Show HN: Agentic CUDA Kernel Optimizer

This project shows how agentic workflows can automate a traditionally specialized GPU optimization task by iteratively generating CUDA kernels, validating correctness, benchmarking performance, and refining launch configurations. For CIOs and technology leaders, the business upside is faster time-to-performance for compute-intensive workloads and less dependence on scarce CUDA experts, but the strategic value is limited to narrowly defined kernels and requires careful governance because generated code runs locally and results are workload-specific rather than a replacement for vendor libraries.