So this kind of email probably looks like a 2-minute reply: "Would you be up for a keynote at our meetup in December? Could you send us a title, a short abstract and a bio?" It isn't. It's finding my latest bio, checking my calendar for December, digging up what I talked about before so I don't repeat myself, and writing an abstract from scratch. Easily an hour, so it sits in my inbox for a week. That's the kind of email Sol is built for. It automatically spots the task, gathers the relevant context and gets the work ready for your review, so you start from a draft instead of a blank page. Still early, but it's the right instinct: it does the prep, then waits for you. I know a few of these kind of mails are likely sitting in your inbox too.. if so, join the waitlist now 👉

Her two rules for multi-agent setups: "it needs a shared state" and sub-agents that don't block each other. After that, "let it rip" and see what Claude designs for you. Full @latentspacepod talk with @katelyn_lesse and @angjiang 👇

Last week @AnthropicAI rolled out their Messages API rewritten from Python into Rust 🤯 @katelyn_lesse and @angjiang on @latentspacepod: "we don't have a single Rust developer in the room." Humans designed the system, Claude wrote the Rust fork, and shadow traffic verified it 👇

I got community noted, muted and insulted on Sunday for trying to warn people. Yes, you need tailnet access first. But an approved user getting privileges they shouldn't have is still bad. Hope everyone that called me a retard took a second look at their Tailscale config 👇

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.