唐山百川机器人共享制造工厂内，整合了800台（套）共享设备和千余名专业人才。前不久，中国科学院力学研究所研发的无源外骨骼仿生机器人就在这里完成样机试制。“工厂科研团队反复优化方案，仅用20天就交付了首台样机。”工厂负责人王孟昭说，“科研机构做‘0到0.8’的技术突破，我们专攻‘0.8到1’的落地转化。”
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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.。关于这个话题，服务器推荐提供了深入分析
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