Chris Chabot

Introducing Trident: github.com/adewale/trident for benchmarking Mermaid implementations.

Another reason it's unlikely there'll ever be a third generation Martian native: Dust. pnas.org/doi/10.1073/pnas.26…

Even a world? This started as a quick experiment: a small alley in Taipei as the starting image, and the same Atmee v2 model generating the scene continuously, live and controllable. There are still distortions and artifacts, but for a live avatar model it hints at where we’re going: an Agentic World Model, a world with life-like agents that behave like people. After all, what’s a world without the people in it?

Looking for a developer (who happens to be great at comms) to lead comms for the Claude developer platform: job-boards.greenhouse.io/ant…

“Define "verified" upfront, because post-facto, you'll rationalize any outcome. “ It’s hard to understate how true these words from Addy are. Without strong verification tooling it’s impossible to fully trust agents. However it’s easy to fall into if you are not careful

really dislike a lot of menswear trend reporting. "corduroy is making a comeback." "the humble cardigan is suddenly everywhere." "this season, it's all about layering." "brown is having a moment." yea man it's called fall/ winter clothing and it's getting cold

From a security engineer, on the AI threat landscape: "I actually don't think it's right that defense is going to get a lot harder because of AI. Most of these breaches, if there's already a security program in place at all that follows the kind of things that companies claim to be doing in SOC 2s or in other certifications, will provide the defense in depth to prevent these breaches from having any meaningful impact on their environments." I hate to say it, but Binance had every single on of these certifications, and got popped open, attackers stealing the largest-ever sum, $6B. I don't think the "traditional" secutity industry realized how bad things are. SOC2 does not protect you from zero days (that abliterated AI agents can and do find), nor from abliterated AI agents getting inside your trust boundary. In a few years I'm sure we'll have the certifications that mandate how to continioulsy scan your own systems with (abliterated?) models, red-teaming; and continiously monitor your live metrics (to spot anythig suspicious); and have near-realtime patching of CVEs (also surely with AI); and that MIGHT be enough But it's putting your hand in the sand assuming that pre-AI security posture will work just fine... unless it's secutity posture that is formally proven to work (see: cryptography)

New Anthropic research: How AI agents shape experts' judgment calls When experts delegate work to an agent, do they still make the key decisions? We tested this in a randomized trial with 50 professional data analysts.

Amazon's leadership is very good at writing, but you still need to read between the lines. What I read between these lines: Engineers who are not "product engineers" (aka eng + PM interest) and don't like to be customer-facing will not have a future inside of Amazon Full: aboutamazon.com/news/company…

The coding interview is dead, at least for my team at Anthropic. I've seen enough to know that it no longer provides signal. I removed it from loops this month. I have a spicy, optimistic take - I think there's an opportunity for junior engineers: felixrieseberg.com/hunger-be…

Fun fact: `--dangerously-skip-permissions` doesn't actually approve EVERYTHING—we still block some obvious things like `rm -rf ~`. But today's release will let you disable this if you really want, by setting a new env var `CLAUDE_CODE_EVEN_MORE_DANGEROUSLY_SKIP_PERMISSIONS=1`

Many of you don't understand how serious this is, so let me break it down. In computer science there is a famous kind of puzzle called 3-SAT. You are given a long list of yes-or-no switches and a set of rules about how they must be arranged, and you have to find a combination that satisfies every rule. The obvious way to solve it is to try every combination, but each new switch doubles the number of possibilities, so a puzzle with a few hundred switches would take longer than the age of the universe to brute force. For decades, the smartest people in the field found clever tricks that made this a little faster, but every trick still hit the same wall: the time required kept exploding as the puzzle grew. About 25 years ago, two computer scientists, Russell Impagliazzo and Ramamohan Paturi, working with Francis Zane, turned that wall into a formal idea. They proposed the Exponential Time Hypothesis, which says this explosion is not a failure of human cleverness but a law of nature. No algorithm, however brilliant, will ever solve 3-SAT without the time blowing up exponentially. Nobody could prove it, but almost everyone believed it, and it became one of the foundations of modern computer science. Researchers used it as a measuring stick. If you could show that solving some other problem quickly would also crack 3-SAT quickly, you had shown that problem was hopelessly hard too. Hundreds of papers, across scheduling, graphs, logic and much more, rest on this one assumption. Now an internal AI model at OpenAI claims to have broken it. It says it found an algorithm that solves these puzzles meaningfully faster than the hypothesis allows, and it has checked the proof in a computer verification system called Lean. And here is the remarkable part: the very people who proposed the hypothesis, Impagliazzo and Paturi, along with MIT's Ryan Williams, are now leading the public review of whether a machine has disproved their own idea. So what happens next? If the disproof holds, a large part of theoretical computer science has to be revisited. Every result that said "this problem cannot be solved faster, assuming ETH" loses its foundation, and researchers will have to work out which of those walls were real and which were simply where we stopped looking. It does not mean everything becomes easy. The new algorithm is still slow in absolute terms and this is not the famous P versus NP problem being solved. But it means our map of what is possible was wrong in a place we were very confident about, and it was an AI that redrew it. If the disproof fails, the hypothesis survives and the field breathes out, but we will still have watched an AI produce serious, checkable work on one of the deepest questions in the discipline, closely enough that the founders of the idea had to take it seriously. Either way, something has shifted.

i started having my agents print off technical (nasa like) review sheets for when they have stuff ready for me i also got some newsprint and it feels so authentic and good to use

If you only remember one thing from this post it's this: "It'll turn your codebase into an unmaintainable mess." Maybe that was true six months ago. Now? I'm not convinced it adds slop. And even if it does, I'm not convinced the result is unmaintainable. Couldn't agree more with @thorstenball

This is it. Everyone is excited about AI. I'm not. AI is the biggest commoditizer of human capability we have ever built. Doctors earn more than cleaners because expertise takes a decade to acquire and judgment to apply. AI is going to make most of that expertise available to anyone, at marginal cost. The gap doesn't go to zero. But it shrinks. And the same thing happens in almost every knowledge profession. Programmers. Lawyers. Analysts. Traders. Designers. The taxi driver replaced by a robotaxi is the easy case. The harder ones are people who spent twenty years training to do something a model can now do in a second. The one corner of human life I don't see this hitting is sport. Nobody pays to watch a robot beat a human. The whole appeal is humans competing against each other. That's why nobody watches Deep Blue play chess anymore. From an investing perspective, the next decade plays out in three phases. Phase 1 is the buildout. Hundreds of billions in capex on chips, data centers, and energy. This is what's driving today's market. Phase 2 is the deployment. Productivity rises sharply, then deflation sets in as the cost of knowledge work collapses. Jobs disappear faster than the economy can replace them. Phase 3 is the social adjustment. Capital becomes more powerful than labor. The biggest winners are likely to sit where AI cannot reach. Real assets. Energy. Commodities. Precious metals. Scarce land. And there's something deeper than the economics. The Japanese have a word for it: ikigai. The reason you get up in the morning. Your vocation. Your purpose. The work that makes you feel needed. AI is coming for that too. Nobody has yet figured out what a healthy society does when most of its members no longer feel needed by anything. I might be wrong. I hope I am. If I'm not, the world is about to change faster than most people are ready for.

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