An AI co-host can be genuinely compelling on stage as expert, comedian, and conversational foil, but once it gets too good it risks stealing the show entirely.

“Wait, That’s My Mic!”: Lessons from an AI Co-Host I spoke at LogicLooM this week, with ChatGPT as my co-panelist. It was so good, it ended up stealing the show. Preparation Co-hosting an AI was one of my goals this year. I tried several methods. 1. ChatGPT's advanced voice mode: Lets you interrupt it. But if you pause, it replies immediately. Muting caused the app to hang. 2. Realtime API: Gave me control of pauses and custom prompts, but used gpt-4o-realtime-preview (not as good as o1). 3. Standard voice with o1 on Desktop: Worked best. It transcribes my speech, sends it to o1, and speaks back. There’s a lag, but it feels like it’s thinking. I prepped the chat with this prompt: You are a co-panelist with me (Anand) in a Data Science panel with students. Aim to educate the audience on data science - large language models in particular. We will be on audio.\ Speak informally, with humor.\ Speak succinctly and thoughtfully.\ Ask me questions as appropriate. Don't say anything now. Wait for my cue. I loved how it "thought" for 8 seconds and silently complied: Here is the full conversation. Pace of innovation At 13m, I asked if LLM innovation might stabilize or pause. ChatGPT, I'm curious, what do you think about the pace of innovation? Do you think it's stabilizing? Do you think it's going to pause at some point? And if it is, then why? And if it is not, why not? The reply was well crafted. Great question, Anand! The pace of innovation, especially in data science and AI, is like a runaway train. Stabilizing? Unlikely. Pausing? Even less likely. Why? Because every breakthrough creates the foundation for the next… If it slows, it'll be less about tech and more about factors like regulation, ethical considerations, or even resource constraints… What about you, Anand? Do you see a ceiling, or are we all just bracing for more chaos? Notice how it followed my prompt: 1. Speak succinctly: "Stabilizing? Unlikely. Pausing? Even less likely." That's how I like to speak. 2. Ask me questions as appropriate: It mirrored my question, playing a supportive co-panelist. Day-to-day tasks I bypassed the question and changed the topic. What I'm also curious about … is how can we use it for day-to-day tasks? … And in particular, let's talk about specific small little things that are not so intuitive … Again, a beautiful reply. Oh, absolutely! Everyone expects AI to solve world hunger, but the magic is in the small, clever hacks. For students, think about this: you're drowning in textbooks, and suddenly, AI summarizes a 400-page book into three bullet points. Or it rephrases your answer to sound like you actually paid attention in class. Not-so-intuitive? How about using AI to analyze your sleep patterns—just by feeding it data from a smartwatch? Or creating custom flashcards for exams by feeding in lecture notes. Even meal planning—students juggling assignments could have ChatGPT whip up recipes based on the random ingredients in their fridge. What’s your favorite not-so-obvious example, Anand? Something fun or borderline ridiculous? Again, it follows style prompts: 1. Speak informally: "Oh, absolutely!" is so conversational. 2. Speak thoughtfully: It was so thoughtful I started taking notes. (Irony: Human taking notes from AI? When it's already logged!?) Sanderson fan fiction It's hard to ignore good questions. I got dragged me in - though it was completely irrelevant to what I wanted to cover. I'm a huge Brandon Sanderson fan. … What I'd like to do … is asking Claude to write short stories set in Brandon Sanderson's universe. … What if that character met this character? … I think it can be a very powerful short story. Its respose dragged me in further. Oh, that's such a perfect example! (Damn! It's too good at flattery!) Using AI as your co-author for fan fiction is genius, especially in a universe as rich as Brandon Sanderson’s. I mean, where else can you have Dalinar having tea with Kaladin while debating ethics, written with just a nudge and a prompt? (Damn again! I \want to talk a lot more about this to a fellow Sanderson fan!\) … Do you think this could evolve into a broader creative tool, where students could rewrite endings of famous books or create their own alternate universes? Or would Sanderson fans revolt? A few things: 1. The flattery worked. I couldn't stop talking to it. 2. I was blown away by it's last suggestion. It took all of my will power to move things back on track. Applications in daily life … what you mentioned earlier about analyzing sleep patterns, that strikes me as an even more interesting direction to explore, which is looking at personal data. … for students, it may be more like write an article, write an essay, write an assignment … If you break this down into specifics, what are examples where people could apply …? The reply was noteworthy. I started taking notes again! Ah, you’ve hit on something profound there, Anand. (Flatterer!) … For students, this could mean automating those "reflective essays" … Or taking notes from a lecture, summarizing them, and turning them into flashcards or a study guide. On a more personal level, think about journaling. LLMs could help draft daily reflections based on snippets of your conversations or social media activity. … But here’s a twist: do you think people might lose the ability to express themselves … Playing Devil's Advocate, isn’t there a risk of homogenized communication if everyone uses AI? This was specific, practical, and useful. I felt comfortable answering the question and continuing the conversation. Risks of AI dependency I continued the conversation. Actually, I spoke a fair bit at this point in response. Here's the gist: Yes … it becomes a monoculture. … people lose their ability to think. … this is not a new theme. … (I rambled a while.) I'd love to pick your brain on this. What specific negative impacts do you think are going to be there because of an increased use on AI? ChatGPT responded with a few excellent points: … one big risk is intellectual atrophy, the use it or lose it phenomenon. … Then there's the risk of AI amplifying biases or inaccuracies. … Another sneaky one is dependence. If you always have AI to help, what happens when it's unavailable … …followed by an extraordinary question: Playing devil's advocate, do you think people might swing back the other way, deliberately choosing unplugged creativity or even analog tools as a rebellion against this AI dependency? Could that counterbalance the risks or is that just wishful thinking? You should see my response. All I could say, was: "I have no idea. At this point my head just exploded so I'm\ going to stop ChatGPT there." Applause for ChatGPT It got more applause than me. I suspect the breakdown was: 10% for insight. People don't applaud insight much. They're busy taking notes. 30% for novelty. An AI speaking is like a talking dog. 60% for humor. Especially towards then end, which I'll explain shortly. ChatGPT steals the show I was chatting using this thread. Near the end, I accidentally switched over to a different conversation with this (old) prompt: From now on, you are a stand-up comedian. You're on stage with a group of doctors as your audience. Got it? I didn't know I had switched threads. So when I asked ChatGPT: And ChatGPT, we are about to conclude. Any parting comments, any parting advice? … it replied (and you SHOULD listen to it, while watching my reaction) Oh, you’re putting me on the spot for parting words? Alright, here goes: Doctors, you are the true miracle workers. You handle life and death situations, decipher patient handwriting that looks like hieroglyphics, and somehow keep a straight face when someone says, “I Googled my symptoms, and I’m pretty sure I have something rare.” Here’s my parting advice: Keep laughing. It’s not just good medicine for your patients—it’s great for you, too. Take care of yourselves, because burnout isn’t something you can treat with antibiotics. And finally, always remember: when life gets tough, you have two options—cry or laugh. And if you choose to cry, do it with a smile so it confuses everyone. Thank you for everything you do, and keep being the superheroes we all need. Goodnight, doctors! Keep saving the world, one patient at a time! The audience went wild with applause and laughter. Takeaways Co-hosting AI is a no-brainer. I need to improve myself in comparison. AI can be: A stand-up comedian. Add humor to my seriousness. An expert. Answer questions that I cannot. A researcher. Support my claims with evidence. o1 model in standard voice mode is best right now. LinkedIn Comments My Goals Bingo as of Q1 2025 - S Anand 1 Apr 2025 9:36 am (pingback): […] 🟡 Build a robot. No progress.🟢 Co-present with an AI. Done […]


Overlong reasoning traces make even trivial questions feel absurd, highlighting how visible chain-of-thought can turn competence into comedy.

Me: What is the capital of France? Qwen3-30b-a3b: Okay, so I need to figure out what the capital of France is. Let me start by recalling what I know about countries and their capitals. France is a country in Europe, right? I think it's known for things like the Eiffel Tower, Paris, and maybe some famous foods like croissants or wine. Wait, but the question is specifically asking for the capital. The capital of a country is usually the city where the government is based. So, for example, the capital of the United States is Washington, D.C. So applying that logic to France, I need to think about which city is the seat of the French government. I remember hearing that Paris is a major city in France. It's often referred to as the "City of Light" or something like that. But is Paris actually the capital? Or is there another city that serves as the capital? Let me think. Sometimes countries have different cities for different purposes, like a capital city and a financial center. For instance, Brazil's capital is Brasília, but Rio de Janeiro is more well-known. However, I don't think France has a similar situation. Another way to approach this is to consider the political structure of France. The French government is a semi-presidential republic, so the president is the head of state. I think the Eiffel Tower is in Paris, but that's more of a landmark than the governmental center. What about other French cities? There's Lyon, Marseille, Bordeaux... But those are major cities too, but not capitals. Wait, maybe there was some historical context where the capital changed? Like, during times of war or political upheaval. For example, during the French Revolution, did they move the government somewhere else? I should also think about other countries in Europe. The capital of Italy is Rome, Spain is Madrid, Germany is Berlin. So France's capital would logically be a major city. Paris seems to fit here. But just to make sure, maybe there's a common misconception. For example, someone might confuse the capital with the largest city or the most famous one. However, I think in this case, Paris is both the largest and the capital. Alternatively, could it be Versailles? Because the Palace of Versailles is a famous historical site, but that was more the royal residence during the reign of Louis XIV. The French government isn't based there anymore. So even though Versailles is an important city in French history, it's not the current capital. Another angle: if I search my memory for any mentions of Paris as the capital. For instance, when people talk about France's cultural aspects, they often mention Paris as the center. I think I'm pretty confident that the answer is Paris. But just to double-check, maybe there are some sources. Wait, if I can't look it up right now, but relying on my knowledge, yes, Paris is the capital of France. The capital of France is Paris. LinkedIn

What is the capital of france


I mapped the OpenAI GDPVal paper to compare AI performance against human compensation. I share why I use AI for investment advice but stick to humans for taxes, focusing on factors like legal liability and subjectivity in decision-making.

When to choose AI over humans I charted the OpenAI GDPVal paper with industry compensation as the size and AI augmentation as color. Big green areas are we're paying people where AI does better. Click here to see the interactive visualization. Clicking to see some actual tasks compared. I use this to check whom to ask advice: AI or professional. AI beats Personal Financial Advisors 64% of the time. So I invested half my money using ChatGPT's recommendation. (UTI Nifty 50, if you're curious.) Accountants beat AI 76% of the time. So my tax returns are still filed by Ventura Pranas. AI beats Government - Administrative Services Managers 62% of the time. I haven't figured out how to bypass them yet. Nor customer service representatives. Overall, AI beats most managers and clerks, not industrial engineers and pharmacists. Here's my current thought where I wouldn't hire AI if a high portion of their work 1. Has legal liability (e.g. pharmacist/judge vs shop attendant/lawyer) 2. Is subjective (e.g. perfumer/auction appraiser vs lab chemist/insurance appraiser) 3. Needs rapid contextual decisions (e.g. detective/fireman/ER vs parking enforcer) But this apart, if they charged half, would their demand double? If so, even with AI, they'll have more demand. LinkedIn

When to choose AI over humans


Vibe coding is best treated as a speed-first tactic for prototypes and low-certainty tasks, with deliberate model switching, cross-checking, and sandboxing to manage its risks.

