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.

All right, we’re locking in. Only things being worked on are simplifications, more efficiency for more usage, groundbreaking features or new models. Sometimes you have to invest ahead of the curve, but feedback is clear that you all want things to get simpler. On it.

I'll keep saying it: this will never be fixed until it is the underlying credit card service that must pay for fraud. So long as it continues to be passed on to the merchant, it will never get fixed, just mitigated (and still at high cost to the merchant).

To be honest with you all, Rails and Claude are making me feel like a fucking genius. Only in this moment, and it’s silly perhaps, and yet. This is about our game-like pre-conference experience, clouds.sfruby.com/pier which was just an idea back on Monday. And these are not my ideas: @antiflasher designed 8-bit theme, @vmelnikova_en invented Cloud Cards, and @palkan_tula came up with the Pier, and it’s giving me so much joy being able to build it out, and implement team’s feedback. Many things are hidden: Luma, where we sell conference tickets, sends us webhooks with ticket info, our Rails app processes them, sends email invitations, authenticates users with magic links, and when the person uploads their photo, we create their 8bit character, using RubyLLM and Nano banana. We place them on the pier and the game begins: you find yourself and complete other Quests, you collect Coins. Perhaps you’ll be able to use those coins at the conference to get things 🤓. Most importantly: you discover who is coming: their interests, backgrounds. As more people join, you’ll get matched both on Pier and in real life at the event based on shared interests. And you already explore and learn about each other in the meantime. For sure, @palkan_tula might come later with a refactoring (perhaps I rely on that for a bit!) but today I was able to build it exactly how I imagined it and it’s nothing short of true magic.