I tested Cloudflare Browser Run and GPT 5.5, added gpt-image-2 to my art gallery, and learned Pandoc tricks for Word comments. I also found a workaround for nested git repositories and explored MCP server auth complexities.

This week, I learned: mdq is pretty useful to extract Markdown sections. For example cat .md | mdq '# Title' extracts all sections where the header contains 'Title' (case-insensitive). CloudFlare Browser Run is, roughly, a browser as a service. Pricing: 10 hours free per month, then 9c per hour. I had Codex run a small research to explore it, and it seems simple to set it up and use it. GPT 5.5 seems to be especially better than GPT 5.4 and running for long, with tool calls, without losing focus. That's something OpenAI models are good at anyway, so this takes it a step further. ChatGPT I added gpt-image-2 to my LLM Art Style gallery. It is notably better with text accuracy. For example, on Rock - Paper - Scissors - Lizard - Spock it consistently lists all 10 rules, which Nano Banana 2 does not. World leaders do keep us entertained. Saparmurat Niyazov (Turkmenistan) renamed the months of the year and days of the week after himself and his mother. He built a towering, gold-plated statue of himself in the capital that rotated so it would always face the sun. He also banned lip-syncing at concerts, outlawed gold teeth, and banished dogs from the capital because he found their smell unappealing. Idi Amin (Uganda) declared himself the "Uncrowned King of Scotland" and sent baffling, unsolicited telegrams to world leaders - advising Richard Nixon to recover from Watergate, or offering food aid to a struggling Britain. François "Papa Doc" Duvalier (Haiti) reportedly ordered all black dogs in Haiti to be put to death and claimed his personal Vodou curse was responsible for the assassination of John F. Kennedy. Francisco Macías Nguema (Equatorial Guinea) banned the word "intellectual", banned the use of lubricants in the power plant (claiming his magic would keep it running, which promptly broke the generators), and stored the nation's remaining foreign currency under his bed. Kim Jong-il (North Korea) claimed he invented the hamburger (calling it "double bread with meat") and shot 11 holes-in-one his first time playing golf. Donald Trump (United States) used late-night tweets to announce major policy shifts and fire his own cabinet members. He altered an official government hurricane map with a Sharpie to match a previous erroneous statement, and publicly mused during a press briefing about the injection of household disinfectants as a medical treatment. Git repositories inside git repositories (without using sub-modules) don't seem to work well. I need this because I have mono-repos for research and I want to use git in a sub-folder to iterate, then commit just the final version to the parent folder. Looks like I need to remove the child .git/ (e.g. rename to .git.bak/, which I've added to my /.config/git/ignore) for this to work. Gemini To run a script in the background (without logs) and detach / disown it, use nohup your-script >/dev/null 2>&1 & disown Running /insights on Claude Code helped me add these two instructions to my code skill: Test web pages with screenshots (for layout, overlaps, contrast) AND CDP (for interactions, navigation) before finalizing Prefer icon libraries over unicode/emoji icons. Sending an entire PDF/PPTX to Gemini costs 40% of sending PDF/PPTX + images. The quality is fine for small files, but for large files adding images reduces error rate from 5% to 0.5%. Pandoc Markdown to Word DOCX supports sidebar comments. You can use this Markdown: Here is [comment in sidebar]{.comment-start id="c1" author="Anand" date="2026-01-01T12:00:00Z"}commented text[]{.comment-end id="c1"} inline. Gemini. In fact, Pandoc supports lots of other things, like: Custom styles via block ::: {custom-style="Custom Style Name"} Track changes via [inserted text]{.insertion author="Name" date="2026-04-20T12:00:00Z"} and [deleted text]{.deletion author="Name"} Page breaks via \newpage (a LaTeX command that Pandoc supports in Markdown) CSS styles via Alt Text{width="5.5in" height="3in"} Offpunk is a CLI offline-first browser. Interesting idea, but installation is a problem. After sudo apt uninstall offpunk running offpunk failed with ImportError: lxml.html.clean module is now a separate project lxmlhtmlclean. After a git clone it reported HTML document detected. Please install python-bs4 and python-readability. These are easy to fix, but I wasn't inclined. Creating an authenticated MCP Server for ChatGPT is complex. It requires OpenID Connect (for which library support is weak and requires a provider like Auth0), dynamic client registration (which is hard to implement though Auth0 supports it), and after half a day of experiments, I still couldn't connect. An easier option is to run temporary tunnels with cloudflared or ngrok or localtunnel.

Things I Learned - 26 Apr 2026


I share weekly learnings: spotting AI writing via paragraph length, using `.git/info/exclude` for local-only Git ignores, and leveraging formal proof tools like Z3 and TLA+ to verify AI-generated code.

This week, I learned: Every Substack feed has an RSS feed at https://your.substack.com/feed. Substack help. I used this to scan my browsing history to identify Substacks I visit - and subscribed to Marcus on AI - an AI sceptic AI asked me to read about. Cloudflare let's agents create temporary accounts so that they can deploy and test. Enables trial and error - a powerful capability. "They're on mobile but this is substantiative enough to warrant length." I spotted this in Claude's thinking when prompting on mobile. So, if I ask Claude something on mobile, it will give me shorter responses by default. Clever design - but something to keep in mind. If I want some heavy thinking done by Claude, better to do it on desktop than try to give it conflicting instructions. Giant Permissive Image Corpus (GPIC) has 100 million Qwen tagged public images. Even as a simple searchable image catalog this has value. Jeff Clark - Import AI Ethan Mollick had an agent test his book summary against multiple LLMs as readers to find out how they would recommend it - and optimized. This is a great practical use of agents as consumers, and material for my When Data is for Agents, Not Humans workshop. kage is an easy CLI to clone websites and read offline. For example, kage clone https://simonwillison.net/2026/Jun/ -o /tmp/site --scope-prefix /2026/Jun/ --max-depth 1 clones all Jun 2026 articles from Simon Willison's blog. Then kage serve /tmp/site serves it locally. While it's easy, the only time I need this is on a flight, and in that case, a local RSS feed app works better. I'm using newsboat for that. To me, the clearest sign of AI writing from the Wikipedia:AI or not quiz was consistent paragraph lengths. I got the first 3/3 wrong, but once I used this heuristic, I got 6/7 right. Updated my LLM Smells. The files .git/info/exclude and /.config/git/ignore are also ignored by git, like .gitignore, but useful if you don't want to commit them into the .gitignore file. For example, .DSStore makes sense only for Mac machines, not each repo. .vscode/ makes sense only for VS Code users. Nelson Figueroa Justin Poehnelt, author of the brilliant Google Workspace CLI gws, was fired for it. There have been no updates for 3 months, but none may be required - it feels perfect. X Lore is a centralized version control system for large binaries. If you have large binaries (e.g. images, videos, ...) that multiple people edit, it's better than Git LFS or Perforce. ChatGPT Deno Desktop lets you use JS to build desktop apps. I tried it. It's easy to install, compact to code, leverages familar web technology, and compiles to multi-platform binary. The binaries are a bit larger than I'd like, though - 80MB for a Hello World on Linux/Windows and 70MB on Mac. Codex reported that You have 2 usage limit resets available. Run /usage to use one. This thread has context. After resetting, the next reset might be 7 days after the reset, though (source). After having a child, fathers are affected biologically, too. Testosterone drops, cortisol & prolactin & estrogen rise, the brain rewires for empathy and threat detection - and of course, there's less sleep. These sometimes lead to "Paternal Postpartum Depression" - something I didn't even know was a thing. The havoc kids wreak upon us! 🙂 Gemini With AI writing more code, formal code proofs are becoming more accessible. You just need to ask a coding agent to prove / disprove a function. You can use: Z3 to find/prove whether a counterexample exists. Best default. Dafny to prove that code obeys a spec. Best for real algorithmic code. Alloy to find loopholes in relational models, schemas, permissions, and workflows. Best for data. TLA+ to check whether stateful, concurrent, or agentic systems can evolve into a bad state. Best for systems / workflows. .. and there's a long tail of these. Python is named after Monty Python#Naming), not the snake. I knew this, but forgot! Python now has multiple cross-platform app paths: PyInstaller and Nuitka for executables, Kivy, Flet, and BeeWare/Briefcase for GUI/mobile/desktop apps, and PyScript/Pyodide for browser/WASM apps - a route that became more serious because Pyodide-compatible WebAssembly wheels can now be published directly to PyPI. On the one hand, AI is writing code, so there's no point learning Python. On the other hand, AI is writing code mostly in Python - so THAT's what you need to learn more. I think we should teach Python using AI, that is, teach how to write and debug Python code using AI. That'll end up teaching skills people will really need. Computational thinking = Decomposition + Abstraction + Algorithm design + Pattern recognition. In AI, that translates to = Framing + Context engineering + Orchestration (harness engineering?) + Verification design. Maybe I'd add Assetization / Systems.

Things I Learned - 28 Jun 2026


I looked into "LLM Psychologist" job trends, OpenAI acquiring Astral, and the hedonic treadmill. I found new inference hardware hitting 30,000 tok/s and learned EARS syntax for better requirements engineering while experimenting with agentic git patterns.