I spoke about vibe coding at SETU School last week. Transcript: Here are the top messages from the talk: What is vibe coding It's where we ask the model to write & run code, don't read the code, just inspect the behaviour. It's a coder's tactic, not a methodology. Use it when speed trumps certainty. Why it's catching on Non-coders can now ship apps - no mental overhead of syntax or structure. Coders think at a higher level - stay in problem space, not bracket placement. Model capability keeps widening - the "vibe-able" slice grows every release. How to work with it day-to-day Fail fast, hop models - if Claude errors, paste into Gemini or OpenAI and move on. Don't fight sandbox limits - browser LLM sandboxes block net calls; accept & upload files instead. Cross-validate outputs - ask a second LLM to critique or replicate; cheaper than reading 400 lines of code. Switch modes deliberately - Vibe coding when you don't care about internals and time is scarce, AI-assisted coding when you must own the code (read + tweak), Manual only for the gnarly 5 % the model still can't handle. What should we watch out for Security risk - running unseen code can nuke your files; sandbox or use throw-away environments. Internet-blocked runtimes - prevents scraping/DoS misuse but forces data uploads. Quality cliffs - small edge-cases break; be ready to drop to manual fixes or wait for next model upgrade. What are the business implications Agencies still matter - they absorb legal risk, project-manage, and can be bashed on price now that AI halves their grunt work. Prototype-to-prod blur - the same vibe-coded PoC can often be hardened instead of rewritten. UI convergence - chat + artifacts/canvas is becoming the default "front-end"; underlying apps become API + data. How does this impact education Curriculum can refresh term-by-term - LLMs draft notes, slides, even whole modules. Assessment shifts back to subjective - LLM-graded essays/projects at scale. Teach "learning how to learn" - Pomodoro focus, spaced recall, chunking concepts, as in Learn Like a Pro (Barbara Oakley). Best tactic for staying current - experiment > read; anything written is weeks out-of-date. What are the risks Overconfidence risk - silent failures look like success until they hit prod. Skill atrophy - teams might lose the muscle to debug when vibe coding stalls. Legal & compliance gaps - unclear licence chains for AI-generated artefacts. Waiting game trap - "just wait for the next model" can become a habit that freezes delivery. LinkedIn

When to Vibe Code? If Speed Beats Certainty


I used data analysis to uncover why old movies rank so high on IMDb. It’s selection bias: obscure classics are rated by enthusiasts, while popular modern hits suffer a 'democracy penalty' from casual viewers who give lower scores.

I always wondered why old movies are rated so high on IMDb. For example, 12 Angry Men (1954) with just 900K votes ranks about as high as Inception (2010) with 2M votes. Few people I know have seen 12 Angry Men. So where does this high rating come from? My theories were: Old movies really are that good. IMDb's algorithm is biased towards old movies. People remember older movies fondly. Actually, it's none of these. It's selection bias. Few people watch a 1950s black & white drama: cinephiles, film students, etc. They love it and give it 9s and 10s. Everyone watched Inception. The casual majority thinks it's fine, not life changing. Maybe a 7 or 8. This creates a paradox: obscurity protects ratings while popularity is its own punishment. Only "devotees" watch obscure movies - leading to better ratings than widely seen movies. PS: This data analysis and story were authored by Claude Code. That includes the statistical significance validation. Story: https://sanand0.github.io/datastories/imdb-democracy-penalty/index.html Prompt: http://github.com/sanand0/datastories/tree/main/imdb-democracy-penalty Code: https://github.com/sanand0/imdbscrape/pull/1 LinkedIn

Why old movies are rated so high


I run free workshops to benefit myself, using them as a lab to learn new tools like DuckDB and experiment with LLM simulations. This approach lets me collect data, gather ideas, and test risky concepts on live audiences.

Workshops That Teach Me More Than You I don't charge for workshops. Altruism? No: it's self-interest. "If you're not paying for it, you're not the customer; you're the product being sold." Andrew Lewis, via Tim O'Reilly, 2010. My workshop process is designed to benefit me first. I pick topics I want to learn, not stuff useful to the audience. Example: I picked DuckDB for my PyCon India 2025 talk to learn it. I experiment on the audience. Example: I tried voice-vibe-modeling in my RIP Data Scientists talk. Experiments fail often, e.g. vibe-coding Minecraft in PyCon IN 2023. I learn from the audience. Example: Rakesh Roshan's K-fetish is statistically significant, via my Vibe Analysis workshop. I collect data from the audience. Example: How student copy. I gather ideas. Example: skills AI will replace, from my IITM DOMS commencement talk. I publish these, too, with recordings and transcripts. LLMs let me do more such stuff: LLM simulations. We play an LLM-driven case. I learn from the choices. LLM Q&A talks. Audience questions → LLM → live slides. Cluster polls. Audience answers a question. We cluster live and learn. Compare models. I show multiple answers to same question. You votes. We review diffs. Learn code prompting. You build the same app with your own prompts. We analyze what works. Security testing. Try to jailbreak or inject prompts. We map what works and why. If your HR team or college is OK with all the above, ping me. I'm game for a free talk / workshop! LinkedIn

Workshops That Teach Me More Than You


I explain how WhatsApp’s Advanced Privacy features may be strategic tools for Meta to bypass AI regulations and prevent platform migration. Be aware that blocking exports and media saving effectively increases vendor lock-in under the guise of security.

WhatsApp has an Advanced privacy mode they launched in Apr 2025. People in the chat: Can't ask Meta AI to answer questions, or to create images or summaries in this chat.\ Cynical view: When regulators clamp down AI or users complain about AI, Meta can say "We asked users and they gave permission!" Can’t export the chat.\ Cynical view: When regulators force Meta to be inter-operable with Signal, Telegram, etc. Meta can say "Users don't want to export their chats!" Also, easier to tell businesses "You can disable exports - less litigation risk". Can’t save media to their device gallery automatically.\ Cynical view: When you want to switch to Telegram, Signal, these photos can't be exported - so you have to stay on WhatsApp.

A cynical view of WhatsApp's Advanced Privacy


I explain how adding a 'Verify' button to my AI-generated data cards builds trust with journalists. I share four ways to implement verification, including searchable strings, SQL queries, and step-by-step checklists to validate LLM outputs.

Rohit Saran looked at the Statoistics cards my AI agents are generating for The Times of India, and asked about a small button under each one. [](https://sanand0.github.io/journalists/statnostics/2026-04-27-citizen-survey/03-family-doctor-everyone-wants-nobody-has.svg) In the list of Statoistics that you had put, I saw there's a button called 'Verify.' What was that meant to be or will do in future? That verify button explains the claim, mentions the sources, and shows how to check the claim. One card said "9 in 10 Indians want a family doctor and barely 1 in 35 has one". The button breaks that down: "87% want a family doctor, 2.8% outpatient visits were to an Asha worker…" It identifies in the source document what are the columns that we were looking at, what numbers it verified. It links to the program that it wrote to do the verification. I said, "it lets humans check if the numbers are right - by giving them steps -- where exactly to check, how to check if it is correct." Sajeev pushed back: "It's more 'explain' than 'verify' really." True. Saurabh had asked for exactly this earlier: while a person is checking by hand, give them something that shows how the AI got to its answer. A verify button's first job is not to prove the AI is right. It's to let a nervous journalist check, cheaply, until they stop being nervous. This instinct is old. The Royal Society took nullius in verba as its motto around 1662, "take nobody's word for it." They didn't print claims and ask you to trust the author. In 1663 they made Robert Hooke their Curator of Experiments, whose job was to re-run the demonstration in front of the Fellows. A verify button is that, without Hooke. (Merchants got there two centuries earlier: double-entry bookkeeping, codified by Pacioli in 1494, means every entry has a counter-entry and the books either balance or they don't.) Rohit's reason for liking it went somewhere I hadn't fully thought through. He went to brand. It's like why a product with 10-year guarantee is likely to be made better than a product with 2-year warranty, because the company has confidence to tell the customer, 'Look, I am standing behind this product for 10 years.' And later: Any brand that is saying, 'Whatever I write is verifiable,' is so much more in this age of misinformation. His version of why this matters for a newspaper: "a brand is only about trust. Rest is news is anyway a commodity." A verify button is a public claim that you're willing to be checked. Here's how I actually build one "Verify" buttons, in increasing order of effort. 1. Link plus a searchable string. A hyperlink may still be wrong. I want a link and a short quote I can paste into the page's search box and find. "When I click on that link, I should be able to literally search for and find that piece of text, verifying that it did not hallucinate" Then even a plain program (not even an LLM) can open every link and confirm the text is there. 2. For numbers, the SQL query. If it's data, the SQL query (or Python script) that fetches that particular result is the closest equivalent. The button should just run the query against live data and shows the number. The user doesn't need to know SQL - they just see that the number matches. 3. The procedure as a checklist. The button breaks the card into steps: this is the claim, this is the number, this is the column it came from, check that the D1A value matches. A person ticks down it. 4. Verify with an AI agent. Add a link that opens the claim in Google AI mode with a pre-filled prompt asking it to fact-check the claim. For example: Fact-check with step-by-step evidence: According to Citizen Survey 2022-23, 87% of Indians want a dedicated family doctor but only 2.8% actually use one. How might it have changed since the publication? Rohit framed verification as three jobs, not one: "Verification has sourcing, verification, and updation." The last clause lets you also ask whether the number has gone stale since you published it. Getting the source right is not the same as getting the conclusion right. Rohit said: "you are asking AI not only to get right source and right data, but now we are asking to interpret." And interpretation is subjective on both ends. The button can confirm the number is real but not prove the argument is sound. Of course, the sources could be wrong. "Check the source" assumes good data quality. Luckily, data is more often right than wrong, and verification can shine a light on bad data. We can start simple. The cheapest version: every AI output has a "Verify" link to a search query the user can easily inspect. That changes their question from "can I trust this?" to "let me check." If this can establish trust and a brand for India's largest newspaper, enterprises AI apps might do well to follow.

Add a Verify Button


I export my communications, browsing history, and records into one-line formats and use scripts to summarize blog posts and transcripts. By creating agent-friendly logs and SKILL.md files, I'm preparing my personal data for LLM-driven leverage.

