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.
What’s your keyboard setup like? Do you use a custom layout or custom keycaps?
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What follows is a proof of concept — not a finished standard, not a production-ready library, not even necessarily a concrete proposal for something new, but a starting point for discussion that demonstrates the problems with Web streams aren't inherent to streaming itself; they're consequences of specific design choices that could be made differently. Whether this exact API is the right answer is less important than whether it sparks a productive conversation about what we actually need from a streaming primitive.