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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.
，详情可参考safew官方下载
В Финляндии предупредили об опасном шаге ЕС против России09:28
Recall that a barycentric coordinate system is given with respect to a -dimensional simplex, where is no larger than the dimensional space. Given a set of scattered points, it’s possible to create a tessellation of the space by forming simplices from the points, such that any input point that lies within the convex hull of the scattered set can be expressed in terms of the enclosing simplex and its corresponding barycentric coordinates2. This can be understood as a kind of triangulated irregular network (TIN).
In Wolverhampton, Wednesfield Library, Central Library and Finchfield Library are holding coffee mornings from 10:30 GMT on Thursday, Friday and Tuesday respectively.