Гангстер одним ударом расправился с туристом в Таиланде и попал на видео18:08
Цены на нефть взлетели до максимума за полгода17:55
，更多细节参见搜狗输入法2026
Жители Санкт-Петербурга устроили «крысогон»17:52
36氪获悉，2月26日收盘，美股三大指数涨跌不一，纳指跌1.18%，标普500指数跌0.54%，道指涨0.03%。大型科技股多数下跌，英伟达跌超5%，市值蒸发2592亿美元（约合人民币1.77万亿元）创去年4月16日以来最大单日跌幅；英特尔跌超3%，特斯拉跌超2%，谷歌、亚马逊跌超1%，苹果小幅下跌；奈飞涨超2%，微软、Meta小幅上涨。热门中概股普跌，百度跌超5%，哔哩哔哩、爱奇艺跌超3%，阿里巴巴、京东、理想汽车、小鹏汽车跌超2%，拼多多、蔚来跌超1%。
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.