The bottleneck for AI progress was never compute, it was always the verifier. Recursive self-improvement is limited by verification, not computation. Compute buys proposals - verifiers buy knowledge. My second post on the recent OpenAI math results
https://t.co/EfE3xJxbtw
@sameenkarim@github Congrats on the launch! Do you or Github have a commit skill handy for using stacked PRs/worktrees? I want to switch off Graphite because stacked PRs/worktrees don't play well together there.
@mitchellh It's really nice to hear someone with your stature vocalize this when you hear so many people talking about loops and claude jobs that generate 100k lines overnight. Thanks
For over a decade, we’ve accepted that end-to-end backprop is the only way to train deep networks. But holding the entire network in memory all at once is why AI training is hitting a resource wall.
We found a new way to break the network into blocks and train them independently. The trick? Treating the network’s forward pass like a diffusion model denoising a signal.
This reinterpretation slashes the memory needed to train deep models. In our #ICLR2026 paper (https://t.co/PK5h0mqQSo), we matched end-to-end performance across ViTs, DiTs, and LLMs. We did this while training just one isolated block at a time.
@GergelyOrosz I switched to graphite (stacked diffs) in 2023 and switched back to regular PRs last month since stacked diffs don't play nicely with git worktrees
We’re introducing Dynamic Workers, which allow you to execute AI-generated code in secure, lightweight isolates. This approach is 100 times faster than traditional containers. https://t.co/c36Vkb7I0R
@StefanoErmon wow, this is incredible and I've requested access. I've been doing some work with using LLMs to generate web animations and the tokens/sec has been the bottleneck.