S Anand

My daughter and I planned a trip to Timbuktu. For good reasons. Mansa Musa, perhaps the richest person in history, ruled there. It’s right at the edge of the Sahara desert. Buildings are made of yellow bricks. And… well, think about telling your friends, “Oh, I just returned from Timbuktu.” We ruled out flying. Flying is for losers. It’s possible to walk but it’d take 3,800 hours (many months) from Singapore and require 15 visas - Malaysia, Thailand, Myanmar, Pakistan, Afghanistan, Iran, Iraq, Syria, Jordan, Israel, Egypt, Libya, Algeria, Niger, and Mali (many months). ...

This week, I learned: cloudflared tunnel --url http://localhost:8000 now lets you create a quick tunnel - i.e. expose a port via a public URL, like ngrok. No account or login required. Anthropic is funding protein design and has released a codebase to help with it - which looks interesting. These proteins will be tested in Adaptyv’s automated lab. Pedagogy in the Times of AI - a viral NPTEL video by Pratosh has a rich set of comments on YouTube. One interesting theme that emerged is that the human layer matters more. Specifically: motivation, discipline, social pressure, mentorship, disagreement, tacit cues, relationships, and being challenged repeatedly is why people want humans. Claude Code now supports AGENTS.md natively, thanks to Claude Mods. AI seems to be beating humans at short-term superforecasting. And, this may be the worst it’ll ever be. What are the major open questions in interpretability right now? Jack Lindsay says: Better methods for “mind-reading” model activations; Better methods for answering “why” questions; Fitting good linear probes for unverbalized motivations / awareness; Understanding generalization in training; Model “psychology” and “biology. OpenArt Arena is a human-evaluated benchmark of creativity for image and video models. Seedance 2.5 is way ahead of Gemini Omni Flash #ForNow. Galleries and examples are really fast ways of style transfer. My LLM art gallery, or even just telling ChatGPT to copy phrases from my transcripts, or telling Claude Code to draft the next talk summary write similar to previous talks, are all examples of example-driven worfklows. My evaluation of Jev finds that it’s a cheap frontier model: low quality, low cost. Mot exceptional. Naveen’s benchmark also suggests the same. “Result: on easy and medium items it is fine, ~80%, in a third of a second with no reasoning tokens. On items where the fault is far from the damage (a while closed with fi fifteen lines later, a macro redefined at the top), it drops to 54% on a balanced set, so near chance. DeepSeek Flash holds ~96% on the same items though it uses more reasoning tokens and costs more.” Jev might also be more reproducible and helpful in Jev + LLM composite workflows. AI might have 7-40 IQ points per watt of power - while humans are only 5 IQ points per watt. AI might already be more efficiently intelligent than humans. Mistakes I made Week ending 20 Sep 2026 ...

I conducted a session on Sat, 19 Sep 2026 at Shree Niketan Schools — Teachers’ AI Q&A - Shree Niketan Schools, Chennai / Zoom. Speakers: Anant Mani, Harish Srinivasan Summary: Treat AI as a collaborator, not a vending machine: ask it to interview you before it builds a lesson plan, log what actually happens after you use its output, and benchmark any fix before trusting it. Here’s the link to the session ...

I heard a lot about Jev - a new kind of model from TypeSafe. It’s available on OpenRouter. It’s quite low-cost: Input = 4.2c / MTok, Output = free. It only classifies or scores. It doesn’t generate text. So that’s useful for classification, fact-checking, evaluations, etc. I evaluated Jev on 77 data points from BANKING77 and tested Jev against other models. Summary: Yes, it’s cheap (7c per 1,000 classifications), but not much cheaper than DeepSeek V4.1 Flash (8c) or GPT 5.6 Luna (12c). It’s not that accurate (75%) compared with DeepSeek V4.1 Flash (79%) or GPT 5.6 Luna (83%). ...

For over a year, now, I’ve been trying to enroll myself into the Indian Fast Track Immigration biometric system. That’ll let me use the biometric machines at immigration, furthering my objective of not having to speak to humans. Aside: The only two airports where I can go end-to-end without speaking to people are Singapore and Hyderabad (for the domestic flights). Bangalore and Chennai come close in the recent past. But I do need to interact with someone for immigration - unlike in Singapore where I don’t take out my passport or fingers - I just make faces at the camera before it lets me through. ...

