Jay @jaytxrx

@blind_via @furan will it be possible to unblock me ? I am not able to follow. I enjoy watching your fpga graphic card project progress.

AI is fast food for the brain. Quick, tasty, zero nutrients. If you eat too much, don’t expect to build real muscles. Same goes for coding.

Bayes’ theorem is probably the single most important thing any rational person can learn. So many of our debates and disagreements that we shout about are because we don’t understand Bayes’ theorem or how human rationality often works. Bayes’ theorem is named after the 18th-century Thomas Bayes, and essentially it’s a formula that asks: when you are presented with all of the evidence for something, how much should you believe it? Bayes’ theorem teaches us that our beliefs are not fixed; they are probabilities. Our beliefs change as we weigh new evidence against our assumptions, or our priors. In other words, we all carry certain ideas about how the world works, and new evidence can challenge them. For example, somebody might believe that smoking is safe, that stress causes mouth ulcers, or that human activity is unrelated to climate change. These are their priors, their starting points. They can be formed by our culture, our biases, or even incomplete information. Now imagine a new study comes along that challenges one of your priors. A single study might not carry enough weight to overturn your existing beliefs. But as studies accumulate, eventually the scales may tip. At some point, your prior will become less and less plausible. Bayes’ theorem argues that being rational is not about black and white. It’s not even about true or false. It’s about what is most reasonable based on the best available evidence. But for this to work, we need to be presented with as much high-quality data as possible. Without evidence—without belief-forming data—we are left only with our priors and biases. And those aren’t all that rational.

Okay, kiddo with the wiggles: Bayes' theorem is like a treasure hunt for truth! You start with a guess, like "There's candy in this box!" (That's your "prior" belief.) Then you get clues: Shake it and hear rattling. (Evidence!) Now you think, "Ooh, maybe 80% chance of candy!" Update your guess with each clue. It helps you not stick to wrong ideas forever. More clues = smarter guesses. Got it? Now go play!

this is wild - claude code got PebbleOS booting in qemu in browser (wasm) overnight with no input from me. Took 6 hours in --dangerously-skip-permissions mode, but I woke up to success! you can now try PebbleOS in the browser here: ericmigi.github.io/pebble-qemu-wa… source: github.com/ericmigi/pebbl…

New art project. Train and inference GPT in 243 lines of pure, dependency-free Python. This is the *full* algorithmic content of what is needed. Everything else is just for efficiency. I cannot simplify this any further. gist.github.com/karpathy/8627f…

no one’s gonna believe me but becoming a good speaker is really easy just record yourself for 10 minutes every day, first thing in the morning. don’t send it to anyone, just force yourself to watch it later. you’ll notice every possible flaw you can imagine.

this list is gonna feel super random but each one really piqued my curiosity at different points in time. you may enjoy some of them. Curiously Recurring Template Patterns: drive.google.com/file/d/1yJPlJ2… Roofline: An Insightful Visual Performance Model for Floating-Point Programs and Multicore Architectures: dl.acm.org/doi/epdf/10.11… Dissecting the NVIDIA Volta GPU Architecture via Microbenchmarking: arxiv.org/pdf/1804.06826 Monolith: Real Time Recommendation System With Collisionless Embedding Table arxiv.org/pdf/2209.07663 Language Models Are Better Than Humans at Next-token Prediction arxiv.org/pdf/2212.11281 Motion Blur Removal for Uav-Based Wind Turbine Blade Images Using Synthetic Datasets mdpi.com/2072-4292/14/1…

This is one of the coolest things I’ve seen

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Marc Andreessen: There are two ways to think about education. One is at the national level — how do you educate all kids? But the real question is N = 1: what do you do for one individual kid? And for centuries, the answer has been obvious. If your goal is to maximize a single child, the best method by far is one-on-one tutoring. Every royal family knew this. Every aristocratic class knew this. It’s why Alexander the Great was tutored by Aristotle — and then took over the world. There’s actually statistical proof of this. The Bloom’s 2-sigma effect shows one-on-one tutoring can move a kid from the 50th percentile to the 99th percentile. No other educational method comes close. AI changes that. For the first time in history, every kid can have access to infinite questions, instant feedback, personalized explanations, and real-time quizzes — all at N = 1 scale. This is the most powerful shift in education we’ve seen in centuries. One-on-one tutoring was always the gold standard. AI is what finally makes it available to everyone.

