Получившая тяжелые ранения при атаке дрона на автобус россиянка высказалась о целях ВСУ08:54
03:57, 12 марта 2026Россия
，这一点在91吃瓜中也有详细论述
[5]全国人口是指我国大陆31个省、自治区、直辖市和现役军人的人口，不包括居住在31个省、自治区、直辖市的港澳台居民和外籍人员。
At the time, OpenAI was training its first so-called reasoning model, o1, which could work through a problem step by step before delivering an answer. At launch, OpenAI said the model “excels at accurately generating and debugging complex code.” Andrey Mishchenko, OpenAI's research lead for Codex, says a key reason AI models have become better at coding is because it's a verifiable task. Code either runs or it doesn't—which gives the model a clear signal when it gets something wrong. OpenAI used this feedback loop to train o1 on increasingly difficult coding problems. “Without the ability to crawl around a code base, implement changes, and test their own work—these are all under the umbrella of reasoning—coding agents would not be anywhere near as capable as they are today,” he says.
The obvious counterargument is “skill issue, a better engineer would have caught the full table scan.” And that’s true. That’s exactly the point! LLMs are dangerous to people least equipped to verify their output. If you have the skills to catch the is_ipk bug in your query planner, the LLM saves you time. If you don’t, you have no way to know the code is wrong. It compiles, it passes tests, and the LLM will happily tell you that it looks great.