Happy Tday to those who celebrate!

Early science acceleration experiments with GPT-5

• AI models like GPT-5 are an increasingly valuable tool for scientists, but many remain unaware of the capabilities of frontier AI. We present a collection of short case studies in which GPT-5 produced new, concrete steps in ongoing research across mathematics, physics, astronomy, computer science, biology, and materials science. In these examples, the authors highlight how AI accelerated their work, and where it fell short; where expert time was saved, and where human input was still key. We document the interactions of the human authors with GPT-5, as guiding examples of fruitful collaboration with AI. Of note, this paper includes four new results in mathematics (carefully verified by the human authors), underscoring how GPT-5 can help human mathematicians settle previously unsolved problems. These contributions are modest in scope but profound in implication, given the rate at which frontier AI is progressing.

CIFAR10 hyperlightspeedbench is a neural network implementation of a very speedily-training network that originally started as a painstaking reproduction of David Page’s original ultra-fast CIFAR-10 implementation on a single GPU, but written nearly from the ground-up to be extremely rapid-experimentation-friendly. Part of the benefit of this is that we now hold the world record for single GPU training speeds on CIFAR10, for example.

What we’ve added:

• custom architecture that is somehow even faster
• way too much hyperparameter tuning
• miscellaneous architecture trimmings (see the patch notes)
• memory format changes (and more!) to better use tensor cores/etc
• dirac initializations on non-depth-transitional layers (information passthrough on init)
• and more!

What we’ve removed:

• explicit residual layers. yep.

This code, in comparison to David’s original code, is in a single file and extremely flat, but is not as durable for long-term production-level bug maintenance. You’re meant to check out a fresh repo whenever you have a new idea. It is excellent for rapid idea exploring — almost everywhere in the pipeline is exposed and built to be user-friendly. I truly enjoy personally using this code, and hope you do as well! 😀 Please let me know if you have any feedback. I hope to continue publishing updates to this in the future, so your support is encouraged. Share this repo with someone you know that might like it!