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.
Стоки заполнили территорию площадью 1,75 тысячи квадратных метров вблизи дендрария. Отобранные специалистами пробы земли показали превышения концентрации ряда загрязняющих веществ, включая железо, натрий, алюминий, кобальт, магний, нефтепродукты и фосфор.
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描述：nums1 中数字 x 的「下一个更大元素」是指 x 在 nums2 中对应位置右侧的第一个比 x 大的元素。给你两个没有重复元素的数组 nums1 和 nums2，其中 nums1 是 nums2 的子集。对于每个 nums1[i]，找出其在 nums2 中的下一个更大元素；若不存在，返回 -1。
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