Ultrahuman Ring Pro hasn’t just got a far bigger battery, it’s been re-engineered from the ground up. The company’s Bhuvan Srinivasan explained the older hardware had been pushed to its limit, especially in terms of the data it could process. Consequently, the Pro is equipped with a dual core processor with on-device machine learning to better crunch the numbers your body is throwing out. Its memory has also been increased, holding up to 250 days of data before it needs to sync with your smartphone. As well as improvements to durability, the new ring is also easier to cut apart in the hopefully rare event your finger, or its battery, begins to swell.
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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.