Cartoon Studio – an open-source desktop app for making 2D cartoon shows

Hacker News

Hi HN — I built Cartoon Studio, an open-source desktop app for making simple 2D cartoon scenes and shows. The basic flow is: place SVG characters on a scene, write dialogue, pick voices, and render to MP4. It handles word timestamps, mouth cues, and lip-sync automatically. This started as me playing around with Jellypod's Speech SDK and HeyGen's HyperFrames. I wanted a small tool that could go from script to video without a big animation pipeline and next thing I knew I was trying to create my own South Park style show and here we are. :D A few details: - desktop app built with Electron - supports multiple TTS providers through Jellypod's Speech SDK - renders via HyperFrames - lets you upload or generate characters and backdrop scenes - includes default characters/scenes so you can try it quickly - open source It runs from source today. AI features use bring-your-own API keys, but the app itself is fully inspectable and local-first in the sense that there’s no hosted backend or telemetry. Here are some fun examples of the the types of videos you can create: https://x.com/deepwhitman/status/2046425875789631701 https://x.com/deepwhitman/status/2047040471579697512 And the repo: https://github.com/Jellypod-Inc/cartoon-studio Happy to answer questions and appreciate any feedback!

Visit product View on Hacker NewsApr 23, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, open · Missing: mac, agents, macos

90%90% 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.

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

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: open source, ide, pipe · Missing: https docs, excited, just released

52%52% 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: video · Missing: mobile apps, ios, personal

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · 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

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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