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Solving poker with custom WebGPU kernels – demonstrating LLM-agent code generation
A developer used Codex to automatically generate WebGPU kernels for a neural poker solver in the browser. The LLM agent achieved over 10x speedup from the first attempt and found an activation function optimization on its own, demonstrating how AI-based code generation can replace traditional libraries when output is verifiable.
WHY IT MATTERS
The example illustrates that when LLM agents can write correct and fast domain-specific code with parity testing as validation, it becomes possible to build specialized solutions without waiting for or depending on general-purpose libraries – a paradigm shift in how software is developed.
SOURCES
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