Paige Didcote says her anxiety is "through the roof" living on the street
// Hash computation is fast，这一点在WPS官方版本下载中也有详细论述
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.，详情可参考heLLoword翻译官方下载
“我们需要的是立足于实际又胸怀长远目标的实干，而不需要不甘寂寞、好高骛远的空想；我们需要的是一步一个脚印的实干精神，而不需要新官上任只烧三把火希图侥幸成功的投机心理；我们需要的是锲而不舍的韧劲，而不需要‘三天打鱼，两天晒网’的散漫。”
The problem gets worse in pipelines. When you chain multiple transforms — say, parse, transform, then serialize — each TransformStream has its own internal readable and writable buffers. If implementers follow the spec strictly, data cascades through these buffers in a push-oriented fashion: the source pushes to transform A, which pushes to transform B, which pushes to transform C, each accumulating data in intermediate buffers before the final consumer has even started pulling. With three transforms, you can have six internal buffers filling up simultaneously.