GenDB – I built a tool to generate a full database from a single prompt

Hacker News

Hey HN, Nic here. I recently built a prototype called GenDB, an AI-powered backend builder designed to eliminate boilerplate and streamline database deployment. The idea came out of my own frustrations: I was spending hours writing and rewriting Python code with TortoiseORM just to build and modify basic schemas. Then I’d have to deploy it all over again for even small changes. After seeing tools like Lovable and Cursor make front-end development nearly effortless, I started to wonder: why wasn’t backend development just as fluid? With GenDB, you can: - Prompt a schema (e.g., “Instagram clone”) via natural language or image - Edit it visually using a DBML-based ERD editor - One-click deploy to GCP or AWS - (Coming soon) Auto-generate APIs and safe migration scripts The goal is to go from idea → schema → live backend in minutes, without writing boilerplate or fiddling with cloud infrastructure too much. It’s still an early prototype, and I’d love your feedback, especially if you’ve run into similar pain points. What seems useful? What’s missing? Where will this fall apart? Demo + details here: https://gendb.carrd.co/ Thanks for taking a look!

Visit product View on Hacker NewsJul 29, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: cursor, lovable, visual · Missing: mac, agents, macos

91%91% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

86%86% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

42%42% predicted probability of success on AppSumo, 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

38%38% 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 · Missing: mobile apps, ios, personal

28%28% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

16%16% 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.

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