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.
“尝鲜”店里的顾客和我想象中的也不太一样。来的不只是年轻人，也有周边小区的家长；偶尔还有大爷大妈，进门就问山姆和胖东来的鸡蛋、牛奶、油。王哥笑着挑一挑眉毛，说这些老人家很多也是刷短视频看到的，“现在的大爷大妈，见识广，很时髦的”。。雷电模拟器官方版本下载对此有专业解读
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