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.
I'm not immune. I've been working on an extensible language-agnostic static analysis and refactoring tool for half a decade now. That's a mothlamp problem if I've ever seen one. My github account is littered with abandoned programming language implementations, parser generator frameworks, false starts at extensible autoformatters, and who knows what else. I think I've even got an async-await implementation in there somewhere. I've got the bug, and I fly toward the light.
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2025年，在一线城市，通过携程平台“日历房套餐”模式，将客房与餐饮、SPA等店内服务打包，酒店单间夜收入同比提升了约10%。
2026-02-28 08:00:00