Codex and Claude: Auto-generated CUDA kernels for PyTorch with end-to-end benchmarks on LTX-Video and Qwen3-8B

AI Impact Summary

A new agent skill enables Codex and Claude to generate production-grade CUDA kernels and PyTorch bindings for diffusion (diffusers) and transformer (transformers) workloads. The approach codifies architecture-specific optimizations for H100, A100, and T4, and outputs a complete kernel project plus benchmark scripts wired to HuggingFace Kernel Hub via get_kernel. Early results show up to 1.88x speedup for isolated RMSNorm kernels and up to ~1.43x end-to-end gains on an LTX-Video/diffusers pipeline with Qwen3-8B, highlighting a path to meaningful throughput improvements but necessitating careful validation of environments and library versions.