Сайт Роскомнадзора атаковали18:00
Anthropic’s prompt suggestions are simple, but you can’t give an LLM an open-ended question like that and expect the results you want! You, the user, are likely subconsciously picky, and there are always functional requirements that the agent won’t magically apply because it cannot read minds and behaves as a literal genie. My approach to prompting is to write the potentially-very-large individual prompt in its own Markdown file (which can be tracked in git), then tag the agent with that prompt and tell it to implement that Markdown file. Once the work is completed and manually reviewed, I manually commit the work to git, with the message referencing the specific prompt file so I have good internal tracking.
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Александра Синицына (Ночной линейный редактор)
效果：瞬间将枯燥的代码逻辑转化为了清晰的时序图。Ring-2.5-1T 对代码逻辑的理解极深，生成的流程图几乎无需修改。