Neurooo – DeepL clone using LLMs for translations

Hacker News

Hi HN, We built Neurooo.com, an automatic translation tool like DeepL or Google Translate, but using LLMs with OpenAI and Mistral. We noticed that LLMs were quite good at translating informal or contextual text, and also way cheaper than DeepL or Google Translate when you factor in the cost per character. So we decided to try and build a similar translation interface to see if we can match the leaders in term of ease of use and quality. We're quite happy about it and people around us seem to think that we're even better in many cases (abbreviations, informal emails, business translations, etc.). We'd love you to give it a try and some feedback! Happy to answer your questions

Visit product View on Hacker NewsJul 31, 2024

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

90%90% 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: google, email, context · Missing: mac, agents, macos

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

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

61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, 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 released

47%47% 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 · Missing: arr, mrr, revenue

12%12% 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.

Incorrect prediction on native model

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