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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.
，这一点在safew官方下载中也有详细论述
🤖 第四步：配置 AI 模型 (以 Qwen 为例)
For him and wife Nicola, from Wednesbury, every day was a waiting game.
围炉之“炉”，从前是炭火炉，如今是电磁炉。不变的是在氤氲的热气里，一家人坐着塑料凳，在木桌前簇拥着，必须起身才够得到对角线的菜。夹肉的筷子频频打架，火锅沸腾到锅盖跳舞了才手忙脚乱地关掉，身后的电视机明明开了很大声音，却总是什么也听不清。