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.
摄像头 AirPods：在现有 AirPods 基础上加入摄像头，主要为 AI 提供视觉信息，而非拍摄照片视频。进展最快，最早可能今年亮相。
，更多细节参见Safew下载
How much for a good night’s sleep? $250?
2026年，将全力推进文昌航天发射场登月任务相关配套设施设备建设，以及测控通信、着陆场等地面支持系统各项目建设工作。
2026-02-27 19:00:00