01版 - 实干担当 为民造福

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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.

The desk is infinitely adjustable, but not quickly adjustable. It stays at standing height. Until recently, I would switch throughout the day between standing on a balance board from FluidStance and sitting on a Håg Capisco stool. Both have now been replaced by an inexpensive treadmill. I’ve found typing while walking to be easier than I’d imagined, but using my trackball mouse to be a bit more difficult than expected. So I’ve customized the mouse significantly to increase its functionality and to reduce the need for precision.

The new an

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This combination – localized Dijkstra, super-fast abstract graph traversal, and highly localized A* refinement – is what delivers the 100x speedup.

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