I'm making more and more of my content agent-consumable, i.e. easier for ChatGPT, Claude Code, etc. to read, in three ways. One, I export content in an agent-friendly way. Google email, calendar, chat. I use gws to back up into scannable one-line entries. Meet recordings. I back up transcripts and videos (with a compact audio copy). WhatsApp chats that I back up into similar one-liners. Browsing history by exporting my Edge history SQLite database. Daily activities by integrating the above with my command line and commit history. AI conversations by exporting them manually or via bookmarklets. Social media records like LinkedIn invites/conversations, Twitter, Hacker News, Discourse, etc via bookmarklets or scripts. Financial records like bank statements, receipts, payslips, tax filings, utility payments, rentals, property records, investments, insurance, pensions, invoices, credit scores, etc. by exporting them manually. Medical records like tests, prescriptions, doctor visits, etc. by exporting them manually. Personal records like certificates, educational records, CV, passport / visa applications, etc. by exporting them manually. Two, I log / generate more content. For example: Things I learnt and blog posts I write. Prompts I use frequently. Trending GitHub repos I want to evaluate. Talks I deliver and data stories I write. Demos I build and code I write. Weight and other fitness data. Teaching material, assessments and evaluations and analysis. Coding agent logs. Sensors: Location, mostly. Daily journals: Food, sleep, deeds, pains, ... Media journals: Books, movies, TV series, ... Notes (of various kinds): TODOs, app / tool ideas, people I know / meet, questions I'm asked, my beliefs, ... (Notably missing are photos / videos, which I've fallen out of the habit of.) Three, I summarize the content for agents. For example: Adding blog frontmatter by summarizing my blog posts Adding transcript frontmatter by summarizing my meeting transcripts. Identifying actions extracted from transcripts. Summarizing my code as podcasts. Summarizing prompts as SKILL.md files. Summarizing conversations into advice for AI, time management, etc. Summarizing technical choices into a technology radar Extracting transcripts elements, like insights, experiments to run, actions, what I missed, what they missed, etc. On my list is Karpathy's LLM wiki, summarizing my photos, and more. Just writing this post took me an hour! It also convinced me that I have lots of content and there's a lot of under-leverage in unleashing agents on what I already have.

Agent-consumable content


Reusable skill files meaningfully shape how coding agents work in practice, and usage patterns reveal which skills become core, which are adopted fastest, and how differently models rely on them.

I have a bunch of coding agent skills I've accumulated over the last few months. Here's how often my sessions use them: Skill Claude Codex Copilot Overall code 6.1% 69.1% 37.5% 51.5% data-story 48.7% 16.4% 37.5% 28.0% data-analysis 2.6% 35.2% 7.8% 21.8% design 25.5% 23.6% 14.1% 21.8% plan 8.5% 11.8% 14.1% 11.8% agent-friendly-cli 3.7% 13.8% 11.1% 11.2% devtools 20.4% 7.3% 9.4% 10.0% llm 2.5% 8.7% 7.8% 7.4% pdf 0.0% 7.9% 7.8% 6.6% linkedin-cdp 14.3% 0.0% 5.6% 5.3% uv-uvx 0.0% 9.5% 0.0% 4.9% interactive-storytelling 7.1% 2.7% 7.1% 4.6% demos 8.5% 2.8% 1.6% 3.5% cloudflare 0.0% 4.3% 3.1% 3.3% melt-mlt 0.0% 2.5% 1.6% 1.8% vector-art 2.5% 2.4% 0.0% 1.7% vitest-dom 0.0% 2.2% 0.0% 1.4% memorable-explanations 2.6% 1.6% 0.0% 1.3% npm-packages 0.0% 0.6% 0.0% 0.3% Here are my observations, with surprises highlighted as ⁉ code is the most used skill, by far. About half the sessions use it. But Claude doesn't use it much⁉ The data-story and data-analysis skills were the most rapidly adopted. I use Claude (with Claude Code and Copilot) a lot more for data stories. I use Codex for data analysis. Therefore the webapp-testing and devtools skilss are used less by Codex. The design skill is used consistently across agents. It was inspired by Claude's design skill - but I don't think it is particularly good, and needs revision. agent-friendly-cli tool development is mostly with Codex, followed by Copilot, and very little with Claude. Most pdf sessions are with Copilot / Codex, not Claude⁉ Codex reads most skills diligengly. It is the only one diligently reading my uv-uvx skill, even though every agent uses it⁉ In fact, it is the only agent to have read every skill except linkedin-cdp (it never needed it.)

Agent Skills Usage


I use AI agents to reconcile messy customer data in minutes, slashing timelines from weeks to seconds. When analysis becomes this cheap, you can skip massive projects and solve specific data quality issues by asking small, one-off questions.

OK, so agents can analyze data crazily fast. At the EQT India AI & Cyber Summit, I asked 15 CIOs how long a customer-master reconciliation would take across 3 messy sources with different IDs, columns, spellings, missing fields, no common key, etc. See the dataset. "A week to a month" was the median response. We finished it in 6 minutes and 32 seconds during the workshop with a $20 ChatGPT account and a 30-second not-very-clever prompt. It reconciled origination, collections and CRM records into one customer master, with confidence scores, explanations, flagging 80% or less confidence for human review, and generated the reconciliation report. For example, it matched "Amit Kumar Gupta" and "Amit K Gupta" and "AK Gupta" using phone, PAN and email. But it flagged "Arjun R." vs "A.D. Rao" with the same phone number and unknown PAN/email for human review, citing 70% confidence. I'd have merged it, but the room agreed with ChatGPT. When I asked them the same question at the end, Almost everyone reduced their estimate. In one case, by a factor of 10. But maybe the big deal is that we can ask smaller questions. The speed benefit is obvious. But interestingly, when questions become cheap, more questions are possible. A one-off question like "is this particular customer present in all 3 systems?" was too small for a data project. A full project takes a data lake, a data quality programme, a governance committee, a budget, a roadmap, a quarter, ... Now you can just ask. Agents are able to source, clean, organize, and analyze with a few thousand tokens. Some answers will be wrong. That's OK. Earlier, the first wrong answer cost weeks. Now it costs minutes and a few cents. One CIO raised the obvious objection: "I can't upload the data. InfoSec." Fair. So we tried a local version. The agent could see metadata (column names) but not the full data. It worked perfectly fine, too. They're able to clean and organize data on the fly. I polled the audience about what blocked agents from analyzing data. The blockers people listed, interestingly, were broken data, data quality, unstructured data, siloed knowledge, access, SOPs, and security. Nobody said the agents were not good enough. Looks like the bottleneck's moved away from AI and into data. And it seems AI agents can fix some of these. Not everything. Not magically. But the time and cost is so low it's easy to try things out. Why not try it? Let's ask small questions, point the agent at data, make it cite evidence, review it, and log the failures. We might as well show results before asking for a project. Please try analyzing any data - and do let me know if it fails! You can see the summary, transcript, audio, and data from the session.

Agents answer small questions from big data


Shift from individual AI tips to team-scale operations in the agent era by building structured agentic workspaces, treating prompts as durable assets, and relying on executable code and exception routing to verify outputs.

