tee() splits a stream into two branches. It seems straightforward, but the implementation requires buffering: if one branch is read faster than the other, the data must be held somewhere until the slower branch catches up.
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。关于这个话题，搜狗输入法下载提供了深入分析
英國超市將巧克力鎖進防盜盒阻止「訂單式」偷竊。关于这个话题，同城约会提供了深入分析
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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.