My Fourth Project – AI Manga Translator, Seeking Your Valuable Feedback

Hacker News

Show HN: My Fourth Project - AI Manga Translator, Seeking Your Valuable Feedback Hello Hacker News community, I'm back with my fourth project, and I'm excited to share it with you all. It's been a journey of learning and growth, and I'm eager to continue that path with AI Manga Translator. This tool is my attempt to bridge the gap for manga fans who want to read their favorite series in different languages. Project Overview: Easy Image Upload: Users can effortlessly upload manga images. AI-Powered Translation: Our AI works to provide quick and comprehensible translations. Expanding Language Support: Currently supports a range of languages, with more on the horizon. Why I'm Seeking Feedback: While I've gained some experience with my previous projects, I know there's always room to learn and improve. I'm particularly interested in: Feedback on the UI/UX and how it can be refined for a smoother user experience. Insights on the AI translation performance and how it can be optimized. Suggestions for additional features that the community might find valuable. Engaging with the Community: I'm looking forward to engaging with seasoned developers and fellow enthusiasts on: Strategies for scaling the application as the user base grows. Best practices for maintaining and updating AI models. Ideas for community-driven features or collaborations. Give It a Try: AI Manga Translator I'm proud of the progress I've made, but I know the journey doesn't end here. I'm looking forward to your constructive feedback and any advice you might have. #ShowHN #AI #Manga #Translation #Feedback #HackerNews

Visit product View on Hacker NewsMay 20, 2024

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best fitHighest predicted score across all platforms for this description.

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

67%67% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

32%32% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: excited, lua, hacker news · Missing: https docs, just released, exist

24%24% 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: growth · Missing: arr, mrr, revenue

11%11% 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.

Correct prediction on native model

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