Гангстер одним ударом расправился с туристом в Таиланде и попал на видео18:08
報告指出，2025年10月中旬，ChatGPT接到一名用戶請求，協助規劃行動，目的是詆毀日本首相高市早苗。當時正值高市早苗競選首相前夕，高市曾公開批評中國人權狀況。
。爱思助手下载最新版本对此有专业解读
如果你在两年前问一个硅谷投资人，AI最核心的竞争壁垒是什么，答案几乎是一致的：算力。谁有更多的GPU，谁就有更强的模型，谁就赢了。
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.