Израиль нанес удар по Ирану09:28
从春风唤醒生命的感触中，忽然想起了贺知章的名句：“不知细叶谁裁出，二月春风似剪刀。”可再一琢磨，诗里春风固然灵巧，用词确有新奇绝妙处，但总不免失之于锋芒过露。而自己眼见的一切，或许更近于“随风潜入夜，润物细无声”的意味。这风似乎不像剪刀，没那么利落、分明的姿态，倒更像是气是水，是弥漫的、渗透的、无处不在的柔情。它不张扬自己的到来，只是默默地让柳丝自己去绿，让草芽自己去长，让蛰虫自己去醒。像个高明的导演，自己隐在幕后，只让万物去演绎生命的繁华。
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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.
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