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.
Instead of tee() with its hidden unbounded buffer, you get explicit multi-consumer primitives. Stream.share() is pull-based: consumers pull from a shared source, and you configure the buffer limits and backpressure policy upfront.
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Metro has been responding directly to riders on X from its @metrolosangeles account, leaning fully into the internet moment.
什么才是大慈善？什么才是真正的达己达人、兼济天下？就是曾国藩说的，以转移天下风气为己任。“凡民之生，庸庸戢戢者皆是，须一二贤且智者率众向义，则风俗渐自淳厚。”这才是大慈善。
，这一点在Line官方版本下载中也有详细论述
Трамп высказался о непростом решении по Ирану09:14
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