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.
BPatterns provide a fluent, Smalltalk-native API on top of the rewrite engine, using ordinary Smalltalk blocks as patterns.
，详情可参考下载安装 谷歌浏览器 开启极速安全的 上网之旅。
深圳坚持将整座城市作为新技术的试验场。在福田，人形机器人探索参与地铁安检；在南山，机器人跟随民警街头巡逻；在宝安，机器人提供“不打烊”的夜间政务服务。
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