当然，就智能眼镜等产品的销量而言，目前新的智能硬件对于智能手机巨头们的威胁微乎其微，但AI浪潮的席卷，会持续不断催生出更多的智能化产品，它们势必会与智能手机争夺用户及用户注意力。一旦它们加速渗透到用户层，做大体量，智能手机长期作为消费电子市场主导者的地位，可能也不复存在了。
I don’t use all of the colors available from most smart lights, but I do like bright cool white light during the day and nice warm white light in the evening. When the back of the desk was close to a white wall, I had a pair of Govee Flow Plus light bars mounted behind the monitors. The light reflected off the wall, providing really nice background light. That doesn't work now that the back of the desk is not close to a wall. Now, for ambient lighting in the evening, I have six Taysing LED mini indoor spotlights on a smart plug. They’re pointed at the wall, ceiling, and desktop and provide just the right amount of warm background light.，更多细节参见safew官方版本下载
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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.
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