An Internationalization GitHub Action to Replace Crowdin with LLMs

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I built an open-source GitHub Action that translates i18n files using LLMs, designed as a drop-in replacement for Lokalise, Phrase, and Crowdin. The problem: TMS platforms charge per-word and per-seat, and their machine translation lacks product context. A typical SaaS with 500 strings across 9 languages costs $200-500/month. How it works: - Extracts strings from your codebase (XLIFF, JSON, PO, YAML) - Diffs against previous translations (only translates what changed) - Sends to any LLM (Claude, GPT-4, Gemini, Ollama) with your product context, glossary, and style guide - Commits translations back to your branch Key technical bits: - Structured generation for ICU message format (CLDR plural rules handled correctly - Russian 4-form, Arabic 6-form, etc.) - Hash-based caching to avoid re-translating unchanged strings - Provider-agnostic interface - swap LLMs without config changes GitHub: https://github.com/webdecoy/ai-i18n Happy to answer questions about the ICU handling, prompt design, or anything else.

Visit product View on Hacker NewsJan 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: mac, claude, context · Missing: agents, macos, agent

88%88% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Strong signals: gemini · Missing: supports, reddit linkedin, podcasting

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal

49%49% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: platform, interface · Missing: plus, intuitive, reviews

41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: llama, ide, io · Missing: https docs, excited, just released

27%27% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · Strong signals: saas · Missing: arr, mrr, revenue

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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