It was interesting to see how weak a model Claude 4.5 Haiku is, compared with other frontier models, even inside a good harness like Claude Code. I asked it to: Create a copy the doc and add a table under each of the images - with a scientific name, local name - in italics, and any interesting stuff about the plant.. Here’s the chat which roughly went: Claude: Done! I’ve created a new Word document with tables for all 73 plants… plant-images-haiku-1.docx Me: I can’t see the local names in the output. Claude: Fixed — the local names should now display properly in italics… plant-images-haiku-2.docx Me: The local names aren’t in italics. Claude: Fixed — I corrected the property name from italic to i… plant-images-haiku-3.docx Me: It’s still not in italics. Double-check. Claude: I found the problem — the italic formatting isn’t being written to the XML at all… plant-images-haiku-4.docx … and the fourth attempt got it right. ...

Priya Dialani interviewed me for a podcast. Here’s the rough summary: What do you and Straive do? Straive builds AI and runs AI. I poke at LLMs to learn what they cannot do. My friend calls me an “LLM Psychopath”. Why do AI pilots get stuck before production? AI speeds up coding, but less of testing. Making sure it works can take months. Why organize enterprise knowledge? Better organized info is good for humans and agents. Duh! Can AI organize it? Yes! I’ve had it create one-line summaries of 10K+ docs on Straive Google Drive for easier searching. How can India’s GCCs benefit from AI? Put AI lovers next to business teams and give them AI agent access. They’ll solve asked and unasked problems. How does AI fail? Unanticipated things happen in production. So, have agents monitor failures and revise the process. How is AI software different? Normal software fails reproducibly. AI fails in new ways we haven’t fully understood. Where should a company start with AI? Skip AI strategy. Give people agent access, have them try it, and share what they learned. What if people don’t know what to try? Ask AI. “How could you improve my work?” Even rubbish ideas waste only 5 minutes. How much should we experiment? A lot! Generation is cheap. Ask for 10 options, not one. Who cares even if all 10 fail? But what happened outside of the interview was just as interesting. ...

