Custom Kernels for All from Codex and Claude
This post introduces a skill that enables coding agents to write custom CUDA kernels. The skill packages domain knowledge about GPU architectures, memory access patterns, and PyTorch bindings. The authors point Claude and Codex at two targets: a diffusers pipeline and a transformers model. Both agents produced working kernels with correct PyTorch bindings and benchmarks. The skill is available via the kernels library, and the post explains how to install and use it with different agents. This lowers the barrier for writing custom hardware kernels.
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