This week, I learned: The Kids Should See This - great collection of videos for curious people. Thej A jury fined Meta and YouTube $4.2m and $1.8m for building addictive features in their products. That's a first. NY Times "I think AI-type tools will actually revolutionize the experimental side of math, where you don’t care so much about individual problems and the process of solving them, but you want to gather large-scale data about what things work and what things don’t." Terence Tao The hedonic treadmill (which roughly quantifies a Buddhist principle) says that we revert to a happiness set point (which varies by individual). Worse, those who experience a high kick (e.g. a lottery) don't get enough kick from normal wins (contrast effect) -- Interactive explainer. The happiness neutral As of today, a LinkedIn search for "llm psychologist" lists 9 people. I'm not alone! Anand S, LLM Psychologist, Singapore, Singapore Anshul Saxena, PhD, AI Advisor & Trainer | Technology Strategist | LLM Psychologist | Currently teaching humans, machines & business to work smarter through Generative AI and Quantum Computing | 15+ Years Experience, Pune, Maharashtra, India Charitarth (Chad) Sindhu, LLM Psychologist / Fractional Business & AI Workflow Consultant/ Digital Nomad, Tokyo, Japan Lancelot Salavert, LLM Psychologist, Barcelona, Catalonia, Spain Lior Dor(Durahly), Team Lead | Bug Banisher | Ex 8200, Tel Aviv District, Israel. Past: R&D Team Lead and LLM Psychologist at Superwise | A Blattner Tech Company maxime bodereau, Lead Creative Art Director | UX Forensics | Ai LLM Psychologist | Visual Alchemist | Codesmith | Brandologist | Full Stack Designer, Nantes, Pays de la Loire, France Mei Chen 🦋, LLM Psychologist | Lead Product Engineer | Delivering Agentic Experiences, Toronto, Ontario, Canada Shoshannah Tekofsky, LLM Psychologist at AI Digest, Zwolle, Overijssel, Netherlands LinkedIn Member, LLM, psychologist, mediator, Prague, Czechia OpenAI acquired Astral!. This will likely slow down the new wonderful tools accelerating the Python ecosystem. Like with PromptFoo and OpenClaw, this seems to be about talent. The "acqui-hire" mode seems a clear niche career path now, and an alternative to getting hired (you get a much higher salary) or getting acquired (you take on much higher risk). quickjs-emscripten lets you run isolated JS code securely in the browser, CloudFlare workers, NodeJS, and Deno. It compiles to WASM. @sebastianwessel/quickjs is a higher-level TS wrapper. Simon Willison Manyana is a CRDT based version control system. It sounds like a good idea but I'm sceptical because merge conflicts are a "what should I do" problem more than "how". With agents doing more merge conflict management, I am not sure this will offer a concrete benefit - but probably no harm either. LLMs are able post-train LLMs on new topics. They're improving fast. Jack Clark Vibe Coding Fixer and AI Slop Cleaner are real job descriptions - which are morphing into enterprise offerings. But I still seem to be the only official LLM Psychologist Notes from AI Services - Wrong Mental Models, Right Moment: AI services has 3 markets. Automatable work: vanishes in 2 years. Human-in-the-loop work: sustains. Judgement-driven: grows in importance. YC: don’t sell access to a tool for $50 a month, use the AI yourself and sell the finished work for $5,000. Sell output. Price on outcome. Sell to business, not IT. Sell accountability: proven success, with your guarantee. Sell authenticity: a brand story representing uniqueness, character, ... or whatever... something people respect. Data transfer between GPU and memory is a bottleck and three approaches are emerging. # Taalas is etching LLMs into the chip. Llama 8b runs at 17,000 tok/s (H200 is at 230 tok/s). d-Matrix is moving compute into SRAM memory chips. 30,000 tok/s for Llama 70b. Cerebras and MatX are similar: memory-oriented. FuriosaAI minimizes data movement. Groq and Sambanova are similar. But in the long run, commodity technology usually beats integrated stacks. GPT 5.4 Nano ($0.2/MTok) and Mini ($0.75/MTok) are good options for bulk OCR, transcription, etc. as cost and quality comparable alternatives to Gemini Flash Lite and Gemini Flash. They can describe 75K photos for $50. Both models are better than GPT-5 Mini on most benchmarks. Cool AI coding agent git prompt fragments: Use git bisect to find when this bug was introduced: ... Find and recover my code that does ... Sort out this git mess for me. Rewrite history removing ... Split the last commit into multiple commits grouped logically. Start a new repo at ... and build just this module ... based on ... with a similar commit history copying the author and commit dates. Campaigns Are Knowledge Workers and the Tools Just Caught Up. A powerful framing. I saw this in action a few days ago when a friend was able to automate an outbound campaign with Claude Code. EARS (Easy Approach to Requirements Syntax)&oq=EARS+(Easy+Approach+to+Requirements+Syntax)>) is a simple structure for requirements. For example, "Users should be able to drag tasks between columns. The app needs to work offline too. Handle errors gracefully." becomes the following - which AI can convert to and is easier to spot errors in. State machines and decision tables are useful alternatives, too. REQ-001 (Event): When the user drags a task card to a different column, the system shall update the task status to match the destination column. REQ-002 (State): While the application is offline, the system shall store task updates in local storage. REQ-003 (Event): When the application reconnects, the system shall synchronize locally stored updates with the server. REQ-004 (Unwanted): If synchronization conflicts occur, then the system shall display a resolution dialog to the user. As of now, avoid using Claude.ai to create (large) visualizations. It runs forever and exhausts credits without generating anything. Claude Code works much better for this.

Things I Learned - 29 Mar 2026


I signed files with OIDC using cosign, queried Wikipedia as Parquet with DuckDB, and explored the PRAGMA model's tabular data tokenization. I also set up local MCP in ChatGPT Developer Mode for unmetered AI coding.

This week, I learned: D-ID is an avatar generator platform like HeyGen. Creatify and Synthesia are a couple of others I heard of. This space seems to be growing. cosign is a CLI that lets you sign and verify any piece of text with a Google, GitHub or Microsoft account. cosign sign-blob FILE --bundle sign.json opens a login window and creates a sign.json signature. Anyone who has FILE and sign.json and the email ID can verify via a Google account with cosign verify-blob FILE --bundle sign.json --certificate-identity $EMAIL --certificate-oidc-issuer https://accounts.google.com. arxiv2md.org converts arXiv papers to Markdown. Source. markxiv.org claims the same - by just changing the URL - but it ended up reporting an error when I tried this link: . From Akhilesh Tilotia: So we have someone in our team with initials AS. She made a document which was named vAS. Then I made edits and named it vAT. These docs were in a CoWork folder. I asked Claude to clean up my doc. It created another version for me to review. In its wisdom, it named the file vAU 🙂 Maybe what a forward-deployed engineer does is enginer AI-native workflows. (This sounded profound when I wrote it down. Not sure if it'll sound as profound tomorrow.) The idea is that the FDE will say, screw existing processes; let me fire up my AI agent and get stuff done; THEN we'll figure out what works, how to optimize it, etc. The PRAGMA: Revolut Foundation Model has some good tokenization ideas for tabular data. Create your own token space with key–value–time tokenization - to retain field information. Bucketize numbers by percentile, preserving magnitude/ordering that subword tokenization destroys. Encode time both as log-seconds and as cyclical calendar features. Codex uses the Alt + Up Arrow key to edit queued commands, but on the VS Code terminal, this key binding is not sent to the terminal. Enable the terminal.integrated.sendKeybindingsToShell setting to send it to the terminal, hence Codex. Based on this catalog on "universal foods", here's what I 🟢 like, am 🟡 neutral, 🔴 dislike, 🟣 must try, and will ⚫ skip. Universal favorites: 🟢 pizza, 🟢 fried potatoes/chicken, 🟡 dumplings, 🟢 ice cream. Universal comfort foods: 🟢 khichdi, 🟡 congee, 🟡 dal-rice, 🟡 risotto, 🟡 ramen, 🟢 pho, ⚫ chicken noodle soup, 🔴 rice porridge, 🟡 mac-and-cheese, 🔴 mashed potato, 🟣 polenta, 🟢 oatmeal, 🟣 Japanese curry rice. Acquired tastes that convert most: 🟡 coffee, 🟢 tea, 🟡 dark chocolate, 🟢 mild fermented dairy, 🟢 pickles, 🟢 olives, 🟣 kimchi, 🟣 miso, 🟢 mild chili dishes. Acquired tastes that have cult devotion: 🟣 durian, 🟣 natto, 🟣 stinky tofu, ⚫ fermented fish, ⚫ hákarl, 🟢 very funky blue cheese, ⚫ offal. OceanoPDF seems like a good place to download ePubs of books. The entire Wikipedia is available as a Parquet file. You can query it like duckdb -c "FROM 'hf://datasets/wikimedia/structured-wikipedia/enwiki/data/.parquet' LIMIT 5". The English version has 35 GB, 7.6 million articles, and you're better off downloading it rather than running analyses remotely. When you receive a Calendly link of the form https://cal.com/USER/EVENT you can fetch the available slots via curl -H 'cal-api-version: 2024-09-04' 'https://api.cal.com/v2/slots?eventTypeSlug=EVENT&username=USER&start=2026-05-25&end=2026-06-01&timeZone=Asia/Singapore&format=range'. Useful to automate good meeting-slot selection. "Reference saved memories" in ChatGPT is different from "Reference chat history" as per OpenAI. In Developer Mode, memory is turned off, but not chat history. I confirmed that I can access past conversations in Developer Mode. It might be a privacy concern for others, but for me, this is singularly useful, because I can use ChatGPT with Local MCP effectively getting a non-metered AI coding agent. Seems GPT-5.2 reaches expert level in peer review: 45 scientists took 469 hours evaluating human & AI reviews on 82 papers. "Surprisingly, current AI reviewers are competitive even with the top-rated reviewers in Nature’s official peer review..." though not without weaknesses, so use AI + humans. On the limits and opportunities of AI reviewers: Reviewing the reviews of Nature-family papers with 45 expert scientists via Ethan Mollick

Things I Learned - 31 May 2026


Recurring meetings should start with an expiry date because time-bounding them forces explicit review instead of letting stale rituals persist forever.

Whenever "let's set up a recurring meeting" comes up (from me or others), I add: "We'll set it up 4 sessions and then finalize the cadence." Why? Most recurring meetings are about: I want to do something Not sure what But I really want it, like long-term And I my future self might not follow through So my present self is going to force my future self with a long-term commitment But during the recurring meetings, my future self is usually asking: Why am I here? How do I get out of this? The 6:30 pm Calvin asks the 8:30 pm Calvin about his homework Finding exit points is hard, so I'm training my present self to give my future self a way out. Let's set up 4 sessions. Then we'll finalize the cadence.

Time bound recurring meetings


A modern AI course should drop static content, foreground AI failure modes, and teach orchestration, debugging, and evaluation instead.

