美债也成了香饽饽,见下图,10年期美债收益率下降,意味着美债价格上涨——债券收益率与价格背道而驰,因债券价格按市场利率计算,利率越低,价格越高,反之亦然。
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Фото: Majid Asgaripour / WANA (West Asia News Agency) via Reuters,更多细节参见体育直播
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.
Wait a second: that's not an argument at all! It's a blind assertion based only on my own experience. The only reason that it might sort of work is that it's couched in the same tone of surprised discovery used in those two innocuous examples above---as though after lots of rigorous searching, and trying, and fighting to find in New York the stuff that makes Cambridge the intellectual capital, it simply turned out---in the way that a pie crust might turn out to be too crispy, or a chemical solution might turn out to be acidic---not to be there.