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

  • Question: Why is it getting harder for graduates to get hired when AI can do a lot of the old work?
    Answer: It is true, but not because graduates can do less. We haven’t figured out what we need graduates for: AI can do the old roles, the new roles and assessment criteria are still unclear, so companies wait or reduce hiring a little.
  • Question: Is government AI adoption driven by utility or FOMO?
    Answer: Both. FOMO is not necessarily bad if it gets people to experiment; the problem is when “we built a chatbot” becomes the achievement. Remove “AI” from the sentence and ask what got better—time, mistakes, cost, or citizen outcomes.
  • Question: If F1 on a small golden set is not enough, how should we set KPIs for an AI workflow at scale?
    Answer: Start with “how much money will I lose?” Put a cost on each kind of error, then compare manual versus AI-assisted work on throughput and error rate. If the human still reviews the whole thing and quality is the same, the automation is only adding cost.
  • Question: Any advice for selling AI when clients are at very different levels of maturity?
    Answer: The range of buyer maturity is enormous and getting stretched: some are discovering basic Copilot capabilities while a small minority are already running autonomous agents. I need a much broader pitch, from correcting spelling mistakes to replacing whole workflows, because I don’t know which buyer I am walking into.
  • Question: Why give an agent a very general prompt instead of a specific one?
    Answer: Be specific if you know what you want. I go general when I don’t know what I want, think I know but am not sure, or may not know that I don’t know; it stops me locking into the wrong answer too early.

Mistakes I made

Week ending 13 Sep 2026