An API for iOS Memoji's character mockups

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TL;DR: Tapback Memojis is a free and open-source API that generates appealing avatar images inspired by iOS Memojis for your web apps. As developers, we've all faced the challenge of finding suitable avatars for our web applications. The clean, expressive style of iOS Memojis has always caught my eye, and I wanted to bring that aesthetic to the wider web development community. Key Features: - iOS Memoji-inspired design - Free to use - Open-source - Simple API integration With popular services like Boring Avatars moving to paid models, I believe there's a growing need for high-quality, free microtools in the developer ecosystem. Tapback Memojis aims to fill this gap, providing an easy-to-use solution for developers who want to enhance their apps with attractive, personalized avatars. Try it out and let me know what you think! Contributions and feedback are welcome. - https://github.com/wimell/tapback-memojis - https://www.tapback.co

Visit product View on Hacker NewsAug 28, 2024

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Indie HackersFits the IH revenue-focused audience · Strong signals: ios · 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, apps, models · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

59%59% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: ios, personal, apps · Missing: mobile apps, entrepreneurs, video

53%53% 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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

14%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: paid · 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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