小马拉大车的结果显而易见：高喊着取代 iPhone 的 Ai Pin 续航崩盘、发热烫人，最终随着服务停止、公司出售，产品也沦为昂贵且无用的电子垃圾。
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В Финляндии предупредили об опасном шаге ЕС против России09:28
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.
Storage Nightmare: A CH car profile for a region can be massive (e.g., OSRM's Europe is tens of GBs, their global car profile around 200GB for just one profile). Our goal was to keep all profiles and parameters for the entire planet well under 20GB.