S Anand

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

This week, I learned: Kamakoti: “Entry (to the course) is relatively easy but the exit is extremely hard”. Generalizing, quality is determined by the exit criteria; loosening entry criteria is just openness / diversity. The Hindu “Once I have a persistent system that I pay to keep thinking, learning, and acting 24/7, I think that will decisively look like AGI.” - Dan Shipper AI has expert-level capabilities in many (increasing) tasks #ForNow. If your edge is OUTSIDE of those, use AI for other tasks you couldn’t do before, insourcing or expanding horizontally. But your edge may be short-term - so move upstream / specialize. Your competitors’ edge may be short-term, too - so plan to attack. Analyzing Anthropic Economic Survey, it looks like people in rich countries are asking Claude for advice (explain this spreadsheet) while poor countries are asking Claude for output (build this website) #ForNow. Maybe because rich users already have tools / people that create output for them? Humans can’t define all laws of language but LLMs have learnt them anyway. What if there are laws of nature that humans can’t understand but AI can? Actually, this is already true of black-box models (loan approvals, weather forecasts, …) where benefit/control > understanding. But as data & compute scales, this might “solve” entire fields like psychology, economics, etc. Noah Smith In each area, there might be a limit to how much intelligence is possible/useful. For example, we’re pretty good at recognizing food and emotions - there’s not much benefit / possibility of more intelligence. But we can copy and share this intelligence - and that might help more than we think. Noah Smith The ChatGPT Dropbox plugin can read Markdown files if you specify the path, but can only read PDF, Word, PPTX, Excel, etc. when searching. It cannot update files on Dropbox, but can add and delete. #ForNow Given how long agents run without mistakes, verification is increasingly “drift correction”. You can’t spot it easily. Learn writing specs that EXPOSE drift. Build and test against “oracles” (verification systems). Reduce cost of error. Permissions, in the context of multiple agents, is complex. If agent A can read my email but wants to consult agent B, can B see the email? We’d need to make permissions pretty specific, like: principal: “anand” agent: “agent-17” purpose: “insurance-coverage-check” allowed_data: [“email:read”, “dropbox/notes:read”] allowed_effects: [“email:send”] audience: [“anand”] expires_at: “…” delegation_depth: 1 After struggling to understand where to apply loop engineering, here’s my guess. If you have a metric (or something really well defined) that you want to optimize, and a single agent iteration isn’t enough, loops are a way to get there. Kaggle competitions, benchmark optimizations, etc. are examples. This means that any complex system that you can benchmark (or at least where you can robustly compare results) is loop engineerable. (This means that the ability to benchmark, and using agents to benchmark, will become a key ability.) Ontologies, state machines, etc. can be used to create verifiable systems, e.g. nodes become states, relations are valid operations. That’s great for building verifiable systems (leading to things like LEAN). Of course, a key skill will be knowing what to put into the state, what relations to allow/disallow, what reflects reality well, how it might evolve (e.g. temporal graphs), how that might change in the future, etc. Having said that, this is just creating a neural network of sorts - so according to the bitter lesson, we should just toss data at an agent and have it build a graph (or not) as required. BTW, I shared this with a bunch of speakers at Data Hack Summit who were speaking about knowledge graphs. There was silence for a while. Then, gently, they all agreed. Some people blab. Interrupting with a question is a good diversion mechanism. Some blab even after that. Exiting politely is both wise and surprisingly un-rude. To control your mental state, breathe slowly. 5–6 times/min for five minutes (that’s longer than I thought was needed), exhaling slower than you inhale. PubMed Once a ChatGPT conversation uses a developer plugin, #ForNow it refuses to use other plugins. So, if you need to use a GMail plugin AND a plugin you built yourself, you’d need to use the GMail plugin first, get stuff into the chat, then switch over to yours. I suspect conversations with developer plugins might not be accessible when using other plugins, too - but that’s untested. There’s a jagged edge of AI adoption as well, not just AI capability. Several organizations limit users to weaker agents #ForNow (e.g. only Microsoft Copilot or Gemini). Many have never seen the power of Codex or Claude Code on their systems. It’s hard to convince them that AI can do much more than they think. There’s a “data engineering” industry incentivized by structuring data. This is partly enabled by poor enterprise agent adoption #ForNow (e.g. Microsoft Copilot). The sequence works like this: “AI does not solve something with the data it’s given. Let’s structure the data. It solves it. Therefore, we need to structure data - all data.” The alternative which I believe is: agents will structure it themselves. I noticed that when you submit a prompt on ChatGPT, it changes the URL to https://chatgpt.com/c/WEB:... and once it starts processing it on the server, changes it to https://chatgpt.com/c/... giving it the actual conversation. So, if you see a WEB: in the URL #ForNow, make sure you copy the prompt before reloading the page - because it hasn’t been saved or sent to the server. I assumed inflammation was mostly a bio/chemical process. Looks like neural signals are involved, too, and electrical simulation can control inflammation. This leads us to a new territory: bio-electrical medicine. The Anthropic Economic Index indicates that, on average, if you prompt Claude like an 8th grader, it responds for a 9th grader. Does that mean (a) that more sophisticated prompts get a better response, and (b) if you repeatedly meta-prompt, you increase the sophistication by about a year each iteration, and hence can get very smart prompts by just getting out of the way and with little hope of understanding the question? This might actually make sense if AI will action the result without you needing to understand. The geometric mean is always less than or equal to the arithmetic mean. This is why a “smooth” 8% return is worth much more than a “wild” 8% return. @lumenxbt Quantum cryptography can give us unclonable encryption, i.e. if someone copies a message midway (or you publish it), you can’t independently decrypt both. We knew how to do this in 2020. Now, ChatGPT helped “indistinguishable security”. Between 2 messages, people can’t figure out (e.g. from the length, or other attributes) which message is which. Gemini Agents can record network requests into a HAR file and reverse-engineer an API for many websites. More efficient than browser control. dax The Anthropic Economic Index dataset is on Hugging Face - released quarterly #ForNow. The longitudinal analysis is likely to be interesting. BusinessCaseBench solved over 238 business cases with AI agents and they’re doing well and improving #ForNow. Not surprising. Frontier AI performance across the business disciplines OpenAI Presence shows a pathway for deploying agents. Deploy for a specific job, with only required access to knowledge and systems, company defined policies for approval, agent periodically reviews logs & escalations and proposes updates for testing and approval. A lot of work people are doing on ChatGPT is OUTSIDE their area of work. “… a substantial part of work-related ChatGPT use is from users expanding their role.” OpenAI

