I built a Bedtime Stories app with classic fairy tales for my daughter

Hacker News

Hey HN! I’m Andrew, a dad and an IT geek, and I wanted to share a small passion project I built for my daughter. Like many parents, I struggled with bedtime routines. I tried different apps, but most were filled with AI-generated or unfamiliar content. So I decided to create something better: a cozy, story-focused experience built around the timeless folk tales I grew up with. The app was built in SwiftUI in just 2 weeks. I’m using the gpt-4o-audio-preview for dubbing and gpt-4.5 for translations (five languages supported so far). I also gave Cursor a try for the first time and ended up building the website in about 8 hours as a bonus ( https://anytales.com ). I’m curious, would you use something like this for your kids? I’d love your thoughts, feedback, or suggestions!

Visit product View on Hacker NewsApr 21, 2025

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

90%90% 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: cursor, apps, using · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

63%63% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

41%41% 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, io · Missing: https docs, excited, just released

38%38% 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, 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: audio · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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