Sugar (or condensed milk) in tea I get often stays unmixed at the bottom. I forget to stir it. Is that poor design that should be fixed? Or is the steadily increasing sweetness of the tea a brilliant feature? Anyway, I therefore lick the bottom of the cup to get every last bit of sweetness. But this cup is so deep that my tongue can just about reach the bottom. And I can’t reach the botom near the handle, because my nose is in the way. ...
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: ...
One of my goals this year is to learn to dance. I haven’t done much about it, but I did snatch one opportunity. At VizChitra, Ashok Kumar led an Afternoon Rhythm “where the drum sets a beat and you find your place within it.” He invited volunteers on stage. I usually volunteer for uncomfortable things (a habit from school days), so I briskly walked to the stage, waited for a few others to join, then started dancing to the beat. ...
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: ...
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.) ...
Sukumar Rajagopal shared a LinkedIn post about our meeting on 11 June at Chennai, saying: Had a fanboi moment recently with Anand S who I have been following for a long time. He is prolific. It’s very difficult, no impossible, to keep up with him. He generously shares his work with full source code; approach; working examples; the whole nine yards. All this I knew before I met him in person. The in-person meeting took me by surprise. ...
I’m doing one extra push-up every day. One of my 2026 goals is to build muscles. I haven’t done anything about it until May. This month, I figured I would do the absolute minimum, at least to get started, because I seem to have starting trouble more than anything else. I asked ChatGPT: I want to build muscles. What’s the most effective thing that I can do that would take no more than one minute that I can practice every day without any equipment and I can do this anywhere and will have the most impact on building muscles? Research, give me the top five options and recommend one for me. ...
Here’s another AI-generated post, with Anand editor notes. But I’ve also added my own version of the post below. I told my “find a free calendar slot” script to “Avoid weekends and holidays”. Wednesday vanished. Turns out it’s a Singapore holiday (Anand: It’s Eid al-Adha), — irrelevant for the people I was meeting in other zones. I’d debugged my own helpful rule. (Anand: What? What does “debugged my own helpful rule” even mean?) ...
A podcast host sent me eight questions. Instead of rehearsing answers in my head, I used ChatGPT with Local MCP to read 6 months of call transcripts and find the best examples: Iteration 1: Here are questions I have been asked to answer in a podcast. Help me prepare with examples. For each question, go through my transcripts or emails and find examples relevant to the question and share (for each relevant example) a summary, how it’s relevant, and the relevant verbatim quotes from the transcript. Iteration 2: Mention WHO said it. Emphasize the most important parts. Do a second pass. More examples. Disprove your own hypotheses with evidence to the contrary and retain what remains robust. Iteration 3: Do a third pass. Find more real-life examples. Try and disprove yourself even harder. Share the best examples for what survives - not all. Same format. Iteration 4: Ensure diversity of client examples. For example, in Q2, all three are the same client. Extend to add / replace examples - ideally with better ones. Then I used Claude with examples of my writing style to summarize it in my voice. ...
I’m making more and more of my content agent-consumable, i.e. easier for ChatGPT, Claude Code, etc. to read, in three ways. One, I export content in an agent-friendly way. Google email, calendar, chat. I use gws to back up into scannable one-line entries. Meet recordings. I back up transcripts and videos (with a compact audio copy). WhatsApp chats that I back up into similar one-liners. Browsing history by exporting my Edge history SQLite database. Daily activities by integrating the above with my command line and commit history. AI conversations by exporting them manually or via bookmarklets. Social media records like LinkedIn invites/conversations, Twitter, Hacker News, Discourse, etc via bookmarklets or scripts. Financial records like bank statements, receipts, payslips, tax filings, utility payments, rentals, property records, investments, insurance, pensions, invoices, credit scores, etc. by exporting them manually. Medical records like tests, prescriptions, doctor visits, etc. by exporting them manually. Personal records like certificates, educational records, CV, passport / visa applications, etc. by exporting them manually. Two, I log / generate more content. For example: ...
I’d love for Claude or ChatGPT to answer questions like: What meetings am I not setting up that I really should be? or: Based on my activities since 9 May 2026, what should I blog about? or: Who in my professional life most deserves an unreasonable gesture? From data. My files, emails, calendar, contacts, transcripts, blogs, notes, code, browsing history, logs, random Markdown files I forgot I wrote. Hence, a Local MCP. ...
