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.
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Over time, he predicts, "We will see those service levels and speeds and experience improve, and we're already seeing some of that playing out."，这一点在夫子中也有详细论述
第十三条 纳税人按照一般计税方法计算缴纳增值税的，因销售折让、中止或者退回而退还给购买方的增值税税额，应当从当期的销项税额中扣减；因销售折让、中止或者退回而收回的增值税税额，应当从当期的进项税额中扣减。。关于这个话题，搜狗输入法2026提供了深入分析