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.
第四条 国务院公安部门牵头负责网络犯罪防治工作。国家网信部门、新闻出版部门，国务院电信、金融、市场监管和外交、教育、商务、文化和旅游、广播电视等有关主管部门，依照本法和有关法律、行政法规的规定，在各自职责范围内负责网络犯罪防治工作。有关主管部门应当与国务院公安部门密切配合，共同做好网络犯罪防治工作。
。关于这个话题，服务器推荐提供了深入分析
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Nature, Published online: 24 February 2026; doi:10.1038/d41586-026-00561-5