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.
export image-lib;
。关于这个话题，WPS下载最新地址提供了深入分析
3014268610http://paper.people.com.cn/rmrb/pc/content/202602/28/content_30142686.htmlhttp://paper.people.com.cn/rmrb/pad/content/202602/28/content_30142686.html11921 蜡梅历寒向春开（一朵花读懂一座城）
float white_noise(int x, int y)
I figured the Secure Snake Home community would be excited to have a new server to play on, so I wanted to support at least a thousand concurrent players. But early performance profiling was bad. I was using something like a full core for every 40 users.