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Трамп высказался о непростом решении по Ирану09:14，更多细节参见51吃瓜
。关于这个话题，夫子提供了深入分析
竹炭的加入，不仅补齐了团队的技术短板，也给缺乏互联网工作经验的波波带来了规范化的工作方式，从部门协作到需求文档，竹炭手把手地教，帮她搭建起互联网公司工作流程。
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.，更多细节参见服务器推荐
Browser benchmarks (Chrome/Blink, averaged over 3 runs) show consistent gains as well: