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.
“存储价格2023年三季度已走出历史底部，2024年为首轮强反弹，2024年四季度至2025年上半年的‘回调+品类分化’小周期后，真正全面、宽基的强上行大概率在2026年兑现。”研究人员称，本轮周期的核心驱动是需求错配、资本开支与技术迁移，周期或将持续到2026年末甚至2027年。，推荐阅读一键获取谷歌浏览器下载获取更多信息
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