There are several more quality–of-life software updates, too, like the ability to sift through all those screenshots after they’ve been automatically categorized into sections like barcodes, events and more. If you can’t get enough AI image generation, you can now use Photo Assist to edit your photos using descriptive prompts. Elsewhere, Circle-to-Search now supports multiple, well, circles, if you’re looking to tag and search for multiple objects at once.
Что думаешь? Оцени!
。关于这个话题，搜狗输入法2026提供了深入分析
2026-02-28 00:00:00:0周小霞3014274110http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142741.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142741.html11921 黔北灰豆腐（多味斋）
// drop-oldest: Discard old data to make room
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.