2026-02-28 00:00:00:0陈 晔3014274510http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142745.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142745.html11921 做宫灯的人
Can these agent-benchmaxxed implementations actually beat the existing machine learning algorithm libraries, despite those libraries already being written in a low-level language such as C/C++/Fortran? Here are the results on my personal MacBook Pro comparing the CPU benchmarks of the Rust implementations of various computationally intensive ML algorithms to their respective popular implementations, where the agentic Rust results are within similarity tolerance with the battle-tested implementations and Python packages are compared against the Python bindings of the agent-coded Rust packages:
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To test this I built gitgres, about 2,000 lines of C implementing the libgit2 git_odb_backend and git_refdb_backend interfaces against Postgres through libpq, plus roughly 200 lines of PL/pgSQL for the storage functions. libgit2 handles pack negotiation, delta resolution, ref advertisement, and the transport protocol while the backend reads and writes against the two tables, and a git remote helper (git-remote-gitgres) lets you add a Postgres-backed remote to any repo and push or clone with a normal git client that has no idea it’s talking to a database. There’s a Dockerfile in the repo if you want to try it out without building libgit2 and libpq from source.
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