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It's been weeks since their parents' passing, so the stab of loss is not as sharp. Instead, the twins get to enjoy the easy comfort of each other's company. Like Jeff and Emily, they share an aesthetic. But it's far from preppy; instead they favor leather jackets and streetwear with an aspirational sense of chill. Whether chatting in the car, getting a coffee, or looking over childhood photos, they are at ease. Where the other families all showed a need to perform for each other, these twins have a connection so deep that it predates birth. They swear that even over oceans, they can sense when the other is getting sick or getting high.
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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.
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