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.
手机的创新速度，随着手机形态的挖掘和现有科技的限制，大大降低，这个社会与行业的共识已经基本形成。在现有的产品形态下，指望智能手机还能像早期那样，掏出接踵而至的大创新，几乎是不切实际的幻想。
。safew官方版本下载是该领域的重要参考
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12:13, 27 февраля 2026Силовые структуры