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.
In one month, she watches what her friends would take a year to watch. "But at least I'm not online shopping!"
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Provides a stable, long-term funding model, independent of volatile corporate and personal budgets.
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Nearly every protection-related instruction -- far CALL, far JMP, far RET, INT, IRET, MOV to segment register, task switch -- needs to load a segment descriptor from the GDT or LDT. The 386 microcode centralizes this into a shared subroutine called LD_DESCRIPTOR, which reads the 8-byte descriptor from memory and feeds the high DWORD (containing Type, DPL, S, and P bits) to the Test PLA for validation.
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