I built the "Lovable for mobile apps" — here's what I learned

For the past several months I've been building AppNative — an AI-powered

mobile app builder that generates real React Native code from a prompt.

I want to share what I built, why, and what surprised me along the way.

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The Idea

As a javascript full stack developer I have so many mobile apps ideas in mind that I wanted to build for a long time, but never get much time to build them. So I was researching on the internet to find some platforms which can help me build mobile apps with my full time job. But I couldn't find any so started building a AI powered platform which can help me build mobile apps with simple plain english and doesn't suck.

The Problem.

Lovable, v0, and Bolt solved AI generation for web apps. But mobile was

completely ignored. Building a React Native app from scratch still

requires:

- React Native/Expo project setup and SDK configuration

- Expo Router file-based navigation wiring

- A component library that's consistent across screens

- Typed data models and Zod validation schemas

- Firebase or Supabase SDK configuration

- Mobile Apps builds requires android studio and xcode for ios.

That's days of boilerplate before you've written a single line of

feature code. I wanted to eliminate all of it.

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What I built

AppNative is a multi-phase AI pipeline:

Phase 1 — The agent plans every screen, data model, and navigation

structure. You review and approve before any code runs.

Phase 1.5 — Before building screens, it generates a full UI component

library: Button, Input, Card, BottomTabBar, Header, auth components,

skeleton loaders — all styled to your app's theme.

Phase 2 — Screens are built by importing from that component library.

Consistent design throughout, every time.

Phase 3 — If you modify the data model, surgical diff-patching propagates

the change to all affected files without touching working UI code.

Phase 4 — Firebase, Supabase, Appwrite, or PocketBase wired automatically.

Then: EAS Build produces a real APK or IPA you can install on a device.

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What surprised me

1. "Plan first" is the most important feature. Every other AI builder

just generates code immediately. Making the user review the screen

plan and data model before generation dramatically reduces wasted

credits and bad outputs.

2. Components before screens is a huge UX win. Generating the component

library first means the agent has a palette to work from. Screens look

consistent. Users love seeing the component gallery before screens are

built — it feels like the AI "knows what it's doing."

3. EAS Build integration is genuinely hard. The EAS CLI is a subprocess,

not a REST API. Managing credentials across thousands of users'

projects, handling webhook verification, and storing APKs in R2 took

longer than almost any other feature.

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Stack choices I'm glad I made

- Laravel + Livewire instead of a React SPA. The platform moves fast,

Livewire components are easy to reason about, and I don't maintain

a separate frontend codebase.

- In-browser Node.js runtime with virtual file

system, 35+ shell commands. Runs entirely in a Web Worker.

- Expo Snack for browser preview. Free, reliable, zero infrastructure.

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Where it is now

AppNative is in beta at appnative.co. Free tier: 100 credits (enough

to build a real app) for beta users.

Happy to answer questions about the tech, the product decisions, or the

AI pipeline design.