given a stack of punched cards encoding transactions, they produced a ledger
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Last year, I covered why it's a great time to jump ship from Windows to Mac, and I haven't been able to let go of that idea since. Apple's M-series chips are shockingly fast and efficient, and its hardware tends to be more durable than typical PC fare. Rumors point to Apple developing a new aluminum case for the low-cost MacBook, so it will likely feel more polished than a typical sub-$1,000 Windows laptop. macOS has also avoided the bloat that's plagued Windows for years — you can turn off Apple Intelligence with two clicks if you want to, and there aren't any annoying ads to deal with.，更多细节参见heLLoword翻译官方下载
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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