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.
While this change is spiritually in line with Tatu Ylonen’s development of ssh to prevent move-sniffing attacks, I figured it wasn’t necessary for us since we’re focused on massively multiplayer play, not competitive play.
，更多细节参见safew官方版本下载
到地方调研，习近平总书记常将地图放在手边，叮嘱各地“自觉打破自家‘一亩三分地’的思维定式，抱成团朝着顶层设计的目标一起做”。，推荐阅读同城约会获取更多信息
5月13日，北京市第八十中学学生展示自己设计制作的仿生学设备。 新京报记者 王飞 摄