LLMs

I ran, an experiment in Straive. I asked my colleagues to mail me any questions or tasks that they had. I’d use my agents + my knowledge as context and reply based on that. A few interesting things came out of it. First: It often does a better job than me - it has more patience and speed. For example: One colleague sent a detailed pitch. My agent spotted a tiny arithmetic averaging error. Another asked if I knew an ontology SME. The agent found a perfect match in my contacts - someone I would not have thought of (and reached out to.) Another asked why a slide (screenshot) was empty. The agent searched Google Drive, found the slide, analyzed it, and replied: “It’s white-on-white text. But don’t bother. The slide two pages earlier is better for your meeting!” One colleague asked: “Instead of sending this to your email id, why can’t we ask this directly into Claude or so?” It replied: ...

Many people commented that they liked my comic illustrations and asked how I create them. Here is my process: Paste a reusable prompt fragment that’ll take any content, think about what to draw, then draw it. Paste a style variation for different comic styles (optional). Paste the content itself and run it. I use ChatGPT with gpt-image-2 more often than Gemini with Nano banana 2. Here are the prompts. STEP 1: Reusable Prompt Fragment: I have a few of these right now: ...

I’m a fan of the Code Mode approach - i.e. letting agents run code rather than narrow functions. Many people agree: CircleCI, Perplexity, etc. In fact, mcp2cli gives MCPs a CLI interface. I feel the main reason is UNIX composability. I can run CLI commands in a loop, pipe them, etc. I tested it out on ChatGPT. ChatGPT has an @Gmail Plugin. I build a Local MCP Server that exposes my CLIs, including gws (Google Workspace CLI). I gave it 3 tasks in a single prompt: ...

On 16 July, my auditor sent me a draft Indian tax return: a refund of Rs 3 lakhs. I gave ChatGPT my AIS, Form 26AS, bank statements, mutual fund statements, property papers, travel records, prior returns, and so on, told it not to look at the auditor’s draft, and asked it to calculate my tax independently. It calculated a refund of about Rs 2.8 lakhs, roughly Rs 20K lower. (Less money, but a smaller refund felt less worse than a larger tax.) ...

It’s amazing to watch the cost of intelligence falling. In Nov 2023, we had college-junior level intelligence for $10 per million tokens, i.e. it would take them $10 to read and process something as large as all seven Harry Potter books. ...

As agents get smarter, and when we ask questions outside our expertise, it’s pretty hard to understand what they’re saying. Andrew Carr uses “only report to me in ASD-STE100 Simplified Technical English” to simplify their writing. Ben Sehl suggested making this a permanent instruction. But, does simplifying the writing worsen their thinking? I tested six tasks on ChatGPT (GPT 5.6 Sol), with and without this suffix: “Answer in ASD-STE100”. ...

Agents are increasingly the consumers of things that used to be made for humans: Docs are increasingly for agents to read. E.g. llms.txt Code is increasingly for agents to edit. E.g. AGENTS.md Tests are increasingly for agents to satisfy. E.g. SWE-bench, GitHub repository Software is increasingly for agents to operate. E.g. --json, --schema, --dry-run, CLI-Anything, AXI Websites are increasingly for agents to invoke. E.g. WebMCP Capabilities are increasingly for agents to discover. E.g. MCP tools, MCP Registry Procedures are increasingly for agents to follow. E.g. SKILL.md, Agent Skills Products are increasingly for agents to choose. E.g. Universal Commerce Protocol Money is increasingly for agents to spend. E.g. Agent Payments Protocol Identity is increasingly for agents to prove. E.g. Visa Trusted Agent Protocol Agents are increasingly for agents to delegate to. E.g. A2A Agent Cards and Tasks Humans are increasingly for agents to escalate to. E.g. MCP Elicitation Agent Experience or AX is the new UX. ...

I track the cost vs capability of LLMs at LLM Pricing - the rough cost to read all Harry Potters (~1M tokens) vs the intelligence level on the LMSYS Leaderboard - over time. Here’s what the models’ strategy evolution looks like. Claude started at the mid-to-high end of the cost-capability frontier. Over time, they decided to specialize in the high-end, which they’re doing well on. ...

Bring AI to where people already work: email Lots of companies are putting AI into their chat applications. Add Claude to a Slack channel, tell “@Claude” to do something, and it reads the conversation, uses tools, does what you tell it to, and replies in the same chat. Nice, for companies that use Slack a lot. (Many do. We don’t.) Straive and many of our clients use email more. There’s Google Chat, Teams, and others too, but email’s what most people access. (Apart from WhatsApp.) ...

