How AI App Cloning Just Made It Possible to Rebuild a $24 Million Dollar App in Under 1 Hour in 2026

How AI App Cloning Just Made a $24 Million Dollar Business Model Available to Anyone in 2026

App cloning using AI is no longer a concept reserved for experienced developers with years of coding knowledge and a large team backing them up.

What was once a skill that required months of planning, design work, backend engineering, and deployment experience has now been compressed into something that almost anyone with curiosity and the right tools can pull off in a single sitting.

The app in question is a calorie tracking tool that earns approximately $24 million every year, built around a dead-simple concept — users photograph their food and the app tells them the nutritional breakdown instantly.

What makes this story even more fascinating is that this app was not built by a Silicon Valley company with hundreds of employees.

It was built by a group of teenagers who were still in school when they launched it, which means the barrier to building something meaningful with AI has dropped further than most people currently realize.

This is exactly where a tool like AI pays you daily becomes relevant, because the same AI-powered systems that make app cloning possible are the same ones that everyday people are using to generate income online in 2026.

The strategy covered here is broken down into three clear stages: building the project plan, constructing the app with AI tools, and deploying it live to the App Store, and each stage reveals just how accessible AI app cloning has become.

Step 1 — Creating the Master Plan With Brain Dumper AI

The first step in any successful app cloning project is building a clear project context, and the tool used for this is Brain Dumper AI, found at braindumper.ai.

The platform is designed specifically to help creators organize their ideas before handing them off to an AI coding assistant, and it works by asking the builder to simply type out everything they want the app to include.

Once the ideas are entered, the platform processes them and generates a structured project plan that serves as the blueprint for the entire build.

After selecting the mobile app category and generating the plan, the platform produces a downloadable context file that contains every detail the AI will need to understand the scope of the project.

This context file becomes the single most important document in the cloning process because it tells the AI coding assistant exactly what the app is supposed to do before a single line of code is written.

NodeJS also needs to be downloaded at this stage because it enables the NPM and NPX commands that are used throughout the build to install packages and run the application in a local environment.

The third tool introduced at this planning stage is Mobin, which functions as a massive library of real, successful mobile apps broken down screen by screen, allowing the builder to study professional UI and UX without having to manually download and screenshot every app individually.

Searching for calorie-related apps inside Mobin surfaces results like Livo, MyFitnessPal, and Macrofactor, and after reviewing the design quality of each, Livo’s interface stands out as the strongest model to clone from.

By navigating to the flows section inside Mobin, the entire user journey of Livo becomes visible, including a 31-screen onboarding flow that has clearly been optimized over time to convert new users effectively.

This entire flow can be copied directly into Figma using the built-in copy button, which means the design work that would normally take weeks to produce is available in seconds through AI app cloning.

Step 2 — Building the App With Windsurf and AI Tools

With the project plan ready and the design assets saved in Figma, the next phase is opening Windsurf and setting up the actual coding environment.

Windsurf is an AI-powered code editor that uses an internal assistant called Cascade to generate, fix, and manage code through a natural language interface, which makes it ideal for AI app cloning even without formal programming experience.

A new project folder is created, the context file from Brain Dumper is dragged in, and then workspace rules are configured inside Windsurf’s settings to ensure the AI knows exactly how to use Expo when building mobile apps.

Expo is a framework that simplifies the process of building React Native mobile applications, and its routing system connects all the different screens of the app using a file-based structure that keeps everything organized automatically.

The command used to initialize the project is MPX create expo app at latest with the router flag, and once Cascade receives this instruction alongside the context file, it begins scaffolding the entire application structure from scratch.

This includes creating folders for onboarding, authentication, home, components, and pages, and it also installs essential packages like Expo Camera for food photography, Image Picker for uploads, and Expo Notifications for user engagement.

Any errors that arise during setup are resolved by sending them directly to Cascade, which identifies the issue and applies the fix automatically, turning what would normally be hours of debugging on Stack Overflow into a matter of seconds.

Once the app is running using the NPX Expo Start command, it can be previewed live on a physical phone by downloading the Expo Go app and scanning the QR code that appears in the terminal.

The result at this stage already includes working onboarding screens covering goal selection, gender input, height, weight, activity level, and even a paywall screen, all generated from the project plan without any manual design work.

This is the power of AI app cloning — the structure, logic, and user flow of a professional app are produced automatically from a well-written context file.

Adding a Real Backend With Supabase

The next major milestone in the cloning process is replacing placeholder data with a real database, and the tool used for this is Supabase, an open-source backend platform that handles user authentication and data storage.

After asking Cascade to set up the Supabase backend beginning with user authentication, a new project is created inside the Supabase dashboard with a chosen region and database password.

Once the project is ready, the URL and API key are copied from the project settings and pasted into the environment file inside Windsurf so the app can communicate with the database securely.

The SQL schema for user profiles, meal tracking, and subscription records is then generated by Cascade and pasted into Supabase’s SQL editor, which creates all the necessary tables in a single run.

After confirming the tables are in place — daily goals, food items, meals, profiles, and subscriptions — the database connection is tested by creating a new account inside the app and checking whether the user appears inside Supabase.

