Developer Uses OpenAI's Codex to Auto-Optimize a GPU Kernel 232x
A developer detailed an experiment where they set OpenAI's Codex agent loose on a compute kernel with a simple loop: generate a variant, benchmark it, feed the results back, and repeat. Over many iterations, the AI-driven process discovered increasingly aggressive optimizations, ultimately landing on a version that ran 232 times faster than the original.
The write-up frames this as 'auto-research' — using an LLM not just to write code once, but to autonomously explore a search space of implementations, guided by real performance feedback rather than human intuition. The approach leans on Codex's ability to reason about low-level performance tricks (memory access patterns, parallelism, avoiding redundant work) and test them empirically instead of guessing.
Commenters on Hacker News were split between excitement about AI-assisted performance engineering and skepticism about how much of the gain came from fixing an unusually inefficient baseline.