Blooming – Chain AI models on a whiteboard to turn text→images→video

Hacker News

Hey HN, I built Blooming, a visual AI workspace where you drag-and-drop “nodes” (text, image, video) onto a whiteboard and chain AI text, image and video models together. It's kinda like n8n but for AI art: • Node-based canvas • Multi-model switching (test different models side by side and iterate multiple versions) • Pipe text or image output to other nodes to refine prompts, turn them into videos or explain images Demo link: https://youtu.be/TdFzhxeRFNg I’d love your feedback on UI clarity, performance, and which models to integrate next. What’s confusing or missing for AI image and video power users? Everything here is live in prod (you can sign in to use it). Trying to see if this is useful to people. For those interested in the tech stack I used see comment below Thanks for checking it out – Edrick

Visit product View on Hacker NewsApr 27, 2025

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

82%82% 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

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: video, users · Missing: mobile apps, ios, personal

53%53% 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, pipe, io · Missing: https docs, excited, just released

34%34% 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

21%21% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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