A developer used local LLMs on a MacBook to generate and benchmark novel matrix multiplication algorithms. The project employed Microsoft Autogen to orchestrate a five-agent system: a Proposer, Verifier, Coder, Tester, and Manager. This setup aimed to explore efficient Rust code generation without relying on traditional BLAS libraries, demonstrating a practical workflow for custom algorithm discovery.

The experiment builds on a common pain point in ML engineering: optimizing core operations like matrix multiplication. By feeding academic papers on Strassen-like methods into a local Chroma vector database, the system could propose new formulations. Using GGML models and autogen-core allowed for a controlled, state-machine-driven process, turning theoretical research into executable, benchmarked Rust code.

This approach highlights how developers can leverage local, open-source tools to tackle specialized optimization problems. While BLAS-level performance remains a high bar, the methodology provides a blueprint for iterative, AI-assisted code refinement. Future work will likely focus on expanding the corpus and refining agent roles to push the boundaries of what's possible on consumer hardware.