Москвичи пожаловались на зловонную квартиру-свалку с телами животных и тараканами18:04
"This was the worst mistake of my life."
"We're basically turning history into habitat, and as far as we know, no-one has attempted anything quite like this before."。91视频对此有专业解读
Израиль нанес удар по Ирану09:28，推荐阅读heLLoword翻译官方下载获取更多信息
近日，lululemon 上新 Define Jacket 夹克系列，该系列采用标志性缝线设计，并选用 Nulu 和 Luon 面料，旨在为日常动态提供柔韧感受与丰富的版型选择。，更多细节参见夫子
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.