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.
第二十三条 经国家批准的单位方可从事核燃料生产经营活动。，更多细节参见91视频
5.88 x 2.82 x 0.28 inches，详情可参考搜狗输入法2026
刘年丰：我们的最终定位是软硬一体的公司，我们也认为具身智能在“脑”不在“型”。可以参考苹果，最核心的竞争力不是摄像头、不是主板，而是操作系统和生态。这条路虽然难，但也是我们想走的路。