4800 or 9600 bps modems to extend the local loop interface to a remote location
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。爱思助手下载最新版本是该领域的重要参考
The bigger your RAM, the bigger your detector, so use a desktop's RAM if you can
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.
投资逻辑上，天际资本倾向于“非共识”机会。当行业焦点仍在大模型参数竞赛时，他们认为真正制约AI落地的瓶颈在安全、成本和工程化。Lemon AI团队不到10人，依靠“AI开发AI”的方式实现高频迭代，这种资本效率也符合他们对AI应用赛道的判断。