顺应“势”。场景内容重构，催生了“小正餐” “特色主食刚需” “一人食”的新红利。一人食将成为未来的主流，尤其是50元以下客单价品类，甚至正餐都会走向“1+1”模式，即各自点餐、合并买单。自选快餐、麻辣烫早已实现这一模式。香港港式茶餐厅的核心就是这种消费行为，只是因品类老化，未能适配大陆消费者需求。
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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.
The first animals on Earth may have been sea sponges, study suggests