App to Draw Silly Faces

Hacker News

I created a free Android app (no ads, no accounts) for kids to draw silly faces. It's easy to use: tap a brush (eye/nose, etc) and on canvas to draw. The game element is that you can make combinations to discover more brushes. Triangle eye + mouth = tail, etc. - Built in React Native - It contains no language, so for all languages - For all ages. - Free, no ads, no in app purchases Created for fun, my kids like it. It has very few users, would be great to get some feedback. (It's also a prototype for creating a mini wireframing/UX tool with comparable interace. Where you can draw boxes, buttons, etc. But what would be a seperate Show HN once it's live.)

Visit product View on Hacker NewsOct 4, 2022

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

63%63% 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.

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

51%51% 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: users, para · 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

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

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

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

17%17% 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.

Incorrect prediction on native model

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