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Tonight's Bedtime Story – AI generated stories with images and audio

Hacker News

Tonight's Bedtime Story – AI generated stories with images and audio

Hey HN - sharing out my holiday hacking project. Tonight's Bedtime Story uses the OpenAI API to generate a bedtime story based on a prompt (or you can just say "tell me a story"). It also generates an image prompt and uses that to create a cover image with dalle-3 then turns the text of the story into audio for easy playback. It's been interesting to see how well this does (as well as some of its limitations). Heres one of my favorite stories it has generated so far: < https://bedtime-ai.nokk.io/stories/2Zek3PtK4SoQaHO1edZDPuQnh... > The project is built on Nokkio[0], a full-stack React framework, and the code is available on GitHub[1]. Hope you like it, suggestions welcome! [0] https://www.nokk.io [1] https://github.com/bradleyboy/bedtime-ai

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
73%73% 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: openai, code, open · Missing: mac, agents, macos
58%58% 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
48%48% 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 · Missing: plus, platform, intuitive
47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
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
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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