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.
比如前面提到的老年人的AI玩具，实际关注的不仅是银发人群本身，更是整个适老化市场。据称唠唠鹰的研发阶段，负责场景需求调研的团队先后走访了全国20余座城市，收集了5000余名不同年龄段、教育背景的老年用户的反馈。这些真实的反馈能够帮团队更加精准地锁定老龄化场景痛点，比如部分农村网络稳定性差，就优化相应的内容缓存功能，再比如更慢的语速、更简化的按键操作等等。
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