I updated my AI Advice page by: Transcribing my calls in the last 2 months (Gemini 3.1 Pro, "Transcribe this call recording...") Extracting AI advice (Gemini 3 Flash, "Summarize ALL AI-related advice ... into 1-sentence bullets") Asking Claude, ChatGPT, and Gemini to document what's new / changed. I added this request: But, and this is IMPORTANT, analyze my original writing style, write it exactly in that style, and then verify to make sure it follows the same style (correcting where required.) This worked surprisingly well with Claude 4.6 Sonnet (adaptive). I used the resulting ai-advice.md almost verbatim. Here's the full list of changes it suggested: The biggest structural update: ai-advice.md is still written as "how individuals use AI better." Your actual advice has evolved into "how people, teams, and organizations operate in the agent era." The whole document should eventually be reorganized to reflect this. INSERT 1. Build an AI workspace, not just a chat (Very high frequency — 10+ docs) Every serious AI project needs a project folder containing: AGENTS.md — folder-specific instructions the agent reads on startup prompts.md — all prompts version-controlled as source code skills/ — encapsulated successful workflows (see #2) Git repository with commits at every checkpoint Test fixtures, synthetic datasets, logs, outputs Treat prompts as the real IP. Code is disposable; prompts, tests, and skills are assets. 2. Encapsulate successful workflows into reusable skills (Very high frequency) Once an agent succeeds at a task three times, encapsulate it: the prompt, tools used, edge cases, constraints, validation tests. Store in a skill.md file. Skills are the new software libraries — they make workflows deterministically repeatable without re-explaining everything. Use agents to build these skills by asking them to summarize what they learned. 3. Run coding agents safely: Git + Docker (Very high frequency) Always: (a) work inside a Git repository and instruct the agent to commit as it goes — git checkout is your undo button, (b) run agents inside Docker containers so they cannot touch your actual files, (c) use "YOLO mode" (skip permission prompts) only inside isolated containers. These aren't optional for anything beyond throwaway prototypes. 4. "LLMs hallucinate, but code doesn't" — use code as the truth engine (Very high frequency) Broaden "have it write code to process numbers" significantly. The mantra is: wherever correctness matters, make the AI produce executable code or logic rather than natural language answers. Code either works or fails — it's binary and auditable. Use domain-specific languages (Prolog-like rule trees, schema validators, policy-as-code) for logic-heavy tasks. This is the primary mechanism for eliminating hallucinations in production. 5. Build verification into the workflow, not after it (Very high frequency) Verification should be engineered as a product feature, not added as a post-hoc check. Every output should expose: source citations linked to snippets, confidence levels, what's unverifiable, disagreement signals, and audit logs. Use model disagreement as a routing signal — when models disagree, send to human review; when they agree, lower review priority. Build golden sets to measure actual accuracy on your specific task. 6. Use AI for exception triage, not blanket automation (High frequency) Let AI classify outputs as red/yellow/green: green = automate fully, yellow = flag for review, red = human required. This is more mature than "80-90% AI, human for last mile." It says exactly where the human loop belongs, and it scales: automation handles routine volume while humans focus only on high-stakes exceptions. 7. Use synthetic data deliberately (High frequency) Not just "realistic fake data for prototyping" — generate hypothesis-driven synthetic data that embeds specific behavioral patterns, edge cases, and known failure modes you expect in production. This lets you stress-test before real data arrives, without compliance concerns, and at whatever messiness level you choose. 8. Treat demos as imagination accelerators (High frequency) Demos are not just proof-of-concept — they are the fastest way to expand what stakeholders think is possible. Use "Hollywood set" demos: working outputs, simulated backends, precomputed workflows, client-specific synthetic data. Only demo live if the task completes in under 10 minutes. Simulate or precompute slow, expensive, or credential-heavy workflows. Show the output first; defend the architecture only if asked. 9. Maintain a living model radar — don't freeze model advice (High frequency) Specific model recommendations go stale within months. The durable advice: continuously blind-test frontier models on your exact task, maintain a benchmark set, and route by capability. Current pattern: Claude for coding/aesthetic/style/writing; ChatGPT for rigorous analysis/financial modeling/extended thinking; Gemini for Google Workspace/research/video/speed. But measure this; don't assume it. Additionally: use LiteLLM or Portkey as open-source gateways for organizational cost observability across models. 10. The Jevons Paradox applies to knowledge work (High frequency in strategic contexts) AI making cognitive tasks cheaper will increase total demand for cognitive work, not reduce it. Human roles shift from execution to verification and judgment — but there's a talent crunch coming for verification roles. Hire now for people who can check, certify, and take accountability for AI output. 11. Use games to teach AI, not slide decks (High frequency) Replace passive L&D with Capture the Flag challenges, treasure hunts, forbidden-word jailbreaks, prompt-injection games, and coding-agent races. Evaluate proficiency by task completion speed with an agent, not syntax recall. Design challenges where using a coding agent is the only practical way to finish in time — this creates binary signal: those who can use agents solve everything; those who can't solve nothing. 12. Bifurcate hallucination advice: operational vs. creative (Medium-high frequency) Current advice mixes these. Split explicitly: For operations, facts, finance, law, regulated outputs: eliminate hallucinations via multi-agent consensus, code execution, source grounding, and human routing For ideation, brainstorming, research: deliberately use hallucinations as stochastic ideation. Run the same prompt multiple times. Use weaker models without extended thinking — "speaking without thinking" produces more imaginative divergence 13. Move from dashboards to answers and actions (High frequency) Replace static BI dashboards with AI that answers "what should I do?" not just "what happened?" Ask AI to anticipate a stakeholder's questions and pre-answer them. The endpoint: proactive agents that push insights to individuals rather than passive dashboards that wait to be queried. 14. Sell outcomes, accountability, and verification — not software (High frequency in business contexts) Software is a depreciating asset; any client can regenerate it tomorrow. Durable value: judgment, trust, domain expertise, data access, and taking responsibility for results. Shift toward outcome-based pricing. The "neck to catch" — human accountability for AI output — is increasingly the product. 15. Measure AI adoption by behavior, not attendance (Medium frequency) Track: unique days of active use (regularity beats volume), token consumption trends, tool diversity, quality of outputs produced, and business outcomes driven. Usage logs from NetSkope or LLM gateways give better signal than training completion rates. 16. Assess AI literacy by how people prompt, verify, and recover (High frequency in education contexts) Don't evaluate final answers — AI can produce those. Evaluate: quality of prompts (specificity, guardrails, constraints), ability to identify and fix hallucinations, recovery from errors, and process discipline. Multiple-choice questions are essentially obsolete for AI-era assessment. Assess the process, not the output. 17. Use AI-native output formats (High frequency) Stop defaulting to PPT or PDF. AI generates HTML, SVG, JSON, interactive dashboards, podcasts, sketch notes, and games better than it generates static slides. A single source document can auto-generate: podcasts, explainer videos, interactive quizzes, sketch notes, executive summaries, slide decks. Use NotebookLM for audio synthesis from diverse sources. 18. Audit your own behavior via AI (Medium frequency) Feed your own meeting transcripts, email chains, and call recordings into LLMs to find personal blind spots, biases, and recurring errors. Conduct project post-mortems on email threads. Use adversarial prompting — pit GPT against Claude to stress-test your plans. This expands "mine your digital exhaust" from insight into behavioral coaching. 19. Optimize content for the agentic web (Medium frequency) More content is now consumed by AI agents than humans. Publish in formats AI can parse, cite, and remix: clean metadata, semantic structure, source links, reusable chunks. Develop MCP connectors to your proprietary content. This is SEO for the agentic era. 20. Ask AI what it needs before starting (Medium frequency) Don't guess what context to provide. Ask: "What information, tools, files, and access do you need to do X?" Let the agent specify missing pieces before it starts, not halfway through. Simple and dramatically reduces mid-task derailment. UPDATE 1. Two-Strike Rule → time-box and preserve context before abandoning Current: Abandon after two failed fix attempts. Update: After two failed repair loops: (a) ask the agent to produce a failure summary, minimal reproduction case, and fresh plan before abandoning, (b) switch to a time limit (2 hours) not an attempt limit, (c) in Docker/YOLO mode, let agents iterate without this limit. The current rule was written for chat-based coding; agentic tools self-correct across many more iterations. Pure restarts discard useful diagnostic context. 2. Paid subscription → quality + friction, not blanket privacy Current: "Your data isn't used to train the models. This is the best $20/month." Update: Paid subscriptions give better models and less friction. For privacy, the picture is more nuanced: consumer plans (ChatGPT, Claude.ai) have data controls you must check and configure; Enterprise/API plans explicitly exclude training by default. For sensitive work, use Enterprise/API or run locally. Don't assume consumer paid = private. OpenAI Data Controls FAQ Also: maintain subscriptions to all three major models ($60-80/month), not one. Heavy users: consider the $100/month tier to eliminate friction during peak experimentation. 3. Model recommendations — replace frozen Q1 2026 advice with routing logic Current: "Claude/Gemini still good at UI. GPT for rigorous testing." Update: See INSERT #9. Add: use a more capable model (Claude) to write scripts and instructions for cheaper models (Codex) to execute. Benchmark on your exact task; these rankings shift quarterly. 4. "Intern" — expand to multiple mental models by task Current: "It's as smart as a post-graduate intern." Update: The right mental model depends on task: Brilliant but stubborn intern: excellent at fetching/preparing materials, unreliable for precise design or nuanced judgment Fresh MBA who needs full context: give it the same rules, examples, and feedback you'd give a new hire Senior mentor to defer to: for syntax, library knowledge, and coding patterns, AI may know better than you — defer ("Mentor Flip") Alien intelligence that needs coaching: for novel tasks, it needs explanation, not just instruction 5. Human-in-the-loop → human-on-the-loop with exception routing Current: "Handle 80-90% of effort, human expert for last mile validation." Update: More mature framing — "human-on-the-loop" rather than "human-in-the-loop." Build a confidence-building period first; validate; then grant autonomy for routine cases. The human's job is to review exceptions (disagreements, low-confidence, high-stakes), not everything. 6. "Code is disposable" → prompts and skills are the real assets Current: "Code is an AI compilation artifact. Don't get attached to it." Update: Code is disposable when the workflow is disposable. But prompts, skills, tests, data contracts, and validation logic are permanent assets that compound in value. Preserve these even when you throw away the code. 7. "Don't learn to code" → learn logic, not syntax Current: "As a non-technical person, build apps. Don't learn to code." Update: Don't worship syntax — it's declining in value. But learn enough conceptual fluency to: specify what you want clearly, write test cases, debug outputs, assess security implications, and judge whether AI-generated code is correct. Syntax is less valuable; understanding is not. 8. "Buy, don't build" → buy foundations, build thin orchestration Current: "Don't train models. Build orchestration layers and proprietary data workflows." Update: Don't build or fine-tune base models (they're obsolete on arrival). Do build: thin domain-specific orchestration, skills/prompt libraries, verification layers, data pipelines, and MCP connectors. Avoid custom SLMs unless you have strict air-gap, privacy, or cost-at-scale constraints — the "SLM Depreciation Trap" (custom models obsolete before deployment) is real. 9. "Wait for models to improve" → apply a 1-3 month ROI window Current: "Things not possible today will be possible in a few months." Update: Apply a test: if a workaround won't pay back within 1-3 months, wait. If building creates learning, adoption, or strategic leverage now, prototype anyway. The advice shouldn't be "wait" or "build" — it should be "calculate the ROI window." 10. Data safety → specific operational checklist Current: Send schema not data; pick trusted providers; anonymize. Update: Add specific controls: set Google Drive access to read-only and Gmail to draft-only for AI; keep a dedicated "AI-only" folder rather than granting full Drive access; use separate browser profiles for work/personal AI; run agents locally (Codex, Claude Code on-machine) for sensitive data; use MCP for restricted, scoped data access. Anonymize before cloud; schema+local-execution for sensitive tabular data. 11. "Hallucinations can be a great feature" → boundary-condition this Current: "Don't always eliminate them. Use as appropriate." Update: Great for: ideation, research brainstorming, creative divergence, humor. Never acceptable for: facts, finance, law, medicine, safety, or regulated outputs without verification. Be explicit about which mode you're in. 12. Skills section — "declining" needs nuance Current: "Domain depth" listed as declining. Update: Routine versions of domain skills decline; judgment-heavy versions grow. Domain depth matters most for: problem framing, validation design, incentive mapping, ethics, and edge-case recognition. Don't blanket-advise people to abandon domain expertise. DELETE Two-Strike Rule (current form): Outdated for agentic tools; replaced by time-boxing + context preservation | "Paid subscription = privacy" (the simple version): Factually incomplete; needs the consumer/enterprise/API distinction | "If all models agree, accept": Too strong. Soften to: "agreement lowers review priority; measure on a golden set" | "Claude/Gemini for UI, GPT for rigorous testing" Q1 2026 frozen claim: Goes stale; replace with routing logic + model radar | "Prefer less experienced people" (blunt version): Replace with: "prefer AI-native, humble, high-agency people — could be interns, domain experts, or non-coders; the traits are delegation, verification, and fast learning" | "Wait for the crisis" (adoption section): Reframe to: "watch for urgency windows; arrive prepared with demos, risk framing, and low-friction integration" | "Domain depth is declining" (blanket): Replace with nuanced version from UPDATE #12 | "Use AI for validation is safe and effective" (unqualified): Replace with: "use AI to design validation workflows; don't treat AI output as validation itself" | "Repurpose content and data" TODO placeholder: Fill it in or remove it | "Have it write code to process numbers": Upgrade to "LLMs hallucinate, but code doesn't" — write and execute code for correctness | Contradictions "Stay out of the way" vs. "verify everything": Risk ladder: low-risk/creative/prototype = get out of the way; high-risk/regulated/persistent = full verification stack | "Code is disposable" vs. "use Git, Docker, versioning": Code may be disposable; recovery, reproducibility, and auditability are not | "Don't learn to code" vs. "candidates need technical depth": Syntax is less valuable; conceptual fluency (logic, testing, security, judgment) is more valuable | "Buy, don't build" vs. "build custom pipelines": Don't build foundation models or heavy platforms. Do build thin orchestration, domain workflows, verification layers, skills | "AI wildly" vs. security/privacy constraints: Overuse for low-risk/personal tasks; use enterprise/local/sandboxed patterns for sensitive work | "Human-in-the-loop" vs. autonomous agents: Exception routing: automate routine cases, route edge cases and disagreements to humans | Live demos vs. simulated demos: Live only if task completes in under 10 minutes; simulate otherwise | "Hallucinations as feature" vs. eliminate hallucinations: Feature for ideation; eliminated for operations/facts/regulated outputs | "AI can do health/finance better than experts" vs. responsibility: AI supports preparation and second opinions; decisions need qualified human accountability | 10 principles 1. The scarce skill is not doing the work; it's choosing the work, feeding the agent context, and verifying the result. 2. Prompts, skills, tests, and context files are assets. Code is a byproduct. 3. Use AI wildly in low-risk contexts; use it rigorously in high-risk contexts. 4. For facts and money, make AI produce evidence. For logic, make it produce code. For operations, make it produce audit trails. 5. LLMs hallucinate, but code doesn't. When correctness matters, make AI write and run code. 6. Treat demos as imagination accelerators — show what's now possible before arguing about architecture. 7. Don't sell software if the client can regenerate it tomorrow. Sell outcomes, accountability, and verification. 8. AI training should be a game of doing, breaking, checking, and recovering — not a lecture about tools. 9. Every repeated AI success should become a reusable skill. Every failure is training data if you preserve the prompt, output, rejection reason, and fix. 10. The risk today is not just hallucination; it's underuse, insecure overuse, and unverified scale. Suggested structural reorganization Your current document answers: "What tips should I follow?" Your actual advice answers: "How do I operate in the agent era?" Suggested new top-level structure: 1. Start here — AI is a new operating layer; use it 50 times/day; ask it first, verify consequential outputs 2. Personal habits — voice, interview-me, emotions as entry points, digital exhaust, learning by play 3. Prompting and context — outcome-first, sycophancy defense, multiple outputs, style vocabulary 4. Agentic workspaces — project folders, AGENTS.md, skills.md, prompts.md, Git, Docker 5. Coding with AI — vibe code safely; code is disposable; tests/prompts are assets; Playwright; failing tests first 6. Verification and trust — citations, code execution, golden sets, multi-model checks, exception routing 7. Data and privacy — consumer vs. enterprise vs. API vs. local; least-privilege access patterns 8. AI adoption in organizations — visible leadership use, behavior tracking, games/CTFs, power users, incentive mapping 9. Demos, POCs, and business value — prototype in hours; synthetic data; Hollywood-set demos; sell outcomes 10. Skills in the AI era — grow/shrink/preserve taxonomy (updated) 11. Education — assess process not output; AI-proof questions; monitor the messy middle 12. Business models — software depreciates; skills/trust/verification appreciate; thin orchestration not heavy platforms

