ended up being 21st on the @GPU_MODE qr_v2 leaderboard, at the end codex helped to push the runtime geomean <3ms, I think everyone should try these competitions, I personally learned quite a lot

Second problem is now out: dense symmetric eigenproblem A=QΛQT. Solution due on July 15! We've also enabled ncu profiling for your agents on a @verdacloud cloud box sponsored by our good friends at Brev at @NVIDIAAI

Holy moly guacamole 🥑 Nvfp4 brah

171/365 of GPU Programming Finally got below 2ms on the GPU Mode QR challenge. Still quite far away from CUDA colonel @blelbach, so I'm excited to read everyones' writeups once the competition is over. I especially wonder how #1 is utilizing NVFP4 (or is the submission name a false flag?). Every lower precision attempt has not panned out for me so far unfortunately. Very curious where the top 3 will converge in a week from now... If you're still debating whether to participate, would highly recommend! You learn so much from just trying stuff and being in the discord. And @modal gives you $30 in monthly compute for free. Maybe we can get a bit more compute for the next challenge if we all ask @charles_irl nicely 😁😁

Here is a tutorial for uninitiated for QR for those wanting to enter the competition. All you need to know is a little bit of algebra. Here @ means matrix multiplication. Let A be a 3 x 3 matrix and task is given A can you find Q and R. A special property of Q is that Q @ Q.T = I and R is a upper triangular matrix: A find Q and R. Q @ R = A [ [0, 1, 0], [ [2, 3, 4], [ [0, 5, 6], [1, 0, 0], @ [0, 5, 6], = [2, 3, 4], [0, 0, 1] ] [0, 0, 7] ] [0, 0, 7] ] The R matrix has a special property: [ [2, 3, 4], [0, 5, 6], [0, 0, 7] ] We are generally fine with any R matrix with this structure, see the zeros on the lower triangle. [ [0, 5, 6], A = [2, 3, 4], [0, 0, 7] ] First column of A: [ [0], x = [2], [0] ] Householder matrix: [ [0, 1, 0], H = [1, 0, 0], [0, 0, 1] ] Now compute Hx: [ [0, 1, 0], [ [0], H x = [1, 0, 0], @ [2], [0, 0, 1] ] [0] ] [ [2], = [0], [0] ] Now how do we get H? Well it turns out these are called reflectors. The householder reflectors. Think of putting a mirror on your vector.

Launching a new kernel competition: Linear Algebra Kernels For The Age Of Research. First problem: batched QR decomposition on B200. Old math, modern hardware. Prize: Rare swag and hangout in SF

The return of the strictly proper losses 👑 this time for DPO 🧐 ft. Core Automation logo on an ICML 2026 poster @CoreAutoAI #icml26 + Richard Nock 🫡

Low key: we pack weights well.

I think you’re wrong and there’s 1,000x efficiency gains leftover in deep learning research that could lead to much smarter faster more agentic models given the same inputs

Step 1: Buy a sh*tload of GPUs Step 2: ? Step 3: Profit

New office pre move. Journey begins monday. Home of core automation. Ask @DougDoesOps for invite.