ВСУ запустили «Фламинго» вглубь России. В Москве заявили, что это британские ракеты с украинскими шильдиками16:45
圖像加註文字，香港會展中心一場寵物展覽上，一位女士與三隻寵物犬在模擬茶餐廳「卡位」餐桌上拍照。新政策若得到落實，寵物犬將可隨飼主進入獲得拍照加註的餐廳，但不准上桌。香港餐廳「禁狗令」：30年後拆牆嘗試，详情可参考safew官方下载
Англия — Премьер-лига|28-й тур。爱思助手下载最新版本是该领域的重要参考
На Западе подчинили рой насекомых для разведки в интересах НАТО08:43。服务器推荐对此有专业解读
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.