The entry shows up immediately, the profile information is saved correctly, and after scanning a meal, the food data appears in both the meals table and the food items table, confirming that the entire backend is functioning as designed.

AI pays you daily is built on this same philosophy — using AI infrastructure to automate the most time-consuming parts of building an income-generating product so that real results arrive faster.

Powering the Food Scanner With Google Cloud Vision API

The food scanning feature is what makes this type of app genuinely valuable to users, and enabling it requires connecting the Google Cloud Vision API to the project.

A Google Cloud account is created, a new project is set up inside the console, and the Cloud Vision API is searched for and enabled from the API and Services section of the dashboard.

Once the API key is generated and named, it is pasted into the environment file inside Windsurf, and a fresh chat is opened with Cascade to instruct it to configure the Google Cloud Vision API as the AI integration for the food scanning functionality.

After the configuration is complete and the app is restarted using the NPX expo start -c command, the food scanning feature is tested by uploading an image of an apple, which the app correctly identifies and returns with approximately 100 calories and no protein — confirming the AI integration is working accurately.

This is the moment where the MVP becomes functional, because the core value proposition of the app — photograph food and receive nutrition data instantly — is now live and operational through AI app cloning.

Upgrading the UI Using Figma MCP and Real App Design Inspiration

With the functionality in place, the focus shifts to improving the visual quality of the app, and this is where Windsurf’s built-in Figma MCP connection becomes a significant advantage.

MCP, or Model Context Protocol, is a standard that allows AI coding assistants like Windsurf to communicate directly with external tools and services, essentially acting as a universal translator between AI and the digital tools it needs to interact with.

Without MCP, the AI can only generate code in isolation and cannot access the design files, databases, or APIs that give an app its real-world functionality and appearance.

With MCP enabled, Windsurf can connect directly to a Figma file and read every screen saved inside it, which means 84 curated mobile app screens from Livo, MyFitnessPal, and Macrofactor are now available to the AI as live design references.

The Figma API key is generated from the account settings inside Figma, pasted into Windsurf’s MCP configuration panel, and the connection is saved, after which Cascade is prompted to examine the Figma file and use the saved screens as design inspiration to improve the current app’s UI and UX.

The AI processes the designs, identifies the strongest patterns across all three apps, and begins implementing a combination of those design decisions into the existing codebase, covering the dashboard, food log, profile screen, and onboarding flow.

After restarting the app and scanning the QR code again, the result looks like something produced by a professional design agency, with clean typography, intuitive navigation, and a visual hierarchy that matches the standard of apps currently sitting in the top charts of the App Store.

This level of output from a single prompt represents what makes AI app cloning genuinely competitive with traditionally built products in 2026.

Deploying the App to the App Store With EAS and TestFlight

The final stage is taking the app out of the local development environment and submitting it to the App Store so that real users can download and test it.

This is done using Expo’s EAS build system, which handles the entire process of compiling the app and submitting it to Apple with a handful of commands run from the terminal.

An Expo account is created at expo.dev/signup, an Apple Developer account is registered at developer.apple.com for a one-time annual fee of $99, and then the EAS login command is run inside Windsurf to link the project to both accounts.

The command NPX TestFlight initiates the build and submission process entirely automatically, installing the EAS CLI, logging into Apple, and walking through each step with simple yes or no confirmations.

Once the build is complete, it is visible inside App Store Connect under TestFlight, and a public testing link is generated by creating a new external testing group, adding the build, and copying the shareable URL.

That link can now be sent to anyone in the world, allowing them to download and test the app directly on their iPhone, which is a fully functional MVP calorie counter built from scratch in under one hour through AI app cloning.

This kind of speed and accessibility is exactly what platforms like AI pays you daily are built around — giving everyday people the tools and knowledge to move from idea to income faster than ever before in 2026.

Why Cloning Is Only the Beginning of the Real Strategy

Cloning an app is not the same as building a business, and the story of the original calorie tracker makes this distinction crystal clear.

When it was first launched, it was not a polished, fully featured product — it was a simple ChatGPT wrapper designed to test whether users actually wanted to track calories through photos before any serious development investment was made.

The fact that it grew into a $24 million per year business is not a result of the technology alone but of the marketing strategy that followed, specifically the way its creators understood their users, positioned their product, and found channels that allowed them to scale acquisition without massive budgets.

The lesson here is that AI app cloning gives any builder the ability to produce a credible, functional MVP quickly enough to test a market before spending months or years building something that may not resonate.

Once the market responds positively to the MVP, that is the signal to invest more deeply in design, features, marketing, and monetization, which is the same path the original app followed on its way to $24 million in annual revenue.

AI pays you daily operates on this same principle — the goal is not to build something perfect on the first attempt but to deploy fast, learn from real users, and iterate toward a product that genuinely earns.

App cloning in 2026 is not about copying someone else’s success for its own sake — it is about using proven demand as a launching pad for building something new, something that fits a gap in the market the original product left open.

The tools are available, the process is documented, and the only thing standing between a working idea and a live product on the App Store is the willingness to start.