My Tools in Data Science course is available publicly, with a few changes from last year. First, I removed all the content! Last year, Claude generated teaching material using my prompts. But what's the point? I might as well give students the prompts directly. They can tweak it to their needs. This time, TDS shares the questions needed to learn a topic. Any AI will give you good answers. Second, it focuses on what AI does NOT do well. Coding syntax? Who cares. Basic analysis? ChatGPT can do that. In fact, each question now has an "Ask AI" button that dumps the question into your favorite AI tool. Just paste the answer and move on. But, these questions (hopefully) teach you where AI fails. Putting tools together, prompting well, debugging and evaluating the output, etc. These matter more. Third, it's easier to audit. Anyone can take the course, even outside IITM. You can join the public Google Group for announcements. All questions are discussed publicly on GitHub. So, check the new version out, learn, and please share feedback!

Tools in Data Science - Jan 2026


I use static HTML files with meta refresh tags and GoatCounter to track redirects minimally. Hosting these on CloudFlare R2 provides a fast, free alternative to shortener services like Bitly without the risk of vendor lock-in.

Everyone needs a tracking URL shortener. Why tracking? I want to know if they opened my email and clicked the link. Why shortener? I want them to know what the link is about. For example, is so much more meaningful than I've used redirection services in the past - like t.co, bit.ly, goo.gl, ow.ly, and others. They tend to vanish, start charging, serve ads, etc. Here's my solution: use static HTML for redirection. For example, redirects to . Here's the code: The first line sets up tracking with GoatCounter, which is my current favorite analytics provider. It might vanish, but I can export the data if required and move to another. While it lasts, I can check how often the link was used. I can create personalized links. For example, tags user1. I can create unique links for each recipient. The second redirects to the target page immediately. I have deployed the files on a CloudFlare R2 bucket with a custom domain, which is practically free and fast. But I can switch at any point to any other hosting provider (e.g. GitHub Pages, etc.) by just copying the files and changing the DNS settings. Advantages: No hosting, no fees, no maintainance, no vendor lock-in.

Tracking redirects minimally


Meeting transcripts can be repurposed by AI into sharper interview answers, making rewrite-based review faster than manual review alone.

Priyanka was ghost-writing an interview request from PC Quest for Ankor. Two questions were a bit technical: 1. Straive combines data engineering, analytics, AI, and content services. At a technical level, how are enterprises stitching these capabilities together architecturally and operationally when addressing complex business problems at scale? 2. GenAI systems tend to behave unpredictably when exposed to real workloads. What engineering patterns, monitoring approaches, or runtime safeguards are becoming essential to maintain reliability, performance, and cost control in production settings? ... and she asked if I could review. These days, "rewrite" is easier than "review". STEP 1: Copy all my meeting transcripts with Ankor. Below is my process, but copy-paste or a coding agent works even better. 1a: Download transcripts with rclone 1b: Convert Word to Markdown with pandoc 1c: Combine using uvx files-to-prompt and copy STEP 2: Pass Gemini (which accepts 1MB!) this prompt: The answer was a bit long and not very insightful. So I added 2 prompts: Then: The final result was quite a good one, reflecting some of our recent conversations: Most enterprises are making a critical mistake with AI right now: they are treating foundational models like marriages, when they should be treating them like batteries. If you hardwire your data and content pipelines directly to today’s best LLM, you are accumulating massive 'AI Debt.' When a better model drops next year, you’re stuck. The true enterprise differentiator isn't the model—it’s building an agnostic orchestration layer. This middleware lets you swap out backend models instantly, without breaking your business. Once you decouple the model, you must stop trying to make legacy, linear assembly lines run faster. Instead, we use AI to 'squish' the assembly line entirely. AI co-pilots extract metadata and trigger multiple agents to execute tasks in parallel, not sequentially. This fundamentally shifts the workforce. We are no longer putting humans in the loop to do the heavy lifting; we are elevating them on the loop as auditors. The AI executes the work and provides the citation trail; the human manages the risk. That is how you bypass theoretical six-month roadmaps and move from ideation to a secure, live enterprise deployment in just eight weeks. STEP 3: For the next question: It again needed some tweaking: ... and the answer, again, was quite close to some of our discussons: "Most leaders try to fix GenAI unpredictability by endlessly tweaking prompts. That’s like trying to tame the weather instead of just building a stronger roof. At Straive, we assume models will hallucinate. The true enterprise differentiator is the engineering harness built around them. We secure workloads through Verifiable Autonomy. Instead of testing in live production, we run agents through simulated environments governed by strict policy-as-code. If an agent can’t deterministically pass the simulation, it doesn’t touch real data. To scale without exploding headcount, we use AI to govern AI. We deploy a 'Maker/Checker' architecture: a creative model generates the work, while a cheaper, constrained model instantly audits it for compliance and accurate citations. Humans only handle the exceptions. Finally, we stop treating every task like a real-time chatbot. By shifting latency-insensitive workloads to asynchronous batch processing, we bypass rate limits and slash compute costs by half. Reliability isn't a model feature; it's an architectural choice." The trick to re-purposing content quickly is: 1. Keep data / docs handy 2. Learn to quickly search & copy-paste 3. Learn to quickly judge and give feedback to AI

Transcript AI-ded interviews


Though travel feels passive, constant physical micro-adjustments, sensory load, noise, and poor circulation make it tiring, and simple habits and supportive gear help reduce the drain.

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: Noise-cancelling headphones (reduces the rumbling / traffing sounds) Compression socks (reduce blood pooling in the feet) Eye masks Good back support ... and the usual good habits: Drink water Exercise Fasting / eating light Breathe slowly

Travel is exhausting


I used Claude to identify substantive, unresolved debates across disciplines like physics, finance, and biology. These prompts helped me map the frontiers of knowledge by uncovering deep disagreements that go beyond mere semantics.

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. Mathematics Are mathematical objects (numbers, sets, complex numbers) real, independent of human minds? Or useful fiction? Do we discover or invent them? Is there one true set theory or a multiverse of equally valid set theories, e.g. where the continuum hypothesis is true vs false? Are theorems true or false, even if we can't prove them? Biology Does natural selection operate at a gene, individual, or group level? Does evolution proceed through gene mutation + selection, or do we need to add epigenetics, niche construction, and developmental bias? Which came first - RNA or mitochondria? Physics What actually happens when a quantum system is measured? When a black hole evaporates, is the information about what fell in preserved (and how) in the Hawking radiation, or destroyed? Why does the universe have a strong entropy increase toward the future but not the past? Computer science Is P really equal to NP? Can fault-tolerant quantum computers actually be built? Can we really achieve AGI with computation? Finance Do asset prices track underlying value or market narrative / noise? Why is the historical US equity premium (6%) so high that it implies a 30–40x risk aversion? Is financial instability inevitable in capitalism, or is it due to external shocks? Psychology Are emotions hard-wired (hence discovered) or constructed from more primitive ingredients? Does trauma hide the memory that we recover later, or are these recovered pieces fabricated? In psychotherapy, do specific techniques work? Or is it just the relationship? History Were the atomic bombings of Japan militarily necessary? Was the Holocaust planned from the start, or did it emerge from bureaucratic chaos? Did the Industrial Revolution raise or lower living standards for ordinary workers? Geography Does physical geography cause big changes to human outcomes in the long run? Are places about a geographic location or about the flow of capital, people, and power that flow through? Are local, regional, national, ... really nested ways of organizing society, or an ineffective way of thinking about geography?

Unresolved questions across disciplines


Workflow automation can turn scattered organizational signals into useful recurring newsletters that keep distributed teams aware of internal innovation.

This week, Namit and I met a Straive team that operates from a client office. One team member asked: I believe that we are doing wonders out here, but we are closed from what is happening in the rest our organization. I want team members to interact with others to see what interesting things they have delivered and where we can implement that solution. Could we have sessions, maybe a monthly newsletter, showing what innovations we're working on? This would really keep us engaged with the tech that is going outside of the work that we do. A good point. This reminded me of an experiment last month. Google Workspace Studio lets you create automations. For example, here's a flow I set up to create a weekly newsletter about client news: [](https://studio.workspace.google.com/workflow/ydef223dcc0e1c583207620711f3fc01a) Step 1: On a schedule. Every Monday at 8am... Step 2: Ask Gemini. Scan my Google Workspace for the latest client related news and organize it as an email newsletter. Also find the latest news about these clients from the web and put it together seamlessly. Write it in a nice Malcolm Gladwell style narrative. Step 3: Ask Gemini. Just write today's date in YYYY-MM-DD format. Nothing else Step 4: Draft an email. To: me. Subject: Weekly news [Step 3: Content created by Gemini] Message: [Step 2: Content created by Gemini] Step 5: Create a doc. New doc name: Weekly news [Step 3: Content created by Gemini] Content to add: [Step 2: Content created by Gemini] Location for new doc: Weekly news Now, I get to see a nicely formatted newsletter every Monday morning about new client activity, added to a Weekly news Google Drive folder accessible to Straive. For example, I learnt that: One client blocked coding agents (Codex, Claude Code, etc) which has slowed down our team. Another client needs to modify their API Gateway + Lambda infrastructure to handle long-running agents without exceeding API timeouts Yet others requested omni-channel convergence optimization, price sensitivity modeling, payments infrastructure setup, and much more. I always wished we had an "internal reporter" at Gramener, going around, interviewing teams, and writing interesting stories about what's happening. I did not expect I'd be able to hire Malcolm Gladwell (or anyone I want) as our internal reporter!

Using AI for work news


Browser history is an overlooked personal memory system that can reconstruct days, interests, and unfinished thoughts when other logs miss them.