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

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

This week, I learned: I continue to be amazed at how efficient video codecs are compared with animated image formats. When compressing 38 PNGs, the final WEBM was smaller than many of the individual PNGs! 2343k: magick -delay 50 -loop 0 file-*.png file.gif 398k: magick -delay 50 -loop 0 file-*.png file.avif (slow) 284k: magick -delay 50 -loop 0 file-*.png file.webp 82k: ffmpeg -framerate 2 -i file-%03d.png -c:v libvpx-vp9 -pix_fmt yuva420p file.webm Please use AI by Shawn Smucker is the best guide I’ve read about where NOT to use AI. I need to be more mindful of this. gpt-transcribe is released at 0.45 cents / minute or 27c / hour. Gemini 3.6 Flash costs about the same ~27c. Gemini 3 Flash costs ~15c and that’s what I use today. Gemini 3.5 Flash Lite costs ~6c / hour but it follows my instructions very poorly. To benchmark this, I just re-run my transcribe_calls.py script on a recent conversation (that I remember well) with a different model to see if it’s clearly better or worse. No fancy benchmarking. Creating / maintaining formal benchmarks isn’t always worth it. ruff 0.16 is out and has a 350+ new default rules. I mean, who would check that functools.cache on instance methods has a memory leak? But its output is so agent-friendly that agents would just fix these on the fly anyway, so it does make sense. It’s another step towards code-writing becoming less accessible to humans. npm install --no-package-lock installs packages ignoring and without creating / updating package-lock.json. Useful for dev environments. Astral has published prebuilt GPU wheels for Flash Attention, vLLM, PyCUDA, and many others. One characteristic of good benchmarks is that they are easy to verify. I see a lot of comparisons of Fable vs Opus by having them generate 3D worlds (e.g. threejs, Blender, melt) - something that’s not trivial for agents, but evaluatable at a glance. Maybe it makes sense to open source the intermediate steps in ALL knowledge work, to make AI as good at it as with code? Arvind Narayanan Open-source software and culture is a historical accident. We take it for granted that not only are the outputs of software engineers’ creative work available publicly, but so are all of the intermediate steps (specifications, plans, mockups), tacit knowledge (StackOverflow, documentation culture), detailed process traces (issues, pull requests, bug fixes, code reviews), collaboration records (version control, project boards), and more broadly a culture of learning in public. This level of explicit description would be completely alien in most professions. ...

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

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

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

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

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