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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“历史研究是一切社会科学的基础。”我们研究台湾历史，编写《台湾百科全书·历史》，是为现实服务的，这个现实就是全面贯彻新时代党解决台湾问题的总体方略。这个服务不是形式的，是实质的，是用台湾历史的真实史事写成的，是有坚实的学术基础的。这部存真求实的著作，清晰地摆明了大陆和台湾同属一个中国的历史依据，它向世人昭示着：祖国必须统一，也必然统一，这是历史发展的大势所趋。。关于这个话题，一键获取谷歌浏览器下载提供了深入分析
Roman numerals: glyph reuse by design