I have a bad memory. (I need to write about that. I k eep forgetting to.) It's worsening. Yesterday, I misplaced my debit card for the first time. Or maybe the second...? Which reminds me, I just forgot a call I have now! (Panic.) (15 min later.) So, anyway, therefore, I log stuff meticulously. Like what I did each day, what I ate, what I weigh, what pained me, etc. But the best logging is automated. My phone logs where I am. My bank logs what I spend. My calendar logs who I meet. The main gap now is when I'm at home, not spending money or meeting people. Just... browsing. And browser history is a great way to "remember" this. I figure out what I did this weekend, I ran: ... and then this query: I pasted the CSV output into Gemini along with this prompt: Here is my browsing history this Sunday. Summarize what I did, grouping into logical groups like: 10:00 - 12:30: What I did in 1-2 sentences 12:30 - 13:00: Next activity ... Ask me questions for whatever's unclear. Here's what it said {with my comments}: 10:00 – 12:00: VizChitra Planning & AI Research. You started with administrative tasks (AWS, Google AI Studio keys) before diving into VizChitra 2026 proposals. You researched AI attribution bias and data storytelling, then used Claude and Gemini to brainstorm ideas, eventually drafting your blog post: "Submitting an AI-Ded VizChitra Proposal." 12:00 – 14:15: Blogging & Philosophy You continued refining your blog post and engaged in deep discussions with Claude about "developing taste and judgment in the AI age." You also briefly checked LinkedIn {"anyone liked my post yet?"} and explored some Dutch legal terms ("Stijlrichtlijn") {work-related}. 14:15 – 16:30: Academic Research & Entertainment. You read heavy research papers on the economic and aesthetic impacts of AI (Microsoft's Future of Work, Peyman Shahidi) {to develop taste}. You then prototyped an "Ask AI" tool using Codex before switching gears {here's the Ask AI tool} to watch the Telugu comedy movie Khatarnak on YouTube {Telugu films are best for vibe coding}. 16:30 – 17:45: Local Development & Coding You spent this block coding on localhost (port 8000), testing your "Ask AI" tool and a "JSON to CSV" converter. You also managed GitHub pull requests for your tools repository. 17:45 – 19:00: Deep Dive into Learning Theory. You went down a rabbit hole on pedagogy and mastery, reading about the "psychology of interest," "tacit knowledge" (Commoncog), "cognitive apprenticeship," and Austin Kleon’s "Scenius" {to develop taste}. You also looked up directions to the Isha Yoga Center in Singapore {my wife asked}. 19:00 – 20:30: Course Admin & Networking. You switched context to your Tools in Data Science (TDS) course, troubleshooting a "GA1 Question 9" submission issue for students. You checked LinkedIn profiles ({names redacted}) and watched a few more comedy clips (Baladoor, Julai) to relax. 20:30 – 22:00: TDS Live Session. You prepared for and joined a Google Meet (likely the TDS Live Session), reviewing papers on "automation bias" beforehand. Afterward, you managed the meeting recordings and looked into configuring custom commit messages for GitHub Copilot. 22:00 – 23:00: Cloudflare & Wrap-up. You ended the day deploying updates on Cloudflare Workers, handling more student emails regarding the GA1 deadline, and doing some light image editing on Squoosh. This is a fantastic way to jog my memory about what I did - and is obviously spot on. The questions it asked were also insightful: 1. The search for "Stijlrichtlijn Verdict to Video" (Dutch guidelines) seemed distinct from your other work—was this for a specific project? ANS A specific work project. 2. Did you end up watching the full Khatarnak movie, or was it background noise while you prototyped? ANS Background noise while vibe0coding. 3. The very last search was for "cnc machine"—are you looking to buy one? ANS Prof Shantanu Chaudhury, a few days ago, likened vibe coding practices to "something like you're creating a CNC machine for coding. You're just giving the specification, programming it, and it's generating the code." Which felt very apt, but I haven't used a CNC machine (or have I?) and asked Google what it was. So there it is. Export your browser history and as an LLM to summarize your day. Great way to jog your memory - and waste an hour. 11 Mar 2026. This continues to be a useful memory hack. In the last 4 weeks, I've used this at least 4 times to find out what I was doing the previous day or previous few days.

Using browser history as memory


Browser tabs can work as a lightweight slide deck, especially when combined with simple title and section pages to restore presentation flow.

My last two presentations used browser tabs as slides. For my talk last week titled Your Chotu Is Smarter Than You Think, I planned to show a series of examples. I loaded them all in a browser window as tabs like this: 1. How I use AI to navigate toilets 2. How I use AI for food recommendation 3. How I use AI for book suggestions 4. What else I can use AI for 5. ... Once loaded, I can press Ctrl+PgDn to move to the next - just like I'd press the right arrow key in a slide deck. I can also use the mouse to click on the tab if I want to jump around. But there's one advantage I missed from slides. I can add title slides, section dividers, etc. Since web pages are so versatile, I vibe-coded a slide tool by roughly saying: Give me a single page "slide" tool Let me edit the title, subtitle, fonts, colors, backgrounds, etc. via a (barely visible) button on the top right Store this in the URL so I can bookmark and share it (Note: I had 5 min on 4% battery and my laptop couldn't connect to the Internet. This was voice vibe-coded on the web and the PR accepted without review.) That let me create a far richer presentation. 1. 🟢 SLIDE I use AI like an intern 2. Like a plumber, to navigate toilets 3. Like a waiter, for food recommendation 4. Like a secretary, for book suggestions 5. 🟢 SLIDE Use Paid AI. ANY Paid AI is the best ROI you get 6. You can hire AI for many services 7. 🟢 SLIDE Under-using AI is more dangerous than over-using 8. ... and so on. Here's what the full presentation looked like The "slides" allow me to add structure and remind the audience and me about the key points. For all the bad press PowerPoint receives, I don't think presentations are a bad format. But today, there are so many more ways of presenting that using slideshow software seems a bit outdated. UPDATE 18 Jun 2026: My talks in the last few months use this technique almost without exception.

Using browser tabs as slides


By running programs through an AI coding agent like Codex rather than executing them directly, you create an agentic operating system that automatically installs dependencies, handles configuration changes, and debugs brittle scripts.

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. It's also useful for brittle scripts. When my WhatsApp scraper broke, I told Codex: The scraper was working fine but today, it fails. Find out what changed in the WhatsApp Web UI and fix it. Codex researched and replied: The current scraper still finds the chat area and message rows, but its row-identity parser is now invalid. On March 28 the code assumed each message node had a packed data-id like: Today, in the live WhatsApp tab, the visible message nodes still exist under #main [role="row"], but data-id now looks like a bare message ID: Because parseDataId() only accepts the old packed format, it rejects every row, so whatsappMessages() returns [], and the bookmarklet shows Copy 0 messages. WhatsApp appears to have split what used to be one overloaded field into separate concerns: data-id now behaves like messageId outgoing direction is likely encoded by DOM class (.message-out) chat/thread id is no longer bundled into the row data-id ... and went on to share a "Recommended hardening plan for future breakages". If coding agents can install, configure, write, run, test, and debug programs, "Agentic Operating Systems" are not nearby. We already have them.

Using Codex as my OS


Letting Codex analyze your past Codex sessions is an effective way to discover underused features and improve how you use the tool itself.

Instead of learning and applying new Codex features, I asked it to analyze my sessions and tell me what I'm under-using. It did a thorough job of listing all the new features and analyzing my gaps. Read the full story Here's the summary: I'm using new models immediately, but not the new features of Codex. For example: Parallel execution. Yesterday, I ran 103 tool calls without the new spawnagentsoncsv feature from last week, which would have saved a lot of time running in parallel. Permissions. Last week, I ran a script that asked me for permissions 7 times towards the end. Instead, I could have used /permissions to set early permissions. The best part is that it could just add a few instructions to my AGENTS.md: Now, the beauty is that the tool optimized itself. I don't even need to learn how to optimize it!

Using Codex to improve Codex


Game-playing agents can turn abstract concepts into interactive classroom experiences, making learning more exploratory, social, and memorable.

After an early morning beach walk with a classmate, I realized I hadn't taken my house keys. My daughter would be sleeping, so I wandered with my phone. This is when I get ideas - often a dangerous time for my students. [](https://tds-network-games.sanand.workers.dev/) In this case, the idea was a rambling conversation with Claude that roughly begins with: As part of my Tools in Data Science course, I plan to create a Cloudflare worker which allows students to play a game using an API. The aim is to help them learn how to build or use AI coding agents to interact with APIs to solve problems. The game needs to be: Playable yet challenging: Fun by itself, human-playable via a text interface, but hard to solve manually at scale. Easy with an AI coding agent. Maybe a maze or text adventure? Seed randomized: Generate a different problem for each student & week, so they can't reuse a solution. Verifiable: The solution and score must be publicly verifiable (JWT token?) without requiring shared secrets. Give me game ideas and explain: 1. What'll the game like on the API and the UI? 2. Why these ideas? What's interesting about the game? 3. What'll students learn by playing the game directly? 4. What'll students learn using AI coding agents on the API? It generated four ideas. I picked three. Labyrinth It's a maze where each may have a data row. Students wander, collect required fragments, reach the exit room, and answer a statistical question from collected data - within a fixed number of moves. Manual play teaches spatial reasoning, systematic exploration, and the cost of backtracking. AI agent play teaches graph traversal, stateful API interaction, and basic data aggregation. Play Labyrinth Video Detective You're investigating a financial network of accounts with transaction links looking for a compromised account which behaves strangely on multiple attributes. Find it, and trace the shortest path to an "anchor" account using as few node queries as possible. Playing manually teaches graph intuition, anomaly detection by feel, and the frustration of systematic searches. AI agent play teaches graph traversal algorithms, outlier detection, path reconstruction, and the exploration-exploitation tradeoff - all real-world data science skills. Play Detective Video Signal AI has locked all your exits in a research facility. You need to restart the core systems by exploring rooms, combining objects, and solving the AI's puzzles. This game is AI-agent native: LLMs can parse the hints better than humans. Students using pure rule-based agent will struggle, teaching LLM-as-a-tool within a larger agent pipeline - an important real-world pattern. Playing manually teaches inventory management, dependency reasoning, close reading of ambiguous instructions. AI agent play teaches multi-step planning with dependencies, agents-in-the-loop, NLP, and state management. Play Signal Video Implementation Frankly, I just asked Codex to crunch it over five hours of a Vijay Antony movie binge. Seriously. I did NOT look at the code. I just wrote 12K worth of prompts (which I'll share later) while it processed 129 million tokens, generated 625K of tokens, and got the entire job done. The game is now part of the TDS Project 1 - apart from a bunch of other exercises. With this as inspiration, I hope to include many more gamifications into this course. This feels a bit like Maze Runner). My condolences to the students.

