04版 - 十四届全国人大常委会第二十一次会议分组审议全国人大常委会工作报告稿

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allocation+copy that the hand-optimized code always does at the end.

First new design in ages, upgraded camera, serious performance and longer battery life make it a standout year

电信诈骗后的复盘搜狗输入法下载是该领域的重要参考

之后就是一些特殊穿戴的锻炼了,比如帽子、手套、围脖、口罩这些。

张女士 [email protected]

我国苹果产量和消费量世界第一

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.