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.
2026-02-28 08:00:00
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As for Feldberg, during his 14 years on the platform he says he's noticed a number of changes including a more modern interface, more targeted ads, and the move away from seeing how many downvotes a post would attract.
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至此，AI硬件市场的分野已然清晰，一端是追求现金流与市场份额的“功能快消品”，在红海中比拼供应链效率与成本控制；另一端是赌注未来与生态壁垒的“战略入口”，在蓝海中争夺用户心智与数据主权。