Establish monitoring routines to track your AI visibility over time. Whether you use commercial tracking tools or build your own system, schedule regular reviews of your performance. Monthly checks might suffice initially, though weekly monitoring makes sense if you're actively optimizing and want faster feedback on what's working.
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For implementers, the locking model adds a fair amount of non-trivial internal bookkeeping. Every operation must check lock state, readers must be tracked, and the interplay between locks, cancellation, and error states creates a matrix of edge cases that must all be handled correctly.
// Clean up compressor resources on error/cancellation