HeyTale – Generative AI picture books for kids

Hacker News

Hey HN, I had an idea a while ago to build an app that uses AI generate a kids picture books, but I didn't know how to train the models. After ChatGPT and StableDiffusionXL came out, it made it entirely possible for me. I don't know if im solving a problem here, it was more of a passion project. Some parents currently allow their child to use a phone or tablet. I wanted to build something that could show the child, these are more than just consumption devices. The idea is the child can imagine a story, and with a single prompt, bring a multipage picture book to life. Tech Stack: - Typescript - React (vite) - Node.js - Firebase (Auth, Functions, Hosting, Firestore) - S3/Cloudfront/Lambda (images) - StableDiffusionXL - ChatGPT (3.5 turbo) Haven't implemented change username yet, so sign up and see what funny kids tale author alias you now go by! Roadmap: - Add change username button - Find cofounders - Improve story quality - Expose page length, target age, art style variables through the UI - Improve character continuity between images - Git like clone/fork/merge features to remix tales - ? - Profit Sometimes ChatGPT returns invalid JSON which causes the generation to fail, this is probably the biggest issue in the system right now. We are currently limiting sign ups to 100 users, as we gather feedback and iterate. Look forward to hearing your thoughts and answering questions.

Visit product View on Hacker NewsOct 25, 2023

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3points

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

92%92% 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, user, models · Missing: mac, agents, macos

81%81% 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: ide, io · Missing: https docs, excited, just released

45%45% 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 · Missing: mobile apps, ios, personal

39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, users · Missing: plus, platform, intuitive

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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