Photovatar – The AI-Powered Avatar Generator

Hacker News

Hey Hacker News community! I'm thrilled to share with you my latest creation - Photovatar, the ultimate avatar generator app powered by state-of-the-art AI models like sczhou/codeformer and pollinations/modnet. With Photovatar, you can easily generate custom avatars with just a few clicks. The app allows you to remove unwanted backgrounds, define custom backgrounds, and even increase photo quality. And the best part? It's completely free, and you can build your own! Our app is perfect for anyone looking to create personalized avatars for their social media profiles, blogs, websites, and more. We've also made it super easy to accept payments and top-up credits with Stripe integration(test mode) Stripe test cards: Card number: 4242 4242 4242 4242 Expiration date: Any future date (1234) CVC: Any 3 digits (567) Give Photovatar a try, and let us know what you think! Feel free the share any feedback and question. Contribute the example repository with issues and pull requests. Live demo: https://www.photovatar.com

Visit product View on Hacker NewsFeb 28, 2023

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

84%84% 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, stripe, new · Missing: mac, agents, macos

58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% 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, revenue

15%15% 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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