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.
“具身天工3.0”刚刚发布——首届机器人半马赛事冠军正在加紧训练，成绩有望大幅提升；朱雀三号重复使用火箭蓄势待发，计划二季度再次挑战回收复用；小米汽车超级工厂的生产线一片繁忙，累计交付量已突破60万辆……。同城约会是该领域的重要参考
Филолог заявил о массовой отмене обращения на «вы» с большой буквы09:36。搜狗输入法2026是该领域的重要参考
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