树立选人用人正确导向，把政治标准放在首位，明确要求“对那些勇担当、有本事、坚持原则、不怕得罪人、个性鲜明的干部……组织上一定要为他们说公道话”；
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AI硬件的战略价值在于，它将门槛再次降低，甚至无需你张口，就能和你心有灵犀。
应对疫情等因素影响，要求“发挥好防止返贫监测帮扶机制预警响应作用”；
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.