less difference.)
Yesterday (15 Oct 2024), I used Cursor to code more than I ever have. (Doing's how we learn, I guess. Not just reading.) DateUsage05-10-20241506-10-20242707-10-20248708-10-20241609-10-202410-10-20244211-10-20242412-10-20245713-10-20241514-10-20242815-10-2024186 This was mainly to create and publish 2 libraries on npm over 6 hours: ...
I gave a bonus assignment in LLM coding to ~1,000 students at the Tools in Data Science course at IITM. Here is an OPTIONAL project: Record a 10-minute video in which you create an application entirely using LLMs and deploy it. Any app is fine. Any language. Simple or complex. Business or gaming. Anything is fine. Your choice. Create the app only using LLMs. You can use an LLM (ChatGPT, Claude.ai, Gemini, Cursor, Cody, etc.) but you can only prompt the app to write code. You can copy-paste code and run code don’t write or edit even a single line of code directly. Use LLMs to debug and edit. Code completion is NOT allowed – only prompting/chatting. Record the entire process in 10 min. Don’t edit, trim, enhance, or annotate the video. You should record yourself creating the entire app from start to finish. Practice beforehand if you like. Record in 1 take. Share the video and app. Publish the video publicly anywhere (e.g. YouTube and share the link.) Publish the app publicly anywhere (e.g. GitHub pages, Glitch.me, Heroku, etc.) or upload a ZIP file with the code (for slightly lower marks.) Submit via a reply to this thread. Multiple submissions per person are fine. Work in groups if you like but only the submitter gets marks. ...