Using game-playing agents to teach


Connecting Gemini to Drive and Gmail lets it assemble slide-ready client and proposal context directly from your working corpus.

On Friday, our data & analytics client-facing teams connected Gemini to their Drive and Email: 1. Open Gemini 2. Make sure you see a "Pro" icon on the top right. (Some already had access. Some enabled it by clicking stuff.) 3. Go to Settings & help > Connected apps. Turn on "Google Workspace" and approve access. 4. Select "Pro" as the model (instead of "Fast" or "Thinking"). Then they ran this a prompt like this: Go through my Google Drive and find out what are the recent sales proposals we've picthed or new clients we've won. ⭐ Gemini searches all your files (even shared ones) and emails (even attachments) and shares a summary. They tried using it to gather material to respond to client requests (potentially attaching documents): Share all relevant material to respond to (details of topic) from my Google Drive / Email. ⭐ Gemini is as smart as a good business analyst at scanning files and gathering material. They went further, and prompted it to convert it to a slide deck: Convert this into a beautiful slide deck, McKinsey style. Make the slides content rich, i.e. clear and self-explanatory with enough detail to help the audience understand without a narrator. Use iconography, typography, stock images, etc. as appropriate. ⭐ Gemini can create reasonably good slide drafts that you can export to Google Slides and edit. In 30 minutes, several people had created beautiful slide decks with no support! So now they can: Create a first version, show it to analysts and say "Build something like this. But change ..." Create half a dozen drafts and pick what they like But what excites me more is when we: Create and share the deck overnight. Lightning-fast turnaround with no support! Create the deck during a client meeting. It's like we came prepared reading clients' thoughts! Create with the friendly client something they are happy to take to their bosses!

Using Gemini to create slides


I explore how to create lightweight, customizable favicons by embedding SVG and Unicode directly into HTML via data URLs. This method allows for tiny, easily editable icons using text, emojis, and CSS-like styling.

Browsers support SVG favicons as data: URLs. For example, this SVG: ... can be: 1. Compressed via svgomg 2. Converted to a data: URL via svgviewer 3. Inserted into HTML like this: The fun part is that you can use text inside the SVG, styled as you wish: DS ... or even use Unicode characters: 🌈 That can be converted into a HTML link like this: The variety is endless! ⚡ 🖱 ✦ ◈ ↻ ⭕ 🕒 👨👦 T L T l T L T L T L T L T L [ TL ] What makes this powerful is that: 1. You can create entire families of favicons using typography, Unicode characters, and colors. 2. The favicons are tiny (a few hundred bytes). 3. They're easy to edit and maintain (just edit the SVG text).

Using SVG favicons with Unicode


LLMs are especially useful for fact-checking textbooks because even occasional successes can uncover high-value errors at scale.

Using LLMs to find errors is fairly hallucination-proof. If they mess up, it's just wasted effort. If they don't, they've uncovered a major problem! Varun fact-checked Themes in Indian History, the official NCERT Class 12 textbook. Page-by-page, he asked Gemini to: 1. Extract each claim. E.g. "Clay was locally available to the Harappans" on page 12. 2. Search online for the claim. E.g. ASI site description and by Encyclopedia Britannica. 3. Fact-check each claim. E.g. "Clay was locally available to the Harappans" is confirmed by both sources. Here is his analysis and verifier code. Before hitting rate limits, he scanned 56 pages and fact checked 45 claims from a dozen pages (detailed report). Even among this small sample, there was an interesting error on Page 22: Only broken or useless objects would have been thrown away. In reality, migrants abandon useful objects and ritual deposits give us intact, useful objects. So, is this an error in the textbook worth correcting? Maybe. Worth exploring. BTW, the original analysis hallucinated sources: "Discard and Reuse in the Ancient Near East" and "Recycling in the Bronze Age" from Cambridge. But for fact-checking, this doesn't matter. The point is to flag questionable claims for further review. There are others: Did R.E.M. Wheeler take over as Director-General of the Archeological Survey of India in 1944 (according to the book) or in 1940 (according to other sources)? Worth checking the ASI records? [Ashoka's army was] 600,000 foot-soldiers, 30,000 cavalry and 9,000 elephants? The book says "some historians consider these accounts to be exaggerated" but the consensus is that these are almost certainly exaggerated. Worth rewording? If LLMs find half a dozen questionable claims from a dozen pages, fact-checking textbooks seems an effective use for LLMs. Especially when 25 million students read them every year. I have personally suffered from errors in history textbooks. I wrote my class 9 history exam in 1989-90. The Hindu published a series titled "This Day That Age" with news from 50 years ago. That morning's was that Germany invaded Poland with Russian aid. I proudly wrote this in my exam and lost a mark, since the history textbook clearly mentioned that Russia opposed Germany. (That I feel indignant 36 years later tells you how sorry my life is.)

Verifying Textbook Facts


I observed a passenger using a plastic safety kit wrapper to mount their phone behind an airplane tray table for eye-level viewing. It's a clever, low-tech travel hack discovered during a flight from Singapore to Chennai.

On the Indigo 1026 from Singapore to Chennai, I saw a passenger two seats in front of me watch videos in an interesting way. She had wrapped her phone in a plastic cover, wedged it behind the tray table so that it would appear at a comfortable viewing position, and watched an Asian movie (presumably with bluetooth headphones). At first, I wondered if she travels with a plastic wrapper for this purpose. Then I realized it was from the Indigo safety instructions kit. Now, that's either a very experienced flyer (who knows about the wrapper) or a very innovative person (likely both). I was curious, so I asked her: 1. Have you done this before? ANS: Yes, many times. 2. Did you know that Indigo carries these wrappers you could use? ANS: No but flights usually carry a Ziploc bag. 3. Is it easy to control through the wrapper? ANS: I don't touch it much while watching. (I was able to control it but fingerprint unlock didn't work) It's fascinating to discover such innovations in the wild!

Watching videos with a plastic cover


I explain why I filter my content for LinkedIn, omitting deep strategy, developer posts, and raw prompts. To bypass platform constraints, I’ve moved my complete, untrimmed blog to an email subscription via Google Groups.

I don’t post all my writing on LinkedIn. For example: Fewer strategy posts, e.g. “Where Enterprise AI is Headed”, “How My Innovation Team Works”, etc. aren't on LinkedIn. Fewer developer posts, e.g. my AGENTS.md, my SKILL.md files files, CLI tools, evals, etc. aren't on LinkedIn. I also shorten content because of LinkedIn constraints. For example: No links, e.g. the list of all my AI-in-education resources Short content, e.g. my full advice for teams using AI is much longer than the LinkedIn post. Trimmed prompts, e.g. how to convert meeting transcripts into a personalized org-consulting report Snipped chats, e.g. the full moves of GPT-5.5 playing chess I filter the LinkedIn posts, sharing what’s most useful for most people. But some people asked if I can share the full content over email. So, I’ve set up my full blog, by email (free, via Google Groups): https://groups.google.com/g/s-anand Preview before you subscribe. Switch to weekly digest if it’s too much.

What I don't post on LinkedIn


Join my workshop to learn how to restructure datasets for LLM agents rather than humans. We'll test hands-on techniques like prioritizing functions over tables, leveraging logs over docs, and using agents to rebuild raw data.

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. I'm not fully sure how much of this is right. That's the point. We'll work it out together, on your data. We'll do two things. 1. See how one dataset looks to a human vs an agent - then rebuild it for the agent. 2. Use agents to rebuild the data. They're good at it. For: data/AI engineers, BI folks, anyone publishing schemas, docs or APIs. Bring: a laptop, a paid ChatGPT/Claude account, and a dataset you have and like. It's both online and in-person. 30 seats. Register at https://hasgeek.com/fifthelephant/when-data-is-for-agents-workshop/ I'll share the recording.\ Attend only if you want to actually do stuff live.

When Data is for Agents Not Humans Workshop


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: 1. Progressive, just-in-time access beats feeding full context at once 2. For changing, text-heavy corpora, grep-style navigation can beat embeddings 3. CSV beats JSON on accuracy per token Hmm... let's test them one by one. Everyone, ask Codex (or Claude Code) to run a benchmark. What? All thirteen of your results said, "It makes no difference?" Huh... Claude, did my benchmarking prompt do a good job? What? I messed up my benchmarking prompt? It gave all the data at once instead of progressively? OK, give me the revised prompt. OK, everyone, try this prompt. What does it say? Aha! Yes, progressive disclosure costs 2.5x - 25x less for the same accuracy. Cool! Let's try another experiment. What? I totally messed up that prompt as well? Er... we're short of time? Fine, wrap up, then. Claude, convert what we learnt into a skill. OK, folks, my takeaways: 1. I don't know what formats agents prefer, but we can ask AI to research. 2. I don't know if the research is right, but we can ask AI to create a benchmark. 3. I don't know if the benchmark is right, but we can ask AI to audit it. 4. I don't know if I'll remember what's valid, so we can ask AI to create and reuse skills.

When Data is for Agents - Workshop Summary


If frontier-model quality keeps getting 10x cheaper every year, many assumptions about AI product design, access, and value creation will break quickly.

