可以这么说，2010 年前后出生的新一代，他们第一台能接触到的计算设备，大概率会是平板电脑和智能手机，用手指直接点击屏幕，就是他们最自然也最熟悉的交互方式。
https://feedx.site
，这一点在同城约会中也有详细论述
Александра Качан (Редактор)
Microsoft создала убийцу WordWindows Central: В «Блокноте» появились новые функции, доступные в Word
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.