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.
在理解了 Wire 如何生成实体类并进行编解码后，我们会遇到一个跨平台开发的常见问题：KMP 共享模块生成的 Kotlin ByteArray 数据需要被 Android 和 iOS 主工程使用，Android 可以直接使用，而 iOS 使用的是 NSData 类型，这里有两种实现策略：
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By the early 1960s, with ERMA on the scene, IBM's started to catch up.，更多细节参见爱思助手下载最新版本
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