从焦虑到真香,小镇青年爱上开电车回乡过年

· · 来源:dev资讯

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.

Many people reading this will call bullshit on the performance improvement metrics, and honestly, fair. I too thought the agents would stumble in hilarious ways trying, but they did not. To demonstrate that I am not bullshitting, I also decided to release a more simple Rust-with-Python-bindings project today: nndex, an in-memory vector “store” that is designed to retrieve the exact nearest neighbors as fast as possible (and has fast approximate NN too), and is now available open-sourced on GitHub. This leverages the dot product which is one of the simplest matrix ops and is therefore heavily optimized by existing libraries such as Python’s numpy…and yet after a few optimization passes, it tied numpy even though numpy leverages BLAS libraries for maximum mathematical performance. Naturally, I instructed Opus to also add support for BLAS with more optimization passes and it now is 1-5x numpy’s speed in the single-query case and much faster with batch prediction. 3 It’s so fast that even though I also added GPU support for testing, it’s mostly ineffective below 100k rows due to the GPU dispatch overhead being greater than the actual retrieval speed.

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