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Beyond update dates, freshness signals include referencing recent events, citing current statistics and data, mentioning the current year in context where relevant, and updating examples to reflect current tools and practices. These signals reassure both AI models and human readers that the information hasn't become outdated even if the core topic is relatively stable.

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The problem gets worse in pipelines. When you chain multiple transforms – say, parse, transform, then serialize – each TransformStream has its own internal readable and writable buffers. If implementers follow the spec strictly, data cascades through these buffers in a push-oriented fashion: the source pushes to transform A, which pushes to transform B, which pushes to transform C, each accumulating data in intermediate buffers before the final consumer has even started pulling. With three transforms, you can have six internal buffers filling up simultaneously.