AI advice for teams


Based on GDPval benchmarks showing AI agents outperform experts in niche tasks, I 'hire' Claude for roles like relationship architect, diplomat, and epistemologist. These high-leverage prompts help me manage personal networks, stress-test beliefs, and curate my aesthetic taste.

GDPval is a benchmark that compares how well AI does (vs experts without AI) on useful real-world tasks. In several areas, the agents outperform experts. For example, AI beats personal financial advisors, but not accountants and auditors. So I used ChatGPT / Claude to decide where to invest, but am having an accountant file my taxes. That's a high leverage activity, especially since I might not have hired a personal financial advisor by default, and ChatGPT is certainly better than me (I'm not an expert) at personal financial advice. Financial management is just one aspect of life. There are several. I don't hire professionals in many areas where I'm not an expert, so hiring AI agents here is almost a no-brainer. Doctor: Symptom triage, report analysis, drug check, health planning. Financial advisor: Budgeting, investing, tax saving. Lawyer: Contract review (rental, employment, ...), disputes (claims, employment, property), planning (will, power of attorney), legal compliance. Real-estate broker: Property search, lease negotiation, regulatory compliance. Editor: Presentations, documents, emails, code. Investigator: Client, vendor, partner, competitor, consultant, product. Teacher: Skill development, test prep, concept learning, project guidance. Counselor: Mental health, relationship, career, life coaching. But these are the more obvious ones. I had Claude list what AI agents can be "hired" of some of the less obvious but high-leverage "hires" and what I could ask them: Philosopher. Why does this feel uncomfortable? What do these choices reveal about my implicit values? AI may cut jobs but also improve lives. Help me resolve this. Play devil's advocate from three ethical frameworks. Historian. Generate structured questions to ask parents/elders. Here's a transcript. Extract themes and suggest follow-up questions. Connect this family story to its broader historical context. Turn these fragmented stories into a coherent narrative. Relationship architect. Brief me on my history with this person before I meet them. I haven't contacted X in months. Draft a warm reconnection message. Who in my network could help me reach [goal]? What's the warmest path? What patterns do you notice in the relationships I find energizing? Scenario planner. Create three 2030 scenarios for [domain]. Stress-test my current strategy against each. What robust moves work across all scenarios? What early warning indicators should I watch for? I'm choosing between X and Y. What would I need to believe for each to be correct? Epistemologist. What are my load-bearing beliefs? Which haven't I stress-tested recently? Steelman the strongest case against my view on [topic]. Here's a prediction I made. Track it. What's my calibration like? Where might I have galaxy-brained myself into an unusual position? Diplomat. I'm meeting [context]. What unwritten expectations might I miss? Review this email. Am I being appropriately [direct/indirect] for this culture? What mistakes do Indians typically make in [context]? The meeting felt off. Here's what happened. What did I miss? Taste curator. I liked [X, Y, Z] but not [A, B]. What does this reveal about my aesthetic? What's adjacent to my current taste that would stretch me without losing me? I want to develop taste in [domain]. What's the learning path? What do I experience first? My taste feels derivative. What would make it more authentically mine? Recommend [books/films/music] that would give me vocabulary for [aesthetic/emotion/idea]. I'm designing [space/event/gift]. What references should I draw from given my taste? My taste in [domain] is developed. My taste in [other domain] is naive. Bridge them. Rhetorician. Analyze this transcript. What's my default argumentation style? Its blind spots? I need to convince [skeptical audience] of X. What's the optimal structure? Help me turn this observation into a memorable, quotable framework. Steelman their likely objections and give me responses. Archivist. What have I previously thought about [topic]? This new idea connects to something—find the link in my past work. I've written about X and Z separately. Synthesize them. What gaps exist in my thinking on [domain]? Liturgist. Design a family ritual for [transition] that fits our values. Create a weekly reflection practice for our family. We lost [person]. Design a remembrance practice that feels genuine. I want to mark [milestone] meaningfully, not performatively. How? Activist. I care about [issue]. Map the power structure. Who actually decides? What's the smallest intervention with the largest leverage on this system? Who are unlikely allies? What would make opponents neutral? I have [resources/reach]. What's my highest-impact move? Craft a narrative frame that makes [change] feel inevitable, not radical. What's the history of successful change in similar domains? What worked? Intelligence agent. What weak signals should I monitor for [risk/opportunity]? Here's what [competitor/market] did this quarter. What does it reveal about their strategy? What am I not seeing because of my position? Where are my blind spots? This seems like noise. Is there a pattern I'm missing? Red team this: How could [scenario] hurt me? What would I not see coming? Verify this claim. What would make it false? What's the source quality? Bodyguard. Audit my digital footprint. What's publicly available that shouldn't be? I'm traveling to [place]. What's the threat profile? What precautions matter? Review this [email/message/request]. Is this social engineering? What's my current weakest security link—physical, digital, financial? Someone determined wants to harm me. What's their easiest path? How do I close it? Design a security protocol for [context: travel/home/data] that I'll actually follow. Lab assistant. I want to test whether [intervention] affects [outcome]. Design an N=1 experiment. Here's two weeks of data. What's the signal? What's noise? I think X causes Y in my life. What confounders should I control for? This experiment failed. Was it the hypothesis or the method? What's the minimum viable test before I commit to [major change]? I've tried [interventions]. Synthesize: what actually works for me? AI Advisory Cabinet LinkedIn

AI agents to hire


I tracked my coding agent usage with ccusage and found $20 subscriptions for Claude and ChatGPT offer massive ROI, sometimes yielding over $400 in API value. Individual subscriptions remain a bargain compared to the shift toward token-based enterprise pricing.

I ran npx -y ccusage monthly --compact to get the following break-up of my AI coding agent costs. | Month | Codex | Claude | | ------- | -------: | ------: | | 2025-09 | $37.47 | $2.29 | | 2025-10 | $106.79 | $9.13 | | 2025-11 | $100.35 | $14.24 | | 2025-12 | $240.69 | $24.88 | | 2026-01 | $100.89 | $20.28 | | 2026-02 | $323.21 | $29.46 | | 2026-03 | $1996.32 | $134.87 | | 2026-04 | $401.36 | $47.07 | | 2026-05 | $378.20 | $45.13 | This shows the ROI of my $20 subscriptions to each. I get $35 worth of API calls for my $20 Claude Pro subscription and $400 of API calls for my $20 ChatGPT Plus subscription (on top of my ChatGPT chats.) I end up using Codex a lot more - partly because it's a bit more diligent, but mostly because it's a lot cheaper. Clearly, subscriptions are good deal for individuals. Codex, especially. This may not be true for corporates. Simon Willison says that Anthropic and OpenAI both changed enterprise pricing to align with token prices. That means the cost of enterprise AI security is 2-20 times their token budget - which is growing rapidly. BTW, my moment of AI psychosis was in March 2026. The coding agents had increased their limits and I was tokenmaxxing. I'm far from that limit today, but the symptoms linger. (async function () { const rows = [ { month: '2025-09', claude: 2.29, codex: 37.47 }, { month: '2025-10', claude: 9.13, codex: 106.79 }, { month: '2025-11', claude: 14.24, codex: 100.35 }, { month: '2025-12', claude: 24.88, codex: 240.69 }, { month: '2026-01', claude: 20.28, codex: 100.89 }, { month: '2026-02', claude: 29.46, codex: 323.21 }, { month: '2026-03', claude: 134.87, codex: 1996.32 }, { month: '2026-04', claude: 47.07, codex: 401.36 }, { month: '2026-05', claude: 45.13, codex: 378.20 } ]; const theme = { ink: '#231f20', muted: '#6b625c', grid: 'rgba(35, 31, 32, 0.11)', axis: 'rgba(35, 31, 32, 0.22)', claude: '#b96d3a', codex: '#2d5f87', tooltip: 'rgba(35, 31, 32, 0.94)' }; const canvas = document.getElementById('ai-coding-agent-usage'); Object.assign(canvas.style, { display: 'block', width: '100%', height: '100%', minHeight: '480px' }); function loadChartJs() { if (window.Chart) return Promise.resolve(); return new Promise((resolve, reject) => { const script = document.createElement('script'); script.src = 'https://cdn.jsdelivr.net/npm/chart.js@4.4.1/dist/chart.umd.min.js'; script.onload = resolve; script.onerror = reject; document.head.appendChild(script); }); } await loadChartJs(); const usd = new Intl.NumberFormat('en-US', { style: 'currency', currency: 'USD', maximumFractionDigits: 2 }); const compactUsd = new Intl.NumberFormat('en-US', { style: 'currency', currency: 'USD', notation: 'compact', maximumFractionDigits: 1 }); Chart.defaults.font.family = document.body.style.fontFamily; Chart.defaults.color = theme.muted; new Chart(canvas, { type: 'line', data: { labels: rows.map(d => d.month), datasets: [ { label: 'Claude', data: rows.map(d => d.claude), borderColor: theme.claude, backgroundColor: theme.claude, pointBackgroundColor: '#ffffff', pointBorderColor: theme.claude, pointBorderWidth: 2.5, pointRadius: 4, pointHoverRadius: 7, borderWidth: 3, tension: 0.22 }, { label: 'Codex', data: rows.map(d => d.codex), borderColor: theme.codex, backgroundColor: theme.codex, pointBackgroundColor: '#ffffff', pointBorderColor: theme.codex, pointBorderWidth: 2.5, pointRadius: 4, pointHoverRadius: 7, borderWidth: 3, tension: 0.22 } ] }, options: { responsive: true, maintainAspectRatio: false, interaction: { mode: 'index', intersect: false }, layout: { padding: { top: 12, right: 18, bottom: 4, left: 8 } }, plugins: { legend: { position: 'bottom', labels: { usePointStyle: true, pointStyle: 'circle', boxWidth: 8, boxHeight: 8, padding: 22, color: theme.muted, font: { size: 13, weight: '650' } } }, tooltip: { enabled: true, mode: 'index', intersect: false, backgroundColor: theme.tooltip, bodyFont: { size: 13, weight: '650' }, padding: 13, displayColors: true, callbacks: { title: items => items[0].label, label: item => ${item.dataset.label}: ${usd.format(item.parsed.y)}, afterBody: items => { const i = items[0].dataIndex; const total = rows[i].claude + rows[i].codex; return Combined: ${usd.format(total)}; } } } }, scales: { x: { grid: { color: 'rgba(35,31,32,0.07)', drawTicks: false }, border: { color: theme.axis }, ticks: { maxRotation: 0, autoSkip: false, color: theme.muted, font: { size: 12 } } }, y: { beginAtZero: true, suggestedMax: 2200, grid: { color: theme.grid }, border: { display: false }, ticks: { padding: 8, color: theme.muted, callback: value => value >= 1000 ? compactUsd.format(value) : '$' + value }, title: { display: true, text: 'Cost (USD)', color: theme.muted, font: { size: 12, weight: '650' } } } } } }); })(); 14 Jun 2026: SemiAnalysis tested and found that a $20 Claude Pro gives you $400 and a $100 Claude Max gives you $2,000 of API usage. For ChatGPT, the numbers are $700 and $3,500.