One of my goals this year is to publish 2 books. One got published. Sort of. Data Science for Sustainable Development Goals: India Case Studies is an open-access anthology and I’m the designated author of Chapter 10: Using Data Analytics to Improve Students’ Performance is about how Gramener worked with NCERT to analyze the National Achievement Survey data, discovering stuff like TV hurts maths but not reading scores, playing helps maths but not reading scores, fathers of West Bengal (not mothers) and mothers of Punjab (not fathers) influence their children’s scores the strongest, and so on. ...

At The Curzon Hotel, my key card didn’t work. But every time I went to the reception, they’d send a bellboy who would use the same key card, jiggle it a bit, pull it in and out a few times, and the door would open. Every night. For five nights. I just couldn’t get the knack of it. I’ve been at the other end of this. People often reach out to me saying, “Anand, this software isn’t working.” Then I go do the same thing they did, and it works. (Sometimes, I just need to watch them do it and it works.) ...

I had Claude Code with Fable create a small scrollytelling map for my 14-minute walk experience at Bagmane Capital in Bangalore. I used this as an opportunity to explore the current status of the technology. ChatGPT suggested: Try ArcGIS StoryMaps first for a polished scrollytelling story. Try Google Earth Projects if this is primarily something you will present live, like a map-based slide deck. Use MapLibre GL JS with a coding agent if you want precise choreography, animated routes, unusual visual effects, or an asset you can continually extend. None of these fit my requirements, which was: ...

Here’s roughly what I said in my When Data is for Agents workshop for Fifth Elephant on 7 Jul 2026. Or you can read the detailed AI-generated version if you prefer - it has all the prompts, links, results, etc. I think agents prefer data in a different form than humans. But I don’t know. So, everyone, open ChatGPT (or Claude or whatever), research and ask it! Now, let’s collate them and see the result. Aha! Looks like: ...

Last week at VizChitra, I ran a “Dialogue” session. A new format for me. I usually speak 80% in my workshops. In this dialog, I spoke 20%. The group discussed. PART A I showed 6 charts and said, “Pick the best.” Then I shared the audience & purpose and asked: “For THIS audience and purpose, will you publish, fix, or kill it?” INSIGHT: almost no one said, “Ship”. That’s good – these were all drafts. ...

Ranjeeta asked me for an article for Built In. I went straight to ChatGPT and said: Write an article for Built In. Read the section below for context on Built In's audience, style, and content preferences. Read the pitch that explains what the article should be about. Then use my blog posts, talk content, transcripts, TIL, etc. Write an article using my writing style. … and gave it all related information. ...

For thirty years, we cleaned data for humans. Now, agents are reading it. What do we do differently? On Tue 𝟳 Jul 𝟮𝟬𝟮𝟲, 𝟮-𝟲pm IST, I’m running a Fifth Elephant workshop: “When Data is for Agents, Not Humans”. I have a few theories we’ll be testing on your data. Like: Tools beat tables. Agents prefers functions over tables they can read. Logs beat docs. Logs show actual usage - not how we’re supposed to use it. Messy beats clean. Agents can do clean & join data. You just need descriptions. ...

Yesterday, I wrote about @syu-toutousai, the bounty-hunting agent ecosystem. That led me to OpenAgents. OpenAgents has plenty of bounty issues: Fix JWT auth middleware accepts algorithm none - $8k Fix rate limiter doesn’t differentiate authenticated vs anonymous limits - $2.2k Add structured error responses with error codes - $8.6k Fix Math.random used for nonce generation - $8k Fix ABI encoding BigInt overflow - $9k Most issues also include a trick requirement. For example, #100 asks contributors to add a @generated-by block with: ...

Yesterday, I submitted a Codex co-authored PR to fix an issue I raised (using ChatGPT and Z3 - so yeah, I used AI to raise the bug and squash the bug!) A few hours later, @syu-toutousai submitted another PR to solve the same issue. @syu-toutousai seems interesting. The user account description says “Autonomous Technical Contributor & AI-Driven Developer” - a bot account. The PR itself was simple and had a few improvements I can think of: ...

On Sat 4 July at Bangalore, Arvind Satyanarayan is speaking at VizChitra 2026 - a talk I’m keenly looking forward to. I’ve been following Arvind’s work since Vega-Lite. It’s a grammar of graphics - something that makes data visualizations (charts) more structured. I tried switching to it our default at Gramener - but most felt it was too much to learn (they already knew Excel/Power BI) or too limiting (D3 can do more). ...

At the PyCon SG Education Summit today, Melvin’s lighting talk on “Writing Proofs in Python” began with a subtle bug in this mid-point calculation (often used in binary search or sort) in languages like Java, C/C++, Go, etc. low = ... high = ... mid = (low + high) / 2 Since the integers are fixed-width, this triggers an overflow when low + high exceeds the maximum integer value. Even popular libraries like Pandas had this bug until 2019. In fact, even Python’s native list.sort() had this sort of bug until 2015! Read the details. ...