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.
for the result of the make if the size requested is small
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If the transform's transform() operation is synchronous and always enqueues output immediately, it never signals backpressure back to the writable side even when the downstream consumer is slow. This is a consequence of the spec design that many developers completely overlook. In browsers, where there's only a single user and typically only a small number of stream pipelines active at any given time, this type of foot gun is often of no consequence, but it has a major impact on server-side or edge performance in runtimes that serve thousands of concurrent requests.，详情可参考同城约会
把 Dify 当 FE 标准化工作流平台，收益才是团队级的。
Litmaps (What is Litmaps?)