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.
Что думаешь? Оцени!
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信中何小鹏还提到，过去十年，小鹏都在做「第一个」——第一个量产城市 NGP，第一个推出全栈自研，第一个把飞行汽车开上天。
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伴随 X300 Ultra 亮相的这枚 400mm 增距镜，估计可以直接将手机的可用焦段拓展到恐怖的 800mm，这个数据放在专业相机上也很罕见，在远景捕捉能力上，X300 Ultra 又一次刷新了移动影像的物理极值。