Choose-your-own-adventure bedtime stories by AI

Hacker News

Hi! I have a 3-yo child that loves bedtime stories and I built an app for him that has evolved a lot during the last 5 months. The main features are: - Personalised stories with your own character names, topics (emojis) and moral - Interactive mode, which is like the "Choose your own adventure" books I used to read in the 80s - Image generation (DALL-e 3) - Text-to-speech (Google TTS) - Supports 5 languages: EN, ES, PT, FR, NO I'd love to see some feedback about the app functionality and how to find a suitable business model (that covers API expenses). Thanks in advance!

Visit product View on Hacker NewsDec 27, 2023

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

88%88% 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, google · Missing: mac, agents, macos

66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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, month, google · Missing: mobile apps, ios, entrepreneurs

46%46% 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

31%31% 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

19%19% 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

4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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