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.
去年「豆包手机」惊艳亮相，又因为令人遗憾的情况而「早夭」。在深感遗憾的同时，也让我们不禁去思考，AI 自动化的模式，就是 AI 手机的理想模式吗？
。Line官方版本下载对此有专业解读
AI浪潮的出现，一度让有些疲软和停滞的消费电子行业看到了复苏的希望，可对智能手机产业而言，到底是希望还是危机，这是一个值得思考的问题。
abort(reason) {