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.
Пьяный турист нанес тяжелую травму участвовавшей в Олимпиаде сноубордистке20:38
。币安_币安注册_币安下载是该领域的重要参考
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在过去的十年里，金蝶的核心护城河，来自ERP系统的高复杂度与高客户黏性。但AI工具的出现，意味着这道护城河有可能已经弱不禁风。过去需要几十人团队开发数月的功能，现在一个小型团队借助大模型，几周就能完成开发，且产品体验更好、落地成本更低。，更多细节参见同城约会
其认为：任何前沿技术的发展与成熟，都需要持续的迭代完善。，详情可参考heLLoword翻译官方下载
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