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Local, In-Browser i18n Translations
Hacker NewsLocal, In-Browser i18n Translations
Hey HN, This idea came to me after using ChatGPT to translate my i18n files. It worked well, but it was inconvenient having to specify each language I wanted to be output. Not to mention the token usage. So, I built this using transformers.js alongside the Xenova/nllb-200-distilled-600M model to enable in-browser translations. There is no server involved with the translation functionality. The only backend-related code running is authentication to enable a few premium features. Would love to know your thoughts :) Cody
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, chatgpt, using · Missing: mac, agents, macos91%91% 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 · Missing: supports, reddit linkedin, podcasting66%66% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released43%43% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal30%30% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue14%14% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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