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This does not mean confusables.txt is wrong. It means confusables.txt is a visual-similarity claim that has never been empirically validated at scale. Many entries map characters to the same abstract target under NFKC decomposition (mathematical bold A to A, for instance), and the mapping is semantically correct even if the glyphs look nothing alike. But if you treat every confusables.txt entry as equally dangerous for UI security, you are generating massive false positive rates for 96.5% of the dataset.
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Data flows left to right. Each stage reads input, does its work, writes output. There's no pipe reader to acquire, no controller lock to manage. If a downstream stage is slow, upstream stages naturally slow down as well. Backpressure is implicit in the model, not a separate mechanism to learn (or ignore).