In Feb 2024, Claude 3 Opus was the best model, at $15/MTok.\ In Jul 2024, GPT 4o Mini reached that quality at 10% of the price.\ In Dec 2024, DeepSeek v3 reached that quality at 1% of the price. Video See the interactive version If the price continues to fall 10x every 11-12 months or so (and it has been), then in a year, a Claude 4.6 Opus like model will cost 1/10th of the $5/MTok today, and in 2 years, 1/100th of that. (We'll be using better models, of course.) But 2 years isn't far away. If Opus 4.6 were 100x cheaper, I could do 100x of what I could do with it today. What would we do with it? If we assume that they'll become 100x faster as well (and that's an important assumption), and the reliability will continue to improve, then: LLM LSPs. Language servers could be LLMs. Hover over a squiggly line to understand a bug it spotted, right click and fix. Move on. LLM pre-commit hooks. Write docs, write and run tests, refactor - automatically before you commit. Continous refactoring. LLMs auto-refactor the code, run tests, and commit better code. Auto-fix from logs. Log analysis -> Test case -> Fix -> Deployment can be automated. Pick best option. LLMs generate 30 diverse options for each task, test all, and pick the best. E.g. What's the best language / framework for this? What's the better visual design? What should I build? Live docs. LLMs auto-update docs every commit. Adversarial workflows. LLMs continously run adversarial test cases to break the code, and fix it. Build & discard, don't buy. Most tools are easier to create than purchase. They're also easier to throw away. To hell with code quality!

When LLM prices fall 10x every year


I used Pi to build a tiny landing page where the prompts were longer than the 471-byte HTML output. This workflow helped me overcome starting friction and solve CSS centering while focusing on review over manual coding.

I used pi to create a compact home page for media.s-anand.net using these prompts: Create index.html - a simple, elegant page that says that this page (media.s-anand.net) serves large media files for Anand - that's where they should look instead. ... followed by: Skip the part that says "Please visit ..." ... then: Shorten index.html to just 2-3 elegant rules of CSS. I want it MUCH smaller and simpler. ... and finally: Center vertically and horizontally. These prompts ended up being larger than the 471-byte index.html: Not that this matters, because: 1. I didn't know what I wanted and having an AI coding agent generate a first draft helped with starting trouble and ideation. 2. I don't know how to center on the screen and this did it for me. 3. I am practicing the skill that matters - reviewing - and not the skill AI is taking over - writing code.

When the prompt is longer than the code


Fast, Thinking, and Pro are best treated as task-specific Gemini modes rather than generic quality tiers, because each one fits a different kind of job.

I continue to be impressed by Gemini 3 and it's become my default agent. It writes in simpler language than ChatGPT (almost as eloquent as Claude), has much larger limits, and, of course, is unbeaten at generating images. The Gemini app has 3 modes: Fast, Thinking, and Pro. Here's when to use each: 1. Simple task, e.g., grammar check, translate, summarize, or basic question? Use Fast. Pro overthinks. 2. Multi-step logic, e.g., planning a trip with constraints, checking 15 emails, or identifying a subtle error in code? Use Thinking. Flash-based thinking beats Pro. 3. Large input, e.g. 300-page PDF, 2 hours of video, etc.? Use Pro. It uses the 1M+ token window well. 4. Complex problem, e.g. PhD-level science or a legal contract review, with high stakes? Use Pro. If you hit your Pro limit (which is pretty high!), just switch to Thinking, which is smart enough for most jobs anyway. Source: Gemini

When to use which Gemini mode


I was asked eight podcast questions about enterprise AI - why pilots stall, where governance bites, what India-led capability centers add, and how to measure ROI. Here are my answers, grounded in actual client meetings.

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: 1. 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. 2. 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. 3. 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. 4. 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. For the first time, I'm happy to publish an AI-written blog post, because it is: Unique to me. No one else has my transcripts, and I'm in an unusual position: enterprises keep calling me with AI problems. More than me. Step #1 takes hours of research. Step #2 takes an hour of patience. I would not do this without AI. Not unlike me. I would have written it slightly differently. Better in some ways, worse in others, but it's close to my style. Given how comfortable I am about this, I plan to be - not just an author, but also - a editor of AI generating from my content. This article uses verbatim transcripts where possible. I've anonymized clients and most colleagues. I've annotated the post with (Anand: ...) commenting with my understanding. 1. So what is Straive, and what do I do there? I'll let Namit explain it. He said this two weeks ago in a pitch to a European credit-insurance client: The focus for Straive is helping its clients operationalize AI. For that, we bring two apparently distinct capabilities together... data analytics and tech development... and large-scale operations. Where we come in is bringing these together and bridging the gap. That's the company. We have about 8,000 people in India - Bangalore, Hyderabad, Chennai, Gurgaon, Noida, Mumbai, Kolkata, Pune. (Anand: Globally it's probably 18K.) My job is innovation. In the same call, I described it as: I lead innovation at Straive. Most of my work involves playing around with Large Language Models, trying to see how they can accelerate our client work as well as deliver new kinds of solutions. That includes improving the software development life cycle. I introduce myself as an LLM psychologist when nobody's watching for corporate decorum. Half of my week is demos for clients. The other half is figuring out why those demos haven't reached production yet. (More on that in question 2.) 2. Why do so many enterprise AI pilots stall? Not for one reason. I keep a mental list of stall patterns. Three of them come up almost every week. Pattern 1: The pilot worked, but nobody is delivering it. In a sync with Namit a few months ago, I caught myself saying: At a global media client, I am a little worried that the engagement keeps growing and we haven't delivered anything yet... Right now, we've been given proposal after proposal after proposal... Nothing has gone to getting deployed so that someone other than our team can use it. Namit's reply was a single, useful sentence: "But they are not in the execution phase?" That was the gap. We had impressive demos. We had no delivery owner. Pattern 2: The data can't move. For a global premium-schools group, the on-site data lead told me: This is the data set that is at the most granular level. There are around 400,000 rows... and around 110 columns... They cannot export it... we cannot export this outside externally. The pilot didn't fail. The architecture failed. We had to redesign the entire engagement around the constraint: schema, profiling stats, sample rows, hypotheses, and queries flow out; raw data stays in. (Knowledge infrastructure as a workaround for missing data infrastructure. See question 4.) Pattern 3: Teams debate frameworks instead of evals. A private-markets investor wanted to lock the "agentic framework" by end of week. Their team was comparing LangGraph vs OpenAI Agent SDK vs Pydantic AI. I told them, more bluntly than I should have: The technical solution may not matter too much because this is moving so fast that anything we built will anyway be outdated in not more than a year... It almost doesn't matter which of these... the effort on the code is the least of our problems. Pilots stall there too - not because the framework is wrong, but because the question is wrong. Without evals and acceptance criteria, no framework choice will rescue the project. The thing that survives all three patterns: pilots prove that a model can produce a good answer once. Production proves the operating model. 3. What operational gaps stop AI from scaling? Telemetry. Objective clarity. Repeatable loops. In that order. Telemetry, not surveys. Our L&D lead asked me how to assess AI readiness across 19,000 employees without sounding like a particular Big Consulting firm threatening people's promotions. I suggested: I would go to the IT team and ask them for three things... using NetSkope, who has been accessing AI-related sites on how many unique days in the past 90 days... Regularity matters more than volume... LLM Foundry access. They have the logs for that. Third, Google Workspace tracks Gemini usage... These three give us a good company-wide proxy for AI usage. He paused, then said it was better than a survey. You cannot scale AI adoption without knowing who is adopting it. Self-reports won't tell you. Logs will. Objective clarity beats agent architecture. (Anand: KISS: Keep it simple & stupid.) A teaching assistant in my IIT Madras course built an elaborate agentic workflow tool - planner agents, executor agents, sub-agents reporting to leaders. After fifteen minutes, I said: You've been speaking for 15 minutes and I haven't understood what you want. I don't know if you understood what you want... You mentioned two objectives: learning traces and helping students learn. We should keep those as two different tools... For the learning traces, the minimal solution is a terminal command. It should authenticate them with their Google account and log all the inputs and the outputs, save it in a signed document that is tamper-proof, that we can replay. 200 lines of Python, not a multi-agent framework. (He took it well, I think.) The operational gap was: nobody had separated the two objectives, so every solution looked too complex. Repeatable loops beat heroics. (Anand: Iterate. Compound improvement.) An internal team complained they couldn't ship because the developers were on other work. I told them: You can try. Don't worry about what is not working. Just write it down. I tried this, this is working this way, this is not working in this way. The gap wasn't developer capacity. It was the absence of a "try, document, learn, repeat" loop that anyone could run. 4. Why does content and knowledge infrastructure matter as much as cloud? Because the model is generic. Your business meaning is not. (Anand: Each company has their own ways of working.) A delivery lead working at the global premium-schools client kept hitting the same wall. The bottleneck wasn't access. It was semantics: The real bottleneck is not access; it's shared semantics: 'Acceptance date,' 'account ID,' 'boarding type,' 'inquiry journey' - these can mean subtly different things across systems. That is knowledge infrastructure. Definitions. Update rules. What "acceptance date" means when a stage is updated vs appended. No model knows this until you write it down. At the European credit-insurance pitch, we made this explicit. A senior delivery architect on our side told the client: We create a Confluence setup, bring in everything that's not already there on Confluence and create a comprehensive Confluence setup... That becomes the input for our agentic implementations as well. That becomes the data room from where the agents draw the knowledge to perform the actions. The Confluence wasn't the deliverable. It was the substrate that made every later agent deliverable possible. On a CPG analytics product demo, the founder explained their "definition library": This is where we're configuring the DNA of the agents... We call the domain definitions. We also call it the definition library... It's not just a wrapper around ChatGPT. It's something that's very grounded in domain-specific definitions that avoids hallucinations, non-deterministic output. I keep coming back to this. Cloud is where the model runs. Knowledge infrastructure is what the model knows. Skip the second, and you have a very expensive autocomplete. 5. What do India-led capability centers add? They convert AI demos into reliable processes. That's not a slogan. It's the only thing that actually scales. (Anand: You need people to operate the AI machinery.) On the European credit-insurance engagement, the client's IT lead described his Bangalore team. Jishnu's response was telling: We also want to absolutely be open and also retain some of that knowledge, because as we transition, those will be critical, the knowledge that is inherent in your people and processes. The center isn't a labor pool. It's a knowledge sink. Without that retention, AI workflows lose context within months. Better example: a media-intelligence client picked us because our AI model scored higher than theirs and higher than humans. The numbers were: Their model: 40% accuracy Their human reviewers: 65% accuracy Our model: 70% accuracy But 30% of cases were still outliers. So we set up an operations team in India to handle those exceptions. AI plus humans, with the humans owning the exception path. We now have about 150 people doing similar work for a global short-video platform out of Hyderabad and Chennai. (Anand: This is a claim I heard in a pitch. I don't have evidence. So, if it's untrue, it's human hallunication, not AI.) Closer to my own work: we have a Hyderabad team that trains coding models. (Anand: Actually, we don't. Rukesh of Deccan.ai does. This is AI hallucination.) About 100 full-time reviewers and 200-300 contractors. The full-timers don't build models - they look at code and rate it, "I like this, this is not so good." They're managing reviewers, not writing code. That's a capability center evolving from delivery to AI ops. The thing that distinguishes India-led centers in 2026 isn't cost. It's the willingness to own the 30% that AI can't handle yet. 6. Where does governance actually bite? Three places, all real, all from the last quarter. Compute-to-data, not data-to-cloud. (Anand: Move code, not data.) Back to the global premium-schools client. The data could not leave. So the governance pattern became: If you could share back the output aggregated of those queries, that will be great... Get the magnitude and the P-value. Which you can dictate over a call if required. We export queries and import aggregated results. The schema travels; the rows don't. "No export" turned out to be a product requirement, not a blocker. Honest impossibility. A global media client wanted us to scrub PII from 3 million user-uploaded images. Their senior engineering leader insisted on zero leaks. I did the math out loud: For 3 million images... with... 99%, we're talking about 30,000 images with personally identifiable information potentially slipping through. He replied, flatly: "We have to have zero leaks. Not thousands of leaks." I said: "Then I think I can safely say we can't do this. This requires more technology than we have." (Anand: When I said this, our sales teams nearly had a heart attack. So did the client, I think.) Trustworthy AI sometimes means saying no. That was a governance decision, not a technical one. Local execution for sensitive data. At a clinical-data conference, I used our own finance controller (a famously cautious Chennaiite) as an example. He emailed his team: Team, please use this opportunity to install CodeX AI as per the recorded demo. This is very powerful, yesterday I tried it for two data requests and the result was fantabulous. The reason he was comfortable: the data is not going to the model. The code is coming from the model. Codex ran the code on his machine, on the financial records, which never left his laptop. (Anand: Well, kind-of. Some data does leave, like summaries, previews, etc.) Three governance patterns, three different problems. None of them is policy text. All of them are architecture decisions. 7. How should we measure real ROI? Cycle time. Quality. New revenue. Risk avoided. Adoption. Not headcount. Cycle time, hard number: on the European credit-insurance engagement, our sales lead told the client: We brought in an AI-based approach solution to accelerate that entire mapping exercise... reduced the execution time by about 80%. That's the easiest ROI to defend. It was an actual XSLT and data-mapping workstream, not a demo. Quality and effort, blended: in a workshop with our research analytics team, an analyst said a CIM (Confidential Information Memorandum) takes: Three to four man-day effort. A man-day is equivalent to eight hours. I did the demo live: In approximately five minutes, Claude will come up with a pretty solid presentation. In approximately 45 minutes... ChatGPT will come up with an outrageously detailed presentation... Those three to four days will come down by 50%. The half-day saving counts. The "five minutes vs three days" headline doesn't, because review still takes time. Honest ROI includes the verification effort. Revenue, not just cost: one of our innovation track leads told the team: This was the demo that we made and that resulted into these two projects, both Sports Coverage and Trends to Clip. That demo turned into part of a $1.15M week of deal movement. Demos that drive pipeline are an ROI line item too, even though no spreadsheet ever credits them. (Anand: This was reported in an internal sales call I was not a part of, but is true.) The fourth measurement is adoption. If nobody uses the thing, the ROI is zero regardless of theoretical capability. Track NetSkope logs, not certificate completions. (See question 3.) 8. Where is enterprise AI going? Three predictions, ranked by how confident I am. Most confident: analysts stop doing research; they start managing AI researchers. I told a research analytics workshop: Stop doing research. Your job has now transformed into somebody who has a team of 100 researchers under you... Your job is no longer managing a team; it is in fact managing a team of teams, perhaps. The implication is real. Hiring shifts toward verification, judgment, and exception-handling. The org chart compresses but the supervisory layer grows. Accountability becomes the scarce skill. Reasonably confident: agentic frameworks will commoditize within a year. Back to the private-markets investor sync. I told them not to obsess about LangGraph vs Pydantic AI vs OpenAI's SDK: Workflows are where you say, 'do it this way.' Agents are where you say, 'figure it out.'... A scalable approach is to give it an agentic loop, say, 'you figure out how to solve the problem.' The frameworks won't matter. The loops, tools, and evals will. Pick something boring and move on. Less confident, but worth saying: nobody jumps straight to autonomous. Even our most ambitious proposal - the European credit-insurance one - staged it explicitly: The Agentic AI component is something which is more prominent and starts in the Modernize phase. But it's not there in phase one and phase two. However, AI is still there... We are going to leverage AI or LLMs for very basic functionality in a non-intrusive manner. Assist first. Instrument. Capture knowledge. Then automate more deeply. The companies that try to skip to step four lose three quarters re-doing step one. What survived three passes through my transcripts I rebuilt this answer three times. Each time I tried to disprove my own pattern. Each time the same shape held: Enterprise AI does not fail because the model is weak. It fails when we mistake a demo for delivery. When data can't move. When teams debate frameworks before defining evals. When the team overbuilds because nobody asked what the actual objective was. When the business case never cleared cost and timeline. The pattern that survives across clients, sectors, geographies: successful enterprise AI is operational AI. It needs telemetry, knowledge infrastructure, governance, human accountability, and a delivery model. Not slogans, not frameworks. Logs. Schemas. Confluence pages. Codex on a controller's laptop. A 200-line terminal recorder instead of a 10,000-line agentic platform. That's what we sell at Straive. That's what I'm trying to scale. Ask me again in a year.

