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.
这些反应暗示了 AI 的价值大于成本，可是能不能跟人并列一起算呢？这引发了关于 AI 是否会取代人类的讨论。
Continue reading...。关于这个话题，夫子提供了深入分析
The agent preset includes domains for Anthropic, OpenAI, Google AI, npm, PyPI, crates.io, Go proxy, GitHub (including release CDN), mise, Node.js, and Ubuntu package repos. CIDR ranges are included for Google and GitHub/Azure CDN IPs.
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Виктория Кондратьева (Редактор отдела «Мир»)。关于这个话题，safew官方版本下载提供了深入分析
Demographics and interest reports: