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I made an iOS app to analyze and interpret kid's drawings with GPT-4
Hacker NewsI made an iOS app to analyze and interpret kid's drawings with GPT-4
Hi HN, this is my first post here! I am a solo developer introducing crAion: a native iOS app that uses the new model 'Vision' by OpenAI to analyze your child's drawings. It interprets what they are representing through their art and how they might be feeling. You can save then the artworks in an organized gallery in the cloud and share them with family and friends. Looking forward to your feedback :) App Link: https://apps.apple.com/app/id6473756867
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Product HuntOn track for Day 1 leaderboard · Strong signals: model, apple, apps · Missing: mac, agents, macos83%83% 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.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps · Missing: mobile apps, personal, entrepreneurs69%69% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Indie HackersFits the IH revenue-focused audience · Strong signals: ios · Missing: supports, reddit linkedin, podcasting61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released34%34% 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, revenue20%20% 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, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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