Liam ERD – Automatically generates beautiful ER diagrams [Apache-2.0]

Hacker News

Hey HN, I’d like to share Liam ERD, an open-source tool that automatically generates beautiful and interactive ER diagrams from your database schemas (PostgreSQL, schema.rb, schema.prisma etc.). We built it to address the common pain of manually maintaining schema diagrams and to help teams keep their database documentation always up-to-date. Key features: - Beautiful UI & Interactive: A clean design and intuitive features (like panning, zooming, and filtering) make it easy to understand even the most complex databases. - Web + CLI: Use our web version for quick demos on public projects, or the CLI for private repos and CI/CD integration. - Scalable: Handles small to large schemas (100+ tables) without much hassle. - Apache-2.0 licensed: We welcome contributions, bug reports, and feature requests on GitHub. Example: For instance, here’s Mastodon’s schema visualized via our web version: https://liambx.com/erd/p/github.com/mastodon/mastodon/blob/m... (Just insert liambx.com/erd/p/ in front of a GitHub URL!) Under the hood, Liam ERD is a Vite-powered SPA that renders an interactive diagram with React Flow. You can host the generated files on any static hosting provider or view them locally for private schemas. We’d love to hear your feedback or ideas! If you find Liam ERD helpful, a star on GitHub would be greatly appreciated—it helps us see what’s valuable to the community and plan future improvements. Thanks for checking it out!

Visit product View on Hacker NewsJan 22, 2025

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

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Hacker NewsStrong engagement from HN community · Strong signals: lua, ide, io · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

26%26% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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