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.
│ │ ├── AdvancedFilterModal.tsx # Airport/country/owner filtering
which demonstrably led to CAs putting
战前，高通胀与货币崩盘已引发大规模抗议，招致当局残酷镇压。如今随着工厂、能源设施、桥梁和铁路被毁——导致许多伊朗人失业——情况进一步恶化。
杰夫分享了自己从人工智能怀疑论者到爱好者的意外转变历程，展示他如何运用克劳德代码等工具构建定制化解决方案。费尔南多则带来全新AirPods Max与AirPods Pro 3的实测对比，并汇报MacBook Neo一个月深度使用体验。