@mnstrmnch it's in japanese (sigbus.info/compilerbook) but you may be able to read it using machine translation given recent ai advancements

スマホでお手軽に制振ダンパーの気持ちに浸れるサイト作りました。 anoken.github.io/html/vibration…

@AlexFinn What is the difference to OpenCode ?

@hamptonism People looking for source math.mit.edu/~gs/linearalge…

@renegadesilicon @vikhyatk found the source math.mit.edu/~gs/linearalge…

My PhD supervisor, Dubuc, once told me « You can do great things with mathematics, as long as you don't become a mathematician. » It took to me a long time to appreciate the wisdom of the his statement. There is basically an endless stream of narrow topics you can master. But most of it would not help you. Rather what helps you is to have a broad base of knowledge that you can quickly integrate. For example, most people need relatively little mathematics. Even high level software developers need little math. But mix in a bit of math, a bit of business, a bit of design, and so forth... and you can achieve great things!

10 days into 2026: - Terence Tao announces GPT & Aristotle solve Erdős problem autonomously - Linus Torvalds concedes vibe coding is better than hand-coding for his non-kernel project - DHH walks back “AI can’t code” from Lex podcast 6 months later An acceleration is coming the likes of which humanity has never experienced before

Truly an honor to see one of my programming heroes, Linus Torvalds, using Antigravity to build part of his most recent project.

@ghumare64 @grok highlight the most important things in the video

All my new code will be closed-source from now on. I've contributed millions of lines of carefully written OSS code over the past decade, spent thousands of hours helping other people. If you want to use my libraries (1M+ downloads/month) in the future, you have to pay. I made good money funneling people through my OSS and being recognized as expert in several fields. This was entirely based on HUMANS knowing and seeing me by USING and INTERACTING with my code. No humans will ever read my docs again when coding agents do it in seconds. Nobody will even know it's me who built it. Look at Tailwind: 75 million downloads/month, more popular than ever, revenue down 80%, docs traffic down 40%, 75% of engineering team laid off. Someone submitted a PR to add LLM-optimized docs and Wathan had to decline - optimizing for agents accelerates his business's death. He's being asked to build the infrastructure for his own obsolescence. Two of the most common OSS business models: - Open Core: Give away the library, sell premium once you reach critical mass (Tailwind UI, Prisma Accelerate, Supabase Cloud...) - Expertise Moat: Be THE expert in your library - consulting gigs, speaking, higher salary Tailwind just proved the first one is dying. Agents bypass the documentation funnel. They don't see your premium tier. Every project relying on docs-to-premium conversion will face the same pressure: Prisma, Drizzle, MikroORM, Strapi, and many more. The core insight: OSS monetization was always about attention. Human eyeballs on your docs, brand, expertise. That attention has literally moved into attention layers. Your docs trained the models that now make visiting you unnecessary. Human attention paid. Artificial attention doesn't. Some OSS will keep going - wealthy devs doing it for fun or education. That's not a system, that's charity. Most popular OSS runs on economic incentives. Destroy them, they stop playing. Why go closed-source? When the monetization funnel is broken, you move payment to the only point that still exists: access. OSS gave away access hoping to monetize attention downstream. Agents broke downstream. Closed-source gates access directly. The final irony: OSS trained the models now killing it. We built our own replacement. My prediction: a new marketplace emerges, built for agents. Want your agent to use Tailwind? Prisma? Pay per access. Libraries become APIs with meters. The old model: free code -> human attention -> monetization. The new model: pay at the gate or your agent doesn't get in.