good time to announce that i’ve joined @GoogleDeepMind as a forward deployed engineer i’ll be partnering with startups and researchers across the ecosystem to co-design benchmarks. if you’re building interesting evals, i want to hear about them!

Today we're releasing Blackthorn in Private Alpha: The first Private AI inferencing stack that removes the CPU TEE from the trust chain. Blackthorn can load and run any AI model privately with little performance overhead compared to plaintext inference. https://t.co/5avAouhgWA

today, we introduce a little mischief to the world. for four years, we’ve been brewing real brain cells on electrodes to understand how they compute. now, we’ve turned those discoveries into a video model anyone can use. from 0.5¢ a second, mischief 1 is at least 5x faster than its base and the cheapest image-to-video model on @fal and @OpenRouter. soon to be on @awscloud marketplace. it’s not a delusion anymore. biocompute is real, commercial and this is just the beginning. happy halloween.

We cancelled our Figma enterprise plan This is how we design at HeroUI now 🧵 https://t.co/5v5ARROXvi

I built bbtree, the foundation of databases and filesystems, and the fastest concurrent btree. Code: https://t.co/CWpHJ7c5Bj Blog post & demo: https://t.co/xRrdiPcgbz https://t.co/w2snig2zny

https://t.co/ZcNtg74dVS

Today we are releasing JST-2, the world's first full-stack physics-native world foundation model. Many world models learn in pixel/video space. We went back to first principles and built the entire model stack in 3D. Generate a 3D scene in seconds and expand it in real time. Create characters with motion built in, all through one general architecture.

People assume that in an AI doing formal verification world, they can take their existing codebase, ask AI to prove it correct, and get either a proof or a bug. Sadly reality has a few bones to pick with this, and one of them is the "existing codebase" part. And that's due to the good ol' halting problem. The short of it is that there is no general purpose algorithm that takes any program and input and correctly determines whether it will terminate or run forever. With a few mathematical flourishes we can extend that to "Rice's theorem" and prove this is true for any nontrivial semantic property, meaning we can't automatically prove arbitrary things about arbitrary programs. Now obviously we can prove some things about some programs, or else formal verification wouldn't exist as a field. The trick is that we write the code in a style suitable for verification. One example of this is totality: in Lean, all functions must be guaranteed to return a value for all inputs. If we want some code that runs forever (like f.ex a server), instead of `while true` we do `while (i < 10^60)` so that it's guaranteed to terminate and return a value (after the end of the universe). (Totality is also why, in Lean, `1/0 == 0`. If you convert your code to Lean without accounting for this, you could have correct lean but incorrect code!) Another "style" we use is state machines. Even if your code doesn't quite fit an SM architecture, if you want to prove it at scale, you're probably going to go with a state machine. State machines are the carcinisation of provable code. Hopefully by now the problem is clearer: even if your codebase is objectively correct, it might not be in the right shape to be proven correct! Okay, just have the AI rewrite your codebase into another form, right? Two problems. One: you don't know if you've introduced errors in the rewrite because you can't prove them equivalent (since one of them is not in a provable shape). Two: making code more verifiable can make it worse on other metrics, like modifiability, performance, and complexity. And now you've got the old bugbear of software engineers, the dreaded tradeoff. Now, don't get me wrong, formal verification has a huge role in the future of software, but it's not going to be a breezy "prove my Ruby thx" Claude prompt. It's still going to take work and expertise on our parts to prove our code, and to strike the right balance between formal verification and other forms of correctness. This is one reason I'm so excited to be working at @AntithesisHQ , which tackles a lot of the same properties as proof methods, but verifies them with "deterministic simulation testing" and fuzzing instead of with a proofs. So it's not as thorough and doesn't give you confidence in all inputs, but it automatically works on pretty much any code shape. I've literally grabbed open source projects off Github, told Claude "check properties XYZ in Antithesis thx", and gotten bug reports back. No state machine rewrites needed.

Intel and AMD are working together to simplify AI development on x86. By bringing @PyTorch CPU optimizations together through oneDNN, we’re creating a more consistent software path for developers across Intel and AMD processors. Learn more: https://t.co/ZWjNXxW5SH

We’re releasing all 211 of the tested designs and the lab data as a resource for scientists: https://t.co/zqke30UV2o Read the full technical report: https://t.co/ikWVcFxnyP