据《The Information》报道，Meta 已与 Google 签署了一项价值数十亿美元的多年期协议，租赁后者的人工智能芯片用于开发未来的新款 AI 模型。
Последние новости，详情可参考旺商聊官方下载
。一键获取谷歌浏览器下载对此有专业解读
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51吃瓜中也有详细论述
* LeetCode 739. 每日温度
Instead of taking the nearest candidates to , we can look for a set of candidates whose centroid is close to . The N-convex algorithm works by finding the closest colour to a given target colour for iterations, where the target is first initialised to be equal to the input pixel. Every iteration the closest colour added to the candidate list, and the quantisation error between it and the original input pixel is added to the target.