But those tricks, I believe, are quite clear to everybody that has worked extensively with automatic programming in the latest months. To think in terms of “what a human would need” is often the best bet, plus a few LLMs specific things, like the forgetting issue after context compaction, the continuous ability to verify it is on the right track, and so forth.
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Instead of tee() with its hidden unbounded buffer, you get explicit multi-consumer primitives. Stream.share() is pull-based: consumers pull from a shared source, and you configure the buffer limits and backpressure policy upfront.
孩子一天天长大，我没有太多的期许，只希望她能一直保持这份善良、勇敢、开朗与自信，能一直快乐、健康、平安。希望她在幼儿园里，能收获更多的友谊，能学到更多的知识，能感受到更多的温暖与爱；希望她能勇敢地面对困难和挑战，能学会坚强、学会独立、学会感恩；希望她能在爱和陪伴中，慢慢长成自己喜欢的样子。