Where Enterprise AI is headed


Students using different LLMs perform differently enough that model choice appears to have a real impact on grades.

In my graded assignments students can pick an AI and "Ask AI" any question at the click of a button. It defaults to Google AI Mode, but other models are available. I know who uses which model and their scores in each assignment. I asked Codex to test the hypothesis whether using a specific model helps students perform better. The short answer? Yes. Model choice matters a lot. Across 333 students, here's how much more/less students score compared with ChatGPT: Perplexity: increases scores by 11% points (99.8% sure it has an impact). Claude: +9% (99.5% sure) Clipboard:, i.e. just copying to the clipboard and pasting in their choice of AI: +9% points (97.5% sure) Google: -1% (Not sure. Sorry, Sundar.) If you are still using default ChatGPT, you are leaving nearly a full letter grade on the table compared to Claude or Perplexity users. I also looked at exam timing. Do specific models perform better under last-minute pressure? Statistically, no. The model-by-timing interaction isn't significant (92% sure). A good model won't save a rushed submission any better than a bad one. But procrastination does hurt, regardless of which AI they frantically prompt. In the first graded assignment (GA1), there was a negative correlation (-37%, 100% sure) between submission time and final score. BTW, I correlated with the most-used model. I can't guarantee they used that specific model on every single attempt. Of course, correlation isn't causation. Maybe Claude writes better code. Or perhaps the kind of student who consciously switches from ChatGPT to Perplexity or Claude is just a better, more engaged student. But a 9-to-10 point bump is a pretty good reason to experiment.

Which LLMs get you better grades?


A no-win situation can sometimes be transformed into a no-lose one simply by reframing the game.

When I was in class 8 or 9, our English teacher told us a story I'll never forget. There was a poor farmer who lived in a village. He owed the zamindar (landlord) of the village a lot of money. The zamindar had an eye on his daughter. "Marry your daughter to me, and I'll forgive your debt," he said. The farmer was reluctant. "Please, sir, what will the village say about your marrying such a young girl?" he asked. The wily zamindar said, "They cannot argue with God's will. Let fate decide." The zamindar gathered the villagers near the river, picked up pebbles of white and black from the riverbed, and told the farmer, "I will put a white and black pebble in this bag. If your daughter picks the black pebble, fate wills that she marry me. If she picks the white pebble, she needn't. Your debt will be forgiven." But without the villager's knowledge, the zamindar put two black pebbles in the bag. The farmer's daughter saw this, and was in a dilemma. Exposing the zamindar force them back into debt. Instead, she picked a pebble from the bag dropped it "by accident" among other pebbles. "Sorry!" she said. "But let me see which pebble is left in the bag." She showed the remaining black pebble from the bag. "I must have picked the white. Fate forgives our debt without marriage, sir." I remember this story is because it converts a "no-win" situation into a "no-lose" one -- just by reframing the problem. Whenever I think, "Damn! What a mess!" I hope I have enough wits to remember this story. There are many more black-pebble problems in life than we realize. We just need to be creative enough to drop the pebble and reframe the problem.

White Pebble Black Pebble


I analyzed Wikipedia citation data to identify the most critical domains. While the Internet Archive is cited most often, niche sites like Statistics Poland or sports-reference.com are the sole references for tens of thousands of unique pages.

Imagine you're an information anarchist. You undermine Wikipedia pages by nuking references. A genie has granted you a wish: you can nuke one entire domain. Just one. As a data-driven decision maker (who is also an information anarchist 🤷), which would you pick? A common choice is The Internet Archive. 2.9 million Wikipedia pages reference it. But, you're sneakier than that. A page isn't undermined just because some references are gone. It's undermined when all the references are gone. In that case, the most devastating domain to nuke is Statistics Poland. Over 45,000 Wikipedia pages cite only Statistics Poland as their reference. Or, if you're particularly fond of the Polish, destroy sports-reference.com. Over 37,000 pages cite it as their only reference. If you prefer hurting scientists, go for biolib.cz - an online encyclopedia of plants, animals, and very importantly, fungi. (But then, you don't need to nuke it - the "server is experiencing high traffic" quite often.) In any case, this is where you'll find most satisfaction, as more sites depend solely on biodiversity and natural history archives like marinespecies.org (WoRMS), Natural History Museum, IUCN Redlist than any other category. For detailed research on which site you'd like to nuke, see What If a Website Just Died? [](https://sanand0.github.io/datastories/wikipedia-citation-impact/)

Wikipidia Citation Impact


Hands-on workshops drive AI adoption better than talks, demos, or interviews because people change faster when they actively try the tools themselves.

