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Analisi dettagliate su codebase generate da IA, architettura di localizzazione e come mantenere il tuo prodotto multilingue senza riscritture.
The next localization default is an open agent skill: repo-native instructions your coding agent reads, applies, and leaves as standard locale files, without a new SDK or hosted MCP dependency.
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Tolgee, Weglot, POEditor, Crowdin, Lingo.dev, and Languine mapped by price and fit. For AI-coded apps with no keys yet, the real gap is upstream string extraction, and globalize.now handles that on every Git push.
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Agent Skills are spreading across Claude Code, Cursor, and Codex. npx globalize-skills installs the localization Skill. Here is why that slot is becoming the default.
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If next-intl, i18next, or Lingui is already in your repo, L1 mode skips the destructive scaffold: no routing rewrite, no surprise switcher, just locale PRs on every push. Detection signals, hard boundaries, recovery if the wrong branch runs.
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Install one CLI, send Cursor one prompt, and turn a hardcoded English Next.js App Router project into a multilingual app with next-intl keys, locale files, and Git-push translation sync.
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We pointed our AI agent at a production monorepo with Next.js, Android, and MCP servers mixed together. Here is exactly what happened: 28 UI string candidates extracted, 41 entries converted, 5 languages added, and production-quality translations confirmed.
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Lovable prompts keep rewriting UI with new hardcoded strings while locale files fall behind. Here is why that gap widens every release and how Git-native i18n sync closes it without abandoning your builder.
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We audited our own five-locale marketing JSON. Cyrillic in German, formal Sie and vous on a dev tool, invented euros, encoding false friends, and shipping verbs that meant parcels. Then we wrote policy and a verify script so it regresses less quietly.
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Install agent skills, run five prompts, and go from hardcoded English to synced locales and a language switcher—without weeks of manual i18n work.
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English reaches a fraction of the world's online users, yet half the web stays English-only. Here's the conversion data, the ROI case, and why AI-built apps make 'later' more expensive than ever.
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Map the full stack in three layers — i18n architecture, translation management, and execution engines — so you know which tool to reach for first when your app is still mostly hardcoded English.
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Lokalise and Crowdin shine at translation workflows — once your app already speaks in keys and locale files. Most AI-built products skip that step. Here is how to think about tooling when your codebase is still the wild west.
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You do not need to throw away an AI-built codebase to support new languages. Start with i18n architecture, extract strings into keys and locale files, then layer glossaries and automated translation with real QA.
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Localization didn't start with AI, It didn't even start with TMS's, It started decades ago with tools built for translators, not product teams. And that legacy still shapes how the industry operates today.
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Traditional localization tools assume your app is already internationalized. Most modern AI-generated apps are not.
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