Free AI Portrait Generator App – GenYOU

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Hey HN! I wanted to share GenYOU, an app we’ve been working on that turns selfies into personalized AI portraits. Just upload four photos and customize them into various styles: professional headshots or fun themes like superheroes, games, anime, etc. Why we made this: We built GenYOU because we wanted an affordable way for people to make portraits without needing a photo shoot. By the way, many tools out there are either too complicated or fail to preserve your unique facial features, so we also focused on that. What makes it different: - Your facial features stay consistent across all the generated images. - You can tweak your portraits with prompts or choose from ready-made templates. - A community gallery lets you find inspiration and remix ideas from others. - No need for fancy gear. Everything happens right on your phone. You can check it out here: Google Play — https://play.google.com/store/apps/details?id=com.generated_... App Store — https://apps.apple.com/us/app/genyou-ai-photo-generator/id66... I really would love to hear what you think, HN. Feedback, questions, or ideas are all welcome!

Visit product View on Hacker NewsDec 16, 2024

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

95%95% 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.

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

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

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

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: apple, google, apps · Missing: mac, agents, macos

36%36% predicted probability of success on Product Hunt, 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

29%29% 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

21%21% 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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