To teach a mindshift change like AI adoption, I've tried to: 1. Workshop: get them to do it. "Let's try something. Can you share your screen?" 2. Live-code: show them how. "I'll share screens and tyep this." 3. Demo: show what's possible. "Here's what I built." 4. Talk: explain it. "Here's something we can build." 5. Interview: ask them about it. "What do you think?" 6. Listen: let them yap. The most effective are on top. But there is one intervention I didn't evaluate: 0. Exercise: give them a problem to solve. This takes more time and patience on my part, but might have the highest value. What this means for me is: Don't live-code. Workshop it. "Can you share your screen and try that?" Preferably as an open-ended problem. Don't demo/talk. Live-code it. "Let me share my screen and try that." Prepare micro-experiments. Keep a ready catalog if things to try.

Workshops help AI adoption


A personal blog archive can be turned into publishable audience-specific articles when AI handles outlet research, topic selection, and drafting in your voice.

You can use AI to submit not just talk proposals but entire articles from your past work. Ranjeeta said Built In wants an article and had written one on my behalf. If someone's going to write for me, I'd rather pick an AI! So here's what I did: Research the audience So I asked Gemini to research and suggest topics: I received a request to write an article for Built In (https://builtin.com/), an online community and publication for startups and tech companies. Research this publication. How large is it? How respected is it? Give me an intuition comparing with other publications. Also help me with the type of content to write. What do its readers read? What's popular? Among the most popular articles, what're typical article lengths? What is the most appreciated style of writing? Are these heavily linked, lightly linked, or not at all? Are they illustrated - and if so, how, e.g. images, charts, animations, videos, etc. and in any case, is this appreciated or frowned upon? What's the review process like? Knowing me, what are examples of topics you'd suggest I write about that will be well received and popular with this audience? It said basically told me: Built In is basically "HBR for coders" - huge reach (3M+), totally legit, and targets mid-to-senior tech pros who want the real scoop, not just fluff. They want "in the trenches" war stories and "how-to" career/tech guides (1k-1.5k words). Less "breaking news," more "here's how I actually built this." Keep it conversational and punchy (no jargon); no selling, but linking your blog/open-source stuff is cool. Pitch "I replaced my workflow with AI" (hot topic), "Chat is the new Dashboard" (your wheelhouse), or "Prototyping your Career" (agile life advice). The AI angle works. Suggest topics from existing content This is the cool part. I told it: I'd like to re-purpose one or some of my blog posts. Here are my LLM-related blog posts in 2025-26. I'd like you to recommend the top 3 articles I could write. Each article could cover 1 or more posts about a single topic. Think about what will be most impactful for this audience as well as unique, i.e. a lot less covered elsewhere. ... and then passed it all of my 2025-2026 blog posts related to LLMs using: Gemini churned and suggested three topics. Vibe Coding: It's not magic, it's a gamble-awesome for rapid prototypes but risky for production. RIP Data Engineering: Stop wasting 15 years fixing fragile web scrapers-just tell the AI. The AI Mirror: Flip the script and use AI to roast you-feed it your meeting transcripts. Find best content for topic I like #3 - AI Mirror: Using LLMs to Decode Your Leadership Style. Look at my blog posts and see which ones are most apt for this topic. Then synthesize from these, writing as much as possible with the same words I used in my blog posts, and combine them into an article for Built In. Plan first. Think hard about the approach, then execute carefully. It wrote a nice, long rubbish article that had little of my style. But, why be rude? Nice structure. But rewrite it using exactly my words in every place unless unavoidable. It wrote another nice, long, rubbish article with little of my style. I don't give third chances. List all the files you used as reference. Then list other files that would align with this topic. In other words, I would like all the file names directly or indirectly related to this topic that can serve as source material for this. ... and it gave me the most relevant articles. 2025/the-surprising-power-of-llms-jack-of-all-trades.md 2025/how-to-double-check-personality-flaws-with-ai.md 2025/llms-as-idea-connection-machines.md 2025/measuring-talking-time-with-llms.md 2025/llm-psychology-podcast.md 2025/wait-thats-my-mic-lessons-from-an-ai-co-host.md 2025/extracting-ai-advice.md 2025/top-8-ways-i-use-chatgpt-in-2025.md 2026/breaking-rules-in-the-age-of-ai.md 2025/turning-generic-gifts-into-joy-with-ai.md Generate the article using my words I started a new chat to avoid confusion and told it to: Write an article for Built In. Read the section below for context on Built In's audience, style, and content preferences. Then read my blog posts further down. Based on that, write an article titled: "AI Mirror: Using LLMs to Decode Yourself". This should be a synthesis of my words, i.e. always use my blog post content, except for transitions, etc. Select content and include images, data visualizations, links, etc. aligned with Built In's style. Then I fed it all the context about Built In as well as the relevant blog posts. The result was pretty good. It was mostly in my voice - literally copy-pasted from my articles. I just made some edits where it had used its own words. The best part is that when AI is assembling your own words, it's acting more like an editor, not an author. That probably isn't slop, right?

Writing articles from my blog posts


I share my process for tracking yearly goals through public email updates. I answer questions on handling "soft" relationship goals, using public commitment for discipline, and why I prefer automatic tracking via Google Fit and GitHub over manual logs.

I track my yearly goals by publishing and emailing them to my contacts: My year in 2020 My year in 2021 My year in 2022 My year in 2023 My year in 2024 My year in 2025 Here are questions people have asked about my goal tracking. How do you know that you have achieved the Better Husband tag? In 2024, she said that I was "definitely worse in 2023 than 2024." In 2025, after a long pause, she finally declared "Yes". 🙂 Your "Better husband - PASS" made me smile. How do you track relationship/soft goals without making them feel transactional? In this case, I don't track. But being serious about my goal makes me mindful. (E.g. during an argument, my mind-voice says, "Remember: Better husband. You're mailing the entire world!") Is living with a stranger something you have never done before? Roommates, staying at hostels, etc.? How are you defining this? Good point. I had roommates only when I was at IBM in Bangalore. They are such good friends that I forgot they were once strangers! If I invite myself to stay for 3 nights with someone I don't really know, I'll count it. What do you have planned for Live with a Stranger and what do you hope to get out of it? 1. Changing environment helps. I hope to get a new perspective 2. Being uncomfortable is good. It pushes boundaries. I hope to get comfortable with discomfort. How do you track "buy low"? By "Buy low" I mean make investmented when it roughly bottomed out. I check if I bought approximately at the bottom of a U curve. It's not a systematic behavior, nor a precise metric, nor a sustainable approach. So I dropped it. What is your process to manage the discipline to stay on course? Public commitment helps. E.g. I'm afraid to email everyone I know that I failed a goal. I pay upfront for courses. I get others to join me - harder to skip, then. Environment & habits help. Yoga as soon as I wake up, phone automatically tracks activity, hiding junk food, etc. Daily tracking helps. I know where I am and whether I'm progressing. When you "failed" the 80 heart points in 2024, how did you process that? Did you adjust mid-year or accept and move on to 2025? I didn't have a choice. I felt very bad (and still feed a bit bad), so I didn't process it well, I guess. The good part is, whether we process things well or not, live moves on. How granular should tracking be? I'm building a Git repository with daily logs, XP points, Python scripts for reports... Am I over engineering this? (Probably yes, but tell me anyway!) I track granularly. I prefer automatic tracking (e.g. GitHub for commits, Google Fit for heart points) over manual apps (e.g. weight on Google Fit, books on GoodReads) over notes (e.g. # of students). Regular tracking feels more important than granular tracking. Exception tracking seems powerful, e.g. track days when I miss Yoga. (That never happened, since I was conscious.) How do you balance "ambitious goals" with "life happens"? Is it okay to explicitly plan for lower standards during chaos (my Q2), or is that pre-planning failure? I've always aimed low - for fear of failure. I tried ambition... but not minding failure so much is working better for me. What inspired you to start this yearly email tradition? How has it evolved over the years? I started emailing goals in Dec 2020. Inspired by Tim Ferriss, maybe? It was a way to commit myself, stay in touch, and brag a bit. My earliest goals list is from 1996. I reviewed those annually. From 1999 - 2006, I tracked "achievements" on a Rating x Weightage = Points scale without goals. From 2007 - 2019, I tried various logging mechanisms but don't remember tracking goals. From 2020 onwards, I use this email tradition. What is the year on year short term to long term thread that connects these decisions to your life purpose? Horizons of Focus I don't know, so I asked Claude - and agree with its synthesis: Learn, Teach, then Automate.

Yearly Goal Tracking FAQ


Update people summary pages based on transcripts every week

About Updates


Enhance Marp slide decks by using this prompt to generate appendix slides from transcripts. Automatically create quizzes, find errata, research counterpoints, and get feedback on clarity to improve the educational value of your presentations.

Add appendices to (Marp) slide decks from transcripts to improve quality and learning.

Add derived slides from a transcript


I use this prompt to transform messy talk transcripts into structured FAQ-style slide decks. It applies the Pyramid Principle for declarative headings and includes automated sections for fact-checking, quizzes, and counterpoints to ensure technical accuracy.

Annotate transcripts with section summaries, creating FAQ-style slides from talks / AMAs.

Create an FAQ-style slide deck from a transcript


I share a prompt I use to extract deep value from call transcripts. It identifies missed emotional bids, unvalidated assumptions, and high-impact experiments, helping me improve how I engage others and learn from every conversation.

Analyze call transcripts to extract key insights, action items, and feedback.

Analyze Call Recording