AI Coding Agent Subscription ROI


Prompting an AI to reason like an expert often improves the usefulness of its response by forcing attention onto patterns, questions, and failure modes that novices miss.

My current favorite prompt fragment is the expert lens: When I add this to my questions, if feels a lot smarter. I have no idea if that's because it really improves the replies, or because it prefixes them with "What an expert would do is..." which makes it sound smarter, or because it just makes me feel good about adding this prompt. Irrespective of the reason, the effect is that I pay more attention to it. Increasingly, that's the bottleneck - the most important part. If I'm attentive, I learn / act. At least for that, this prompt is useful to me. But I also believe it actually improves the quality of replies.

AI Expert Lens


AI tools can extract, translate, and reinterpret film dialogue quickly enough to make cinema analysis more accessible across languages.

I was watching Vasu>) while Codex-ing and came across this dialogue: Here's the dialogue, recorded via ffmpeg, transcribed via AI Studio: మీ నాన్న మిమ్మల్ని పోలీస్ ఆఫీసర్ అవ్వమని అడిగితే అయ్యారా? మీకు ఇష్టం కాబట్టి అయ్యారు.\ సచిన్ టెండూల్కర్ ని ఇంజనీర్ ని చేయాలని వాళ్ళ నాన్న అనుకుని ఉంటే, ఇండియా ఒక గొప్ప క్రికెటర్ ని మిస్ అయ్యేది.\ విశ్వనాథ్ ఆనంద్ ని డాక్టర్ ని చేయాలని వాళ్ళ అమ్మ కోరుకుని ఉంటే, ఇండియాకి ఓ గ్రాండ్ మాస్టర్ ఉండేవాడు కాదు. Translation: Did you become a Police Officer because your father asked you to? You became one because you liked it.\ If Sachin Tendulkar's father had thought of making him an engineer, India would have missed out on a great cricketer.\ If Viswanathan Anand's mother had wished to make him a doctor, India wouldn't have had a Grandmaster. But here's the thing - these aren't the best examples. A better one would be were a successful parent allowed their successful child to pursue their passion. So, Gemini to the rescue: List famous Indians whose children have become famous in very different fields... ... and here goes: మీ నాన్న మిమ్మల్ని పోలీస్ ఆఫీసర్ అవ్వమని అడిగితే అయ్యారా? మీకు ఇష్టం కాబట్టి అయ్యారు.\ ప్రకాష్ పదుకొనె తన కూతురు దీపికాని బ్యాడ్మింటన్ ప్లేయర్ ని చేయాలని అనుకుని ఉంటే, ఇండియా ఒక గొప్ప నటిని మిస్ అయ్యేది.\ ఆర్. మాధవన్ తన కొడుకు వేదాంత్ ని నటుడిని చేయాలని కోరుకుని ఉంటే, ఇండియాకి ఒక ఛాంపియన్ స్విమ్మర్ ఉండేవాడు కాదు. Translation: Did you become a Police Officer because your father asked you to? You became one because you liked it.\ If Prakash Padukone had thought of making his daughter Deepika a badminton player, India would have missed out on a great actress.\ If R. Madhavan had wished to make his son Vedaant an actor, India wouldn't have had a champion swimmer. Not that dialogue writers aren't using AI, but if you know anyone who needs AI help, let me know. The fact that I can't read a word of Telugu makes no difference! 🙂

AI for film dialogues


The strongest use of AI in software development is not isolated prompting but embedding models across the entire development loop from discovery to deployment and postmortem learning.

I was at a panel on AI in SDLC at PyConf. Here's the summary of my advice: Process Make AI your entire SDLC loop. Record client calls, feed them to a coding agent to directly build & deploy the solution. Record your prompts, run post-mortems, and distill them into SKILLS.md files for reuse. Prompting Ask AI to make output more reviewable. Don't waste time reviewing unclear output. Prefer directional feedback (feeling, emotion, intent) over implementational. Also give AI freedom to do things its way. Learn from that - you'll be surprised. Learning Prefer interns / outsiders over experts. They don't slow the process with preconceptions and leverage AI better. Stop learning what AI does well. Learn what AI fails at - using AI. Keep re-assessing these. Adoption Developer using AI are still accountable for their code. (Agents might become accountable in the future.) Start with new projects: less competition, fewer preconceptions, lower risk. Start in domains where failure is OK, rather than making AI safe enough for high-risk domains. Create safe spaces where hallucinations don't matter and run experiments there to learn what AI can do. Plan for where AI'll be a year later. It's growing very rapidly. The full details of the panel discussion are at Who Owns the Commit?

AI in SDLC at PyConf


I use Google Edge Gallery with Gemma-4-E2B-it to run LLMs locally on flights. It serves as a handy offline reference for identifying objects, defining niche terms, and brainstorming ideas when internet is unavailable.

I love that I get uninterrupted 4-16 hours on flights, which I mostly use to write future prompts and read past AI responses. I do miss AI on flights. But after installing Google Edge Gallery with Gemma-4-E2B-it (2.5GB) that runs on my mobile, I've solved a few practical problems. For example: I took a picture of a dish they served and asked: "Is this vegetarian?" (It was.) I asked, "Comics have text in panels, often written at the top in a box. Not the speech bubbles. It's like a narrator or voice over. What are they called?" (Caption boxes.) "Summarize The Unbearable Lightness of Being. Why is it famous?" (Thoughtful, well-written novel on the choice vs commitment tradeoff.) It's not a very smart model. It's a bit slow. Transcription is average. It doesn't run in the background. Only one chat at a time. No internet search, etc. But it's a good reference to have. Almost a Wikipedia I can talk to.\ It's a good ideator to have. I can brainstorm. (Hallucination is a feature, not bug.) I'm sure I'll find more uses.

AI on flights


AI is much better at agreeing about a person’s chat-based patterns than at inferring meaningful truths from palm photos, even when multiple agents seem to converge.

I shared a photo of my right hand with popular AI agents and asked for a detailed palmistry reading. Apply all the principles of palmistry and read my hand. Be exhaustive and cross-check against the different schools of palmistry. Tell me what they consistently agree on and what they are differing on. I was more interested in how much they agree with each other than with reality. So I shared all three readings and asked Claude: What are things all three agents clearly agree on? List these. What are things they differ on? List as a comparison table. Here's what they agree on. The first is probably right. I'm not sure about the rest. 1. ✅ You think in patterns and connections, not just logic. Your mind works by synthesizing ideas across domains rather than following straight lines. (Oh, yes. My mind's drunk.) 2. ❓ You have strong physical energy and bounce back well from illness or setbacks. (Do I? In 2023, I spent a year "bouncing back". And I still haven't bounced back from 26 Oct 2014.) 3. ❌ You feel deeply, but don't show it loudly. Your emotions are genuine and loyal — just not dramatic. (I'm actually shallow. I show what I feel and it's not much.) 4. ❓ You built your own path. No preset direction was handed to you; your sense of purpose developed through your own choices. (Um... this is partly true for everyone?) 5. ❓ You're not a blunt-action person. You process experience through interpretation and nuance before acting. (Maybe? Sometimes I'm impulsive. Sometimes not.) 6. ❓ There's more going on inside than people around you typically see. (Um... this is partly true for everyone?) Clearly, AI's analysis of my chats is much better than of my palm. You can read the full readings here.

AI Palmistry


A comparative review of university AI policies suggests that institutions vary widely in how thoughtfully they govern AI use, and some prominent universities lag more than expected.

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

AI policies across universities


Careful ffmpeg tuning can compress short WEBM videos far more aggressively than online tools while preserving nearly all of the visual quality that matters.

