// 易错点1：条件写反（比如写成cur）→ 栈逻辑完全错误，无法找到上一个更大值
В конфликте на Ближнем Востоке увидели выгоду для Украины20:58
，这一点在新收录的资料中也有详细论述
It’s time to pull the plug on plug-in hybrids，详情可参考新收录的资料
Now back to reality, LLMs are never that good, they're never near that hypothetical "I'm feeling lucky", and this has to do with how they're fundamentally designed, I never so far asked GPT about something that I'm specialized at, and it gave me a sufficient answer that I would expect from someone who is as much as expert as me in that given field. People tend to think that GPT (and other LLMs) is doing so well, but only when it comes to things that they themselves do not understand that well (Gell-Mann Amnesia2), even when it sounds confident, it may be approximating, averaging, exaggerate (Peters 2025) or confidently (Sun 2025) reproducing a mistake. There is no guarantee whatsoever that the answer it gives is the best one, the contested one, or even a correct one, only that it is a plausible one. And that distinction matters, because intellect isn’t built on plausibility but on understanding why something might be wrong, who disagrees with it, what assumptions are being smuggled in, and what breaks when those assumptions fail。新收录的资料对此有专业解读