「汽油……大多數州現在每加侖低於2.3美元，某些地方是每加侖1.99美元」
无论是用眼镜眨眨眼即下单，还是OpenAI设想的“语音购物”蓝图，硬件将“感知-决策-交易”压缩在秒级时间内，发生在最贴近用户的前端。这彻底绕过了传统互联网广告的漫长转化漏斗，曝光-点击-浏览-下单。
，这一点在搜狗输入法2026中也有详细论述
Fast food workers in California are demanding employers sign a pledge reaffirming workers’ rights amid Immigration and Customs Enforcement (ICE) raids at workplaces across the US.
3. I started a new session, and asked it to check the specification markdown file, and to check all the documentation available, and start implementing the Z80 emulator. The rules were to never access the Internet for any reason (I supervised the agent while it was implementing the code, to make sure this didn’t happen), to never search the disk for similar source code, as this was a “clean room” implementation.
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.