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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第九条 国家鼓励和支持网络相关行业组织开展网络新技术新应用监测分析、网络犯罪态势及产业链条分析、网络犯罪风险动态评估，制定网络犯罪防治行为规范，加强网络犯罪防治行业自律、信用惩戒等工作。
In 1992, in a small shop in British Columbia, a sign maker named Blair Gran stared at a wall full of half-finished jobs and felt something click. Sign-making was treated like a commodity — orders in, banners out — but as thousands of signs came through his shop, he couldn’t help but notice the difference between the good ones and the bad ones. He could see that every sign that left his shop was either helping a business get noticed, or letting it disappear in plain sight.