Silly Face Builder Toy

Hacker News

I created a simple app for my kids and myself to have a little fun. It's a single screen Android app, free, no ads, no account. - Tap buttons (multiple times) to select brush for features like eye, nose, ear. - Tap canvas to paint. - Stack brushes for new features. Like painting round-nose with normal-nose, will get you a new rounded-nose-with-spots. It's a bit like the alchemy game. Created with React Native. I want to try to keep everything without language, so it can be a global app out of the box :). Was just published yesterday, and there will be an update soon where for instance mouth icons can merge to one wide mouth.

Visit product View on Hacker NewsAug 29, 2022

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

75%75% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% 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 · Missing: https docs, excited, just released

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

nativeThis product was originally launched on this platform.

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

38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

16%16% 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.

Correct prediction on native model

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