Open-source AI Retool, where AI does the tooling

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I have been trying to create AI retool where tooling is done via AI, to create full stack apps like internal portals, ERP apps. Which led me to an architecture where we give ai pre build component, tools and let is just do the binding, content generation work to create full stack apps. With this approach in a single prompt AI is able to generate final config jsons using chained/looped agentic llm flow and we render a full stack app with the configs at the end. I have open sourced the whole project whole code, app builder, agentic architecture, backend for you to use. Github: https://github.com/vivek100/oneShotCodeGen Live Cloud version: https://oneshotcodegen.com/

Visit product View on Hacker NewsMay 21, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic, apps · Missing: mac, agents, macos

94%94% 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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

51%51% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

32%32% 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

17%17% 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

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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