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.
Удары российской артиллерии по Краматорску также подтвердил в Telegram-канале советник министра обороны Украины Сергей Бескрестнов с позывным Флеш.
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Трамп высказался о непростом решении по Ирану09:14
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AI产业链的三层架构：从“卖铲子”到“淘金客”的价值传导要理解上游企业业绩与股价的诡异背离，就需要回到AI产业链的三层架构——一个形象的“淘金热”比喻，能清晰拆解各环节的价值逻辑与生存现状，进而找到这一矛盾的根源。