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.
经过深入细致的实地调研，习近平总书记针对现实存在的问题，为扶贫开出了“精准”处方，有效指导打赢脱贫攻坚战。。搜狗输入法2026对此有专业解读
// 本地测试示例：head = [2,1,5] → 输出 [5,5,0]。业内人士推荐服务器推荐作为进阶阅读
Available model flags: --110m, --tdt-600m, --rnnt-600m, --sortformer. All Google Benchmark flags (--benchmark_filter, --benchmark_format=json, --benchmark_repetitions=N) are passed through.
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