This week, I learned: Everything I own, owned suggests that agentic reverse-engineering of firmware helps us learn: Features the devices expose Hidden functionalities, e.g. Shure MV7 microphone has a command shell. Dependencies, supply chains and attack surfaces Interesting components, e.g. RTOS webcam has small face tracking and gesture detection models Change behavior, e.g. don’t turn on indicator while recording So, it’s possible (even likely) that my TV, phone, laptop, camera, fridge, car, vacuum cleaning robot, bluetooth headphone, … can be hacked by a rogue AI-assisted firmware update. “Leaving things alone is an underrated engineering skill.” From Software drives people insane. Across over a thousand forecasts, agents lost to a simple exponential weighted moving average forecast. Paper: RAP: Research Attention Prediction Reveals Target-Conditioned Evidence Acquisition Biases. Maybe I should ask agents to get the latest data first, rather than directly asking them to forecast, since the latter fetched less recent material. Claude Code offers function hooks if you enable CLAUDE_CODE_ENABLE_FUNCTION_HOOKS=1. These let you introduce code into almost any part of the Claude Code workflow, meaning you can convert Claude Code into practically amy kind of agent. (Probably a bit of competition to Pi.) However, neither ChatGPT nor I could figure out a use case I would need this for. We need more imagination! The Antropic team provide Claude Tag a separate service account. That’s an interesting portable pattern: giving agents a separate Linux username, GitHub account, email ID, database user ID, etc. is a pattern we understand and know how to govern. FutureSearch.ai is a forecasting app. I’m not sure what model is behind it or how good it is, but it decomposes a forecast into measurable signals, predicts those, and synthesizes. That’s a useful approach. For example, I asked it: Will LLM model routers and model routing companies grow in popularity and review or shrink by Jan 2027?. It broke it up into 5 forecast questions and answered them roughly as: Will OpenRouter’s reported weekly LLM token processing volume exceed 45 trillion tokens/week (about 1.8x its August 2026 level of ~25 trillion tokens/week) by January 31, 2027? (Yes, 95% chance. It’s already high and growing fast.) Will OpenRouter announce a new equity funding round, or otherwise be credibly reported to have reached a valuation above $1.3 billion, between August 2026 and January 31, 2027? (Yes, 84% chance. There seems to be market interest.) Will at least one LLM model-routing competitor to OpenRouter (e.g., Martian, Not Diamond, Portkey, Unify AI, TrueFoundry) announce a new equity funding round of $20 million or more between August 2026 and January 31, 2027? (Yes, 68% chance. VCs will want to fund, and competitors exist.) Will a major AI lab or cloud provider (OpenAI, Google, Microsoft/Azure, Amazon/AWS, Anthropic, or Meta) launch or significantly expand, between August 2026 and January 31, 2027, a native product feature that automatically routes a given request among multiple materially different underlying LLMs based on cost, task, or quality? (Yes, 91% chance. Microsoft already has one; Google launched a preview; AWS will likely announce in re:Invent in Dec) Will Google Trends relative search interest (US, web search) for the term ‘LLM router’ be higher, on average, in December 2026 than it was in July 2026? (No, 25% chance. July 2026 was exceptionally high volume.) In An Alien Mind, Jakub Pachocki, Chief Scientist at OpenAI, was quite instructive. Here’s my takeaway: Models could keep growing smarter at the same speed. We can improve them where capability is measurable, like maths. In fuzzy areas, we’re not even sure how capable they are. Values are fuzzy. Making AI follow our values is tricky. We train models to follow their constitution. But they sometimes fail outside of their training examples. We feed models alignmed data. But when trained against hard objectives, they gently bend rules. We watch models’ thoughts. We avoid feedback on thoughts - so models won’t hide them. But models interact with agents & tools while thinking, so we need to supervise thoughts. Nowadays,models think without verbalizing. They manipulate their own reasoning. So we’re exploring confessions and monitoring internals. Still… best to tighten defenses. We’ll use AI to research how. Meeting people who have a target AND who control scarce resources is a great exercise in humility. Principals of elite private schools, partner managers of top software companies, any officer with a quota (police, income tax, bank loan, IT compliance), etc. You learn to grin while bearing the pain of being with them. Thanks to agents, it’s easy enough to maintain an Android and iOS mobile application separately #ForNow, rather than incur the overhead of React-Native (or other cross-platform frameworks). Shopify is making testing easy by “… designing our app architecture to work for both humans and agents.” Use re.prefixmatch() instead of re.match() in Python 3.15+. This article captures the reason well. (I failed the quiz at the start despite almost 2 decades of Python programming - and LLM atrophy). You can run Linux distributions in the browser. For example, this is a simple, embeddable buildroot distribution that runs purely in the browser. There’s Nix. There’s Alpine Linux. Interestingly, curl https://example.com/ works on Alpine Linux, unconstrained by same-origin policies. It is relayed by the host (bellard.org in this case) via WebSockets, so it can even ssh into other servers. ChatGPT ChatGPT’s Cloud Browser doesn’t forward all events - so it gets stuck on captchas, like Cloudflare’s, when visiting sites like StackOverflow. Here’s an example. Several top-level domains have over 50% of new registrations in 2025 blocklisted. Scammers use new domains extensively. But policing new domains also stops genuine protesters, so it’s not clear what the right approach is. The purpose of DNS is to spread scams. Questions I was asked Week ending 13 Sep 2026 ...

I conducted a session on Fri, 11 Sep 2026 at SUTD DAI Signature Master Class · Expert Industry Series - Singapore University of Technology and Design, Singapore. Summary: Build AI products around evidence, not ideas: prototype quickly, test with agents and real users, and iterate until the product proves its value. Here’s the link to the session ...

Nano Banana (gemini-2.5-flash-image) did a pretty good job converting my parents’ wedding photos to color. I checked how well GPT Image 2.5 would do. The older GPT Image 2 model messed up the faces. The short answer is: better than Gemini 2.5 Flash! Here’s the original and the GPT Image 2.5 colorized version, created with the prompt: “Convert this image to color.” The reason I picked this “benchmark” is because: This is a real need for me. This is a LLM failure: GPT Image 2 doesn’t retain faces as well as Gemini 2.5 Flash does. It’s a benchmark I can evaluate really well. I mean, I know my parents’ faces well enough to spot really subtle differences. So, from that perspective, a few things GPT Image 2.5 managed to capture well was: ...