Omnicode – Generate Next.js, Vite, or Expo projects from single prompt

Hacker News

Hi HN! We’re building Omnicode at @leftlabs_ – a developer tool that lets you create complete Next.js, Vite, or Expo projects with just a single natural language prompt. The idea is simple: skip boilerplate, setup, and config. Just describe what you want – e.g., “a blog with dark mode, markdown support, and a responsive navbar” – and Omnicode spins up the whole codebase in minutes. Key Features: Supports Next.js, Vite, and React Native (Expo) Instant project scaffolding via prompt Minimal, clean code output Built for solo devs, hackathon MVPs, and fast prototyping We’re inspired by tools like @lovable_dev and wanted to build something India-native, optimized for speed and simplicity. We’d love your feedback, ideas, and brutal honesty. Try it here We’re also open to collabs, integrations, or helping open-source builders. Thanks! – Mandar (@leftlabs_)

Visit product View on Hacker NewsJul 27, 2025

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

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

40%40% 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

33%33% 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

25%25% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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 · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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