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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The practical implementation involves thinking about the conversation your audience wants to have rather than the keywords they might type. What are they trying to accomplish? What confuses them? What decisions are they facing? What objections or concerns do they have? When you address these elements in natural, conversational language, you simultaneously create content that people find valuable and that AI models recognize as comprehensive answers to common questions.
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