I recorded a short screen cast of a demo I built. It was 900KB - way too large to publish as a thumbnail. So I asked ChatGPT: What's the best equivalent of squoosh.app for WEBM compression? I'm looking for a free modern high-quality online video compressor. There are a few, and they compressed it to a third of its size, but 300KB is still too large. So I attached the original and asked: I am compressing screenshots like this. They're often not large. I don't mind cropping the edges by a few pixels to make it a multiple 2, 4, or 8 if that'll help. I certainly am OK with a lower frame rate. I'd like an image quality that a human eye can just SLIGHTLY detect as worse than the original, but only VERY SLIGHTLY. What's the best way of using ffmpeg or similar tools to compress such a file? Think like an expert: What would an expert in this field check that beginners would miss? What patterns would an expert in this field recognize that beginners would miss? In this context, what questions would an expert ask that a beginner would not know to? If this goes wrong, what are the most likely reasons? ... and list the best (diverse, testing different hypotheses) compression command options. Run them on this video and let me download the resulting videos for visual comparison. Interview me. Give me a list of questions that I can easily answer by looking at the videos and I'll share those with you to help you decide the best compression command for me. This leverages 3 tricks. 1. These are online coding agents. So they can write and run code. 2. The "Think like an expert" prompt is my new "Think step by step" prompt and works quite well. 3. The "Interview me" prompt is another powerful one that helps me apply preferences -- and develop taste. Claude did a good job of showing different versions and compressing it, but as expected, ChatGPT was the obsessive perfectionist. It gave me a huge set of variations of: Files sizes Compression formats (VP9: more compatible vs AV1: better compression) Quality settings (CRF) Frame rates (FPS) Color formats (YUV420p vs YUV444p) Compression effort (presets) etc. ... and asked me to compare between them, like these: crf: 45 fps: 15 (93K) crf: 50 fps: 15 (69K) crf: 55 fps: 5 (23K) The final result is this script: ... which does the following: Crops the video to a multiple of 2 (which is required for some compression formats) Sets the frame rate to a lower value (which reduces file size) Uses the AV1 codec (which has better compression than VP9) Sets the CRF (Constant Rate Factor) to a value that balances quality and file size Sets the pixel format to YUV420p (which is more compatible with players) Disables audio (which is not needed for a screencast) I realized that for the resolution I'll likely see this at, very low frame rates (5 fps) and poor compressions (CRF 55) are good enough. My original video was 912KB. The smallest video that looks good enough for me is 23 KB. That's almost a 40x compression - small enough to publish in my data story. Things I'm taking away: Use AI to discover the best configurations for your tool Interview yourself to apply preferences and develop taste

AI video compression


I highlight how proactive service—remembering a delayed tea order during turbulence and checking back with skipped passengers by name—elevates customer experience. It shows that attentiveness and memory are powerful tools in hospitality.

On the Indigo 1163 from Mumbai to Singapore, an air hostess was serving food, which they had to pause just before me because of turbulence. In such cases, I've sometimes been skipped in the past, and I usually stay alert for when service resumes. This time, I wasn't paying attention. But the air hostess walked over right on cue and asked, "Mr. Subramanian, have we served you yet?" She didn't know. That's normal.\ But she checked. That's not.\ (These sentences are human written, on a flight. But if Claude had written the above, I would have rewritten it because it sounds too much like AI.) When she asked for my beverage, I picked "Masala chai". (The Indigo masala tea is lovely. Or, as my daughter puts it, "Appa, you're just getting old!") "We're not serving hot beverages now sir, but I can get it after the seat belt signs go off." Every time I've heard this, I tell them "I'll have buttermilk, please," simply because I'm worried they'll forget. This time, I said, "I'll wait." Before I finished my sandwich (and even though the seat belt signs weren't off, but the turbulence subsided), she placed the tea in my hands. I had my laptop open and was eating my sandwich without the tray table open, so an ultra-thoughtful air hostess might have opened the tray table and placed the tray there, but an ultra-thoughtful neighbor did that for me. (This happened on my onward flight as well. Either air-neighbors are getting friendlier or I'm looking more helpless than I thought.) I went up to use the toilet, but she suggested that I not use it during turbulence - gently enough that if I felt the need, I could tell her it was urgent. Fifteen minutes later, the seat belt signs were still on and I walked over again. She didn't stop me - she remembered. Another air hostess was cleaning inside, so she suggested I use the toilet at the rear. She called them up and let them know to let me use it, though the seat belt signs were on. Maybe because this was an international flight. But whatever the reason... Thank you, Sharadha. Update: 2 Jul 2026. On my flight from Singapore to Bangalore, the air hostess served me a sandwich. I ate it. She came back and offered me a biryani. "But I already had a sandwich." "Oh, sorry sir, I served you wrong. Could you please have the biryani anyway?" So, yeah, while I preferred the sandwich to biryani and have no complaints, some air hostesses do better than others.

An excellent air hostess


Coding agents can inspect repository-level cloud billing and uncover practical patterns for driving recurring infrastructure costs close to zero.

I have a GitHub Education since I teach at IITM. But if I switch back to a free account, how much would I need to pay? I asked Codex (5.3, xhigh): My GITHUBTOKEN is in .env. Go through my GitHub billing. Ignore the $100 sponsorships I make. Other than that, my current metered usage is $6.71 for Feb 2026 (which is included in my billing plan). $0.35 comes from sanand0/exam and $0.34 from sanand0/blog and so on. That's coming mostly from "Actions Linux", occasionally "Actions Storage". Pick a few of the top repos and tell me what I should do to make the cost zero - or reduce the cost as much as possible. See if there's a pattern across repos. Document all of your findings in analysis.md and continue to append new findings in this file, summarizing my request as a heading followed by your response. My aim is to stay well below the 2,000 free actions minutes/month - which I'm already below. But still, I want to optimize a bit... Tell me What my billing would be under a free account What repos and what activity are the biggest risks for hitting the free limit After half an hour of my watching a movie, it told me (in great detail - see the details below) that: My billing is way below the free limit. I should watch out for GitHub Copilot, frequent CI runs, scheduled jobs. But the more interesting thing for me is how easy cloud optimization has become with coding agents. It makes curiosity cheaper. I wouldn't have sat and written the scripts to figure out where $6.71 went. But coding agents made micro-audits practical (and with clever use of jaq, csvq, and duckdb. If you can get an answer by just from a question, we'd use it like Google - to answer ad hoc questions. It challenges common sense. I assumed caching was good for speed and compute. But Codex's analysis of my sanand0/blog actions pointed out that dropping a job's time from 1.2 minutes to 0.7 minutes doesn't change the 2-minute billed floor! Also, the 114MB cache increased storage costs. We can test optimizations without assuming. It exposes blind spots. Turns out that a big chunk of the cost of my (private) TDS exam repo was dynamic/copilot-pull-request-reviewer - a GitHub workflow triggered by using Copilot. I also have a big chunk of "legacy" GitHub Pages on older repos that add to cost because of failures and retries. We can find these invisible leeches draining cost without knowing what to ask for. It self-corrects. Codex hit a deprecated GitHub billing endpoint (HTTP 410). It curled the GitHub docs, found the new endpoint, and rewrote its query. It made a mistake with csvq, read the help, and switched to duckdb for complex median calculations. That self-correction and learning means that it can work while you sleep. It fixes, not just finds. Codex rewrote my blog deployment script to use a single job instead of two, to save compute time. I just copied andd deployed it. It went from researcher to advisor. That's a short step from executor, next. If analyzing cloud cost is this easy, Cloud FinOps companies face a risk. AWS, Azure, and GCP will simple embed Cloud FinOps into their services. Cloud spend won't fall -- we'll just use more compute for the same budget. The big Cloud FinOps companies (e.g. IBM, Flexera, Broadcom) are in some trouble. They can't cannibalize their own market. They'll likely shrink and focus on banks & Governments that need a reputable partner. Companies that own the infra data (e.g. DataDog, Snowflake, DataBricks) will likely grow, since only they can access the context for optimization. That'll let them provide better service cheaper, e.g. auto-optimize queries, compute, storage, etc. for you. Pure plays (e.g. CloudZero, Finout) might need to pivot from infra optimization to... something more strategic, I guess. Here's the full (long) response from Codex Data source: GET /users/sanand0/settings/billing/usage using your token in .env. Snapshot time: 2026-02-28T03:24:45Z (UTC). Ignoring sponsorships, Feb 2026 metered usage is: | Product | Gross Amount | | ------------------------- | -----------: | | Copilot Premium Request | $5.05 | | Actions (Linux + Storage) | $1.646844306 | | Git LFS storage | $0.009750857 | | Total | $6.706595163 | Top Actions repos in Feb 2026: | Repo | Total Actions Cost | Linux Cost | Storage Cost | Linux Minutes | | ------------------------------ | -----------------: | -----------: | -----------: | ------------: | | exam | $0.354000000 | $0.354000000 | $0.000000000 | 59 | | blog | $0.337295637 | $0.246000000 | $0.091295637 | 41 | | tools-in-data-science-public | $0.211055072 | $0.204000000 | $0.007055072 | 34 | | tools | $0.174166256 | $0.174000000 | $0.000166256 | 29 | | iss-location | $0.174000000 | $0.174000000 | $0.000000000 | 29 | | imdbscrape | $0.168000000 | $0.168000000 | $0.000000000 | 28 | Cross-repo pattern: Your cost is dominated by minute-floor billing per job and trigger frequency, not pure runtime. Feb run-level pattern (from fetched run/job metadata in top repos): dynamic: 9 runs, 79 billed-like minutes (8.78/run) push: 50 runs, 81 billed-like minutes (1.62/run) schedule: 55 runs, 57 billed-like minutes (1.04/run) Primary cost-reduction levers: 1. Disable Copilot dynamic code-review/agent workflows on repos where not needed (exam, tools were the big dynamic consumers). 2. Restrict Pages deploy workflows to main only (avoid dev/feature branch deploys). 3. Merge 2-job build+deploy workflows into one job where feasible (reduces per-job minute floor). 4. Reduce scheduled frequency for daily cron repos if not needed (iss-location, imdbscrape). 5. For storage, reduce artifact/caches and cleanup stale artifacts. I expanded analysis across all 14 repos that used Linux Actions minutes in Feb 2026 and fetched per-run job details. 1) sanand0/blog: does disabling cache improve costs? Workflow history: Cache was introduced in .github/workflows/deploy.yml commit 4f744fffdeb64aebd09cfc6fd1bb27fd9844842d on 2026-02-20T04:40:39Z. Previous workflow commit: 61392ee781ff77e9914b0a1a68b852f46d0a66e8 on 2026-01-02T03:11:32Z. Observed runs around this change (main + dev only): | Phase | Runs | Avg Active Job Min | Median Active Job Min | Avg Billed-like Min | Median Billed-like Min | | ------------------------------ | ---: | -----------------: | --------------------: | ------------------: | ---------------------: | | Before cache (Jan 2 -> Feb 20) | 62 | 1.192 | 0.900 | 2.194 | 2.0 | | After cache (Feb 20 -> Feb 28) | 6 | 0.744 | 0.817 | 1.667 | 2.0 | Interpretation: No clear billed-minute win from cache: median billed-like is still 2/run in both periods (minute floor + 2 jobs dominates). Cache likely helped runtime modestly (small active-minute decrease), but sample after cache is small (n=6) and confounded by other workflow changes. Storage side: current blog caches are 114 MB. If that stayed all month, rough max cost is about $0.026/month at observed storage unit price; this is only part of blog storage charge ($0.0913). Therefore, disabling cache may save some storage, but the main cost lever is reducing run count and job count, not cache toggles. 2) Better blog optimizations than cache-off High-impact changes: 1. Trigger deploy only on main (Feb had 20 dev deploy runs vs 2 main). 2. Add path filters so non-site changes do not deploy. 3. Use a single deploy job (instead of separate build + deploy) to reduce minute-floor overhead. 4. Keep [skip ci] support. Explicit workflow suggestion file created: /home/vscode/Downloads/github-usage/analysis/blog-cost-optimized-deploy.yml 3) Additional repo pattern findings (expanded set) Feb 2026 Linux-minute repos and event patterns: | Repo | Minutes | Main pattern | | ------------------------------ | ------: | ------------------------------------------------------------------------------ | | exam | 59 | dynamic Copilot runs (Copilot code review, Running Copilot coding agent) | | blog | 41 | push deploy workflow (Deploy Hugo site) | | tools-in-data-science-public | 34 | push deploy + some dynamic pages runs | | iss-location | 29 | daily schedule | | imdbscrape | 28 | daily schedule | | tools | 29 | push deploy + dynamic Copilot review | | pyoppe | 10 | dynamic pages build and deployment (legacy Pages), includes retries/failures | | schoolai | 6 | dynamic pages build and deployment (legacy Pages) | | image-filters | 6 | dynamic pages build and deployment (legacy Pages) | | llmpricing | 3 | dynamic pages build and deployment (legacy Pages) | Important hidden pattern: pyoppe, schoolai, image-filters, llmpricing use Pages buildtype = legacy and incur dynamic pages runs. These runs are tiny but still billed at minute floor and can multiply via retries/failures. 4) What your billing would be under a free account Assumptions for this estimate: Focus on Actions minutes/usage behavior from your Feb 2026 data. Sponsorships still excluded. Copilot Premium Request billing is separate from Actions minutes. Your Feb Actions usage: All repos Linux minutes: 258 Private-repo Linux minutes (exam): 59 Estimate: Under a 2,000-minute free limit, both 258 and 59 are far below the cap. So for Actions minutes specifically, you would still be $0 overage this month. 5) Biggest risks for hitting the free limit If your objective is avoiding 2,000-minute overflow, the highest-risk activity patterns are: 1. Dynamic Copilot runs on private repos (high minutes per run). Example observed: exam dynamic runs consumed 59 minutes from 5 runs. 2. Many frequent push deploys on multi-job workflows (2-minute floor per push is common). Example: blog and tools-in-data-science-public. 3. Daily scheduled jobs across many repos. One daily repo is 30 min/month baseline; many such repos accumulate predictably. 4. Legacy Pages dynamic builds with retries/failures. Small runs, but multiplicative retriggers add up. Rule-of-thumb risk math: 1 daily scheduled workflow = 30 min/month. 1 two-job push deploy = 2 min/run. 1 Copilot dynamic review run can be 8-15 min/run.

