V3 also added adaptive speed control. Rather than blindly holding at 16x, the script monitors the audio element’s buffered time ranges to assess buffer health. If the buffer ahead of the playback position is shrinking (meaning the network can’t keep up with the decode speed), the playback rate is reduced to give the fetcher time to catch up. If the buffer is healthy and growing, the rate is nudged back up. This prevents the browser from stalling entirely on slow connections, which would previously break the ended event trigger and leave you waiting forever.
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.。WPS下载最新地址对此有专业解读
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