I’m a man of simple needs. All I want is: when I’m on Google Meet, I turn on captions. I wanted to click a bookmarklet and save those captions into a local Markdown file. (So that an AI agent can guide me from it.) Hence, Google Meet Captions. The code is in gmeetcaptions/. Drag the button to your bookmarks bar. Join a Meet. Turn on captions. Click it. You get a tiny panel with two buttons: Copy and Start Recording. ...
Based on 44 meeting recordings from February to late April 2026, here’s how Straive’s small team (3-6 people at any time, mostly freshers and interns) produce a continuous stream of client-facing demos across topics as diverse as image filtering, geospatial analysis, insurance contract verification, NFL medical scoring, OCR benchmarking, and song similarity clustering — often with a 24–48 hour turnaround from assignment to demo. Here is how the team works: ...
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. ...
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. ...
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. ...
I’ve been using AI in my Tools in Data Science course for over two years - to teach AI, and using AI to teach. I told GitHub Copilot (prompt) to go through my transcripts, blog posts, code, and things I learned since 2024 to list my every experiment in AI education, rating it on importance and novelty. Here is the full list of my experiments. 1. Teach using exams and prompts, not content ⭐ Use exams to teach. The typical student is busy. They want grades, not learning. They’ll write the exams, but not read the content. So, I moved the course material into the questions. If they can answer the question, great. Skip the content. Use AI to generate the content. I used to write content. Then I linked to the best content online – it’s better than mine. Now, AI drafts comics, interactive explainers, and simulators. My job is to pick good topics and generate in good formats. Give them prompts directly. Skip the content! I generated them with prompts anyway. Give students the prompts directly. They can use better AI models, revise the prompts, and learn how to learn with AI. ⭐ Add an “Ask AI” button. Make it easy for students to use ChatGPT. Stop pretending that real-world problem solving is closed-book and solo. ⭐ Make test cases teach, not just grade. Automate the testing (with code or AI). Good test cases show students the kind of mistake they may - teaching them, not just grading them. That’s great for teachers to analyze, too. Test first, then teach from the mistakes. Let them solve problems first. Then teach them, focusing on what failed. AI does the work; humans handle what AI can’t. This lets us teach really useful skills based on real mistakes. 2. Make cheating pointless through design, not detection ...
When people ask me for connections, I share my LinkedIn data and ask them to pick. This week, three people asked for AI ideas. I shared my local content with AI coding agents and asked them to pick. STEP 1: Give access to content. I use a Dockerfile and script to isolate coding agents. To give access, I run: dev.sh -v /home/sanand/code/blog/:/home/sanand/code/blog/:ro \ -v /home/sanand/code/til:/home/sanand/code/til:ro \ -v /home/sanand/Dropbox/notes/transcripts:/home/sanand/Dropbox/notes/transcripts:ro This gives read-only access to my blog, things I learned, transcripts, and I can add more. (My transcripts are private, the rest are public.) ...
I created an embedding map of my blog posts. Each point is a blog post. Similar posts are closer to each other. They’re colored by category. I’ve been blogging since 1999 and over time, my posts have evolved. 1999-2005: mostly links. I started by link-blogging 2005-2007: mostly quizzes, how I do things, Excel tips, etc. 2008-2014: mostly coding, how I do things and business realities 2015-2019: mostly nothing 2019-2023: mostly LinkedIn with some data and how I do things 2024-2026: mostly LLMs … and this transition is entirely visible in the embedding space. ...
I often research how the world will change with AI by asking AI. Today’s session was informative. I asked Claude, roughly Economics changes human behavior. As intelligence cost falls to zero, here are some changes in my behavior [I listed these]. Others will have experienced behavioral changes too. Search online and synthesize behavioral changes. It said this. 🟡 People spend time on problem framing & evaluation. AI can execute the middle. (I’m OK at this. Need to do more framing + evaluation.) 🟢 People don’t plan, they just build. (I’m prototyping a lot.) 🟢 People build personal data & context. (I’m mining my digital exhaust.) 🔴 People queue work for agents, delegating into the future. (I’m not. I need to do far more of this.) 🟢 People shift from searching to asking for answers. (I do this a lot, e.g. this post.) 🟡 People are AI-delegating junior jobs and developing senior level taste early. (Need to do more.) 🟡 People treat unresolved emotions as prompts. (Need to do more.) Rough legend: 🟢 = Stuff I know. 🟡 = I kind-of know. 🔴 = New learning. ...