This creates both an opportunity and a maintenance requirement. The opportunity is that regularly updating content can improve AI citation rates even if the core information hasn't changed dramatically. The requirement is that high-performing content needs periodic refreshes to maintain its competitive position as newer articles on the same topics emerge.
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The solver takes the LLB graph and executes it. Each vertex in the DAG is content-addressed, so if you’ve already built a particular step with the same inputs, BuildKit skips it entirely. This is why BuildKit is fast: it doesn’t just cache layers linearly like the old Docker builder. It caches at the operation level across the entire graph, and it can execute independent branches in parallel.