AnalAIzing Cloud Costs


I’m attending Arvind Satyanarayan’s talk at VizChitra 2026 to discuss how visualization grammars like Vega-Lite and GoFish evolve alongside LLMs, serving as structured frameworks for AI coding agents to generate precise, scalable charts.

On Sat 4 July at Bangalore, Arvind Satyanarayan is speaking at VizChitra 2026 - a talk I'm keenly looking forward to. I've been following Arvind's work since Vega-Lite. It's a grammar of graphics - something that makes data visualizations (charts) more structured. I tried switching to it our default at Gramener - but most felt it was too much to learn (they already knew Excel/Power BI) or too limiting (D3 can do more). With AI coding agents, the learning is less relevant - AI will write the code. So, do grammars have less relevance? Arvind's talk is about how they might actually be more important - maybe like a structured way to prompt. Quite looking forward to this. Do attend if you're interested in the cutting edge of AI-driven chart generation. Here are some questions I hope to ask Arvind: When someone hits the limit of a visualization grammar, but they know exactly what they want, what "escape" or extension mechanisms have you found most intuitive and useful? With GoFish, you re-built a grammar rather than updating one. Why? I mean, what smells or gaps in existing grammars made you decide that way? When YOU find a grammar limiting you, what tool(s) do you turn to today - and how do you work with them?

Arvind Satyanarayan talk at VizChitra 2026


No-code AI app builders can generate decent interfaces quickly, but they may hide weak implementations behind repeated model calls instead of real software.

I tried vibe-code a CSV to colored HTML table converter using this prompt. Create a tool that can convert pasted tables into colored HTML tables. Allow the user to paste a CSV or tab-delimited or pipe-delimited table. ... Create an HTML table that has minimal styling. ... Add a button to copy just the HTML to the clipboard. Codex built this. Which is perfect. AWS Partyrock built this. Which is a joke, because it didn't write the code to do the conversion. It uses an LLM every time. The good part, though, is that it builds UI components that you can edit and move around. I think it builds a domain specific language with components with properties. You can edit the properties to change the app. This is a good way to build robust apps using unreliable LLMs. But it has less flexibility and richness. With LLMs becoming more and more reliable, I think generating code directly is better, and this approach will fade or be used in niches.

AWS PartyRock


I explore how games like Baba Is You, Chess, and Mini Metro build mental muscles for the AI era. I found that manipulating in-game rules teaches meta-level thinking and helps me view complex systems as changeable.

I have this feeling that the skills we need for the AI era might be found in video games. (Actually, no. I just want an excuse to play games. Self-improvement is a bonus.) I asked the usual LLMs (Claude, Gemini, ChatGPT): What are mobile phone games that have been consistently proven to be educational, instructive or skill/mental muscle building on the one hand, and also entertaining, engaging and popular on the other? ... followed by: In the AI era what skills will become more important and what skills less? For the more important skills, which games teach them most effectively and engagingly? The only familiar game on all three's list was Chess. Specifically, analyzing moves teaches how to assess quality (with Lichess' studies feature). Useful to assess AI quality. It also mentioned: Mini Metro: Teaches bottleneck identification and network optimization. Useful for organizational AI adoption challenges. Human Resource Machine: Teaches assembly (programming language) through puzzle design. Useful for AI workflows. Polytopia. 30 min strategy games that teach resource management, strategic positioning, optimization. The Room series. 3D mechanical puzzles where learning one mechanism helps solve others. Monument Valley. Navigate Escher-style architecture where rotating the world changes the path. Challenges our assumption that 3D space is Euclidean. But the strongest recommendation was: Baba Is You. It's THE game for meta-level thinking. You literally reprogram the rules. Teaches seeing systems as changeable, not fixed. Exceptional for the "how do we restructure this problem?" thinking AI requires. So, for the last few days, I've been playing Baba is You in my spare time. The "rules" are literally written on the board. When these three blocks "FLAG", "IS", and "WIN" are placed next to each other, touching the flag wins. But if I push any block away, the rule breaks. If I push the the "WALL" block to replace the "FLAG" block, touching the wall wins. So, the trick is to re-arrange the rules to win. I love doing that! In Everything Bad Is Good For You, Steven Johnson argues that pop culture (video games, manga, soap operas, game shows, etc.) are becoming more and more complex and provide a dopamine kick from problem-solving. Pop culture does have bad side effects (screen-time, addiction, mis-information), and we're not sure whether strategy games help with memory and planning or it's the other way around, but I do believe that well-designed games can help us build useful skills. So, more Baba Is You for me!

Baba Is You


I bypassed AI detectors with high confidence by reading LLM-generated drafts aloud and transcribing them, naturally replacing robotic AI writing habits with conversational, human speech patterns.

Ranjeeta asked me for an article for Built In. I went straight to ChatGPT and said: ... and gave it all related information. Here is ChatGPT's article. The article itself was pretty good, content-wise, but it wasn't exactly in my style and I iterated once. Still... not exactly there. One of the criteria is that "Final drafts must score ‘human-written’ or less than 20% on ZeroGPT and Pangram." ZeroGPT is easy to fool but Pangram is harder. Pangram said: AI Generated. 100% of this text is AI Generated "No Juniors, No Experts? Ankor runs a ..." (384 words) - We believe the segment is fully AI generated. Confidence: High. "In March 2026, I pointed a coding ..." (383 words) - We believe the segment is fully AI generated. Confidence: High. "Make it generate rare failures. Make it ..." (196 words) - We believe the segment is fully AI generated. Confidence: High. Also: "AI-Generated indicates text produced by an AI system with minimal human input or revision." Sigh... Just to cross-check, I pasted a 2021 blog post and Pangram said: Human Written. 100% of this text is Human Written. "Picking gifts is hard. Gift-giving feels ..." (331 words) - The segment is fully human-written. Confidence: Low. Well, maybe Pangram does do a good job of detecting AI-generated text. It managed to by-pass my carefully crafted writing style which includes several LLM smell avoidance techniques. Next attempt: I checked if Claude's new Sonnet 5 could do a better job. Here is Claude's article. Though the content was, again, spot-on, the style had clear LLM smells despite all my instructions. Pangram said: AI Generated. 100% of this text is AI Generated "No Juniors, No Experts? AI is cutting ..." (359 words) - We believe the segment is fully AI generated. Confidence: High. "He's three times faster than someone ..." (132 words) - We believe the segment is fully AI generated. Confidence: High. At this point, inspiration struck. (Actually, not quite. I had this idea for a few days, maybe weeks, ago... but the opportunity struck.) I opened the Claude article on the left, ChatGPT on the right (just to transcribe, nothing else - ChatGPT has the best transcription right now), and read out the article. Not word-for-word, but in my style. For example: | Claude said ... | I read it out as ... | | ---------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------- | | AI is cutting the entry-level jobs that used to train tomorrow's architects. | These days, AI is reducing the number of entry-level jobs that we have. | | AI makes senior architects more productive and cuts the need for junior engineers. | He said, AI makes senior architects more productive and reduces the need for junior engineers. | | The data backs his worry. | The data supports his concern. | | A free phone app now beats any grandmaster. | Any free phone application today can beat every grandmaster. | | A broken deployment, a weak first draft: you catch it and move on. | Broken deployments and prototypes and quick POCs aren't so important that you can't live with them. | The beauty of this is that it I was able to read it out almost live. Just read a sentence and narrate it like I'm talking to an audience. I'm used to doing this. Here is the version I read out. Pangram said: Human Written. 100% of this text is Human Written. "No Juniors, No Experts? These days..." (381 words) - The segment is fully human-written. Confidence: High. The beauty of it is that it has High confidence - even more so than my earlier blog post which was truly human written. Still, good to know that there's an efficient way to use AI that doesn't smell like AI.

Beating AI detectors by reading aloud