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.
在陆逸轩手下，重力在这两股相互牵引的力量中暂时占了上风。听众仿佛被置于钢琴内部，得以充分感受琴弦的震动。这个音持续了约十秒，随后而来的“轻”却如履薄冰。在这个时间点发布，这张唱片几乎像一则宣言，将围绕肖赛的声音一并抛在身后，陆逸轩得以如释重负地，回到观众此前所熟悉的、那个内省的自我之中。
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