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ʻŌlelo Honua v3.1.2 – Automating internationalization

Hacker News

Show HN: ʻŌlelo Honua v3.1.2 – Automating i18n for Multilingual Apps Hey HN! I’m excited to share ʻŌlelo Honua v3.1.2, a tool built to take the pain out of internationalization (i18n) for multilingual websites and apps. If you’ve ever wrestled with messy translation files or clunky i18n workflows, this might be just what you need. Since the early alpha, beta, and RC releases, tons has improved—thanks in large part to the amazing feedback from the HN community! Previous Show HNs: https://news.ycombinator.com/item?id=43474837 https://news.ycombinator.com/item?id=43365955 What is ʻŌlelo Honua? ʻŌlelo Honua (Hawaiian for "World Language" or "Language Bridge") automates and simplifies i18n by handling translation file generation and synchronization. It taps into OpenRouter, Google Translate, and new models like DeepSeek to provide LLM-powered translations—and even generates critiques so devs or translators can do quick sanity checks. Bonus: It’s free, and even works with Shopify! Why I Built This As a developer, I found i18n to be a frustrating afterthought—time-consuming, error-prone, and often deprioritized. Existing tools didn’t quite solve the problem, so I built ʻŌlelo Honua to make i18n painless and accessible for everyone. Huge Thanks to HN This project has come a long way, largely due to the incredible feedback and insights from this community. I’ve completely rewritten it based on your input, and I truly appreciate it. Would love to hear your thoughts—feedback, ideas, or anything else!

Visit product View on Hacker NewsMar 31, 2025

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3points

Did not reach leaderboard

Launch Intel predictions

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

94%94% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: model, google, apps · Missing: mac, agents, macos

87%87% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua

53%53% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

34%34% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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