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.
"We don't know where they go, but they disappear for about 10 days, and then they'll come back again. And without the CCTV, we never knew that."
。业内人士推荐51吃瓜作为进阶阅读
The Recency GradientNewer models tend to pick newer tools. Within-ecosystem percentages shown. Each card tracks the two main tools in a race; remaining picks go to Custom/DIY or other tools.
const byobRequest = controller.byobRequest;
* @param {number[]} temperatures - 每日温度数组