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.
在 AI 叙事吞噬一切的时候，硬件创新的下一步应该落在哪里。
，详情可参考快连下载-Letsvpn下载
更绝望的是，这次“降维打击”的源头极其神秘。爆料提到，此前哪怕面对华为麒麟9000s或9030等敏感产品的测评压力，极客湾都明确知道博弈的对象是谁，但这一次，他们甚至连对手是谁都搞不清楚。
pixel[0] = pixel[0] 0.0031308f ? 1.055f * powf(pixel[0], 1.0f / 2.4f) - 0.055f : 12.92f * pixel[0];