SDK for Building AI-Powered Creative Tools

Hacker News

We’ve integrated AI features into our Creative Editor SDK using its plugin system. It’s model-agnostic, so you can plug in GPT-4V, DALL·E, Stable Diffusion, or your own custom models. Unlike traditional prompt → copy → paste workflows, this lets users generate, edit, and remix images and video directly on the canvas. You can even compose visual prompts using annotations, references, or multiple images to guide generation. We designed it for real-world creative workflows, beyond simple concept generation. - Works with any model or API - AI for image, sticker, video and audio generation - Try the live demo in your browser You are not limited to these workflows; Our plugin system allows you to extend several parts of the UI and create tailored workflows depending on the models you want to use. Would love feedback or to hear how others are thinking about AI-powered creative tooling. Questions and ideas welcome!

Visit product View on Hacker NewsJul 24, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: model, user, models · Missing: mac, agents, macos

96%96% 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 · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

50%50% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

49%49% 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

44%44% 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

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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