Background: Why I Decided to Build an App

My professional background is in Salesforce development, where I’ve spent more than a decade working with enterprise systems.

However, the rapid progress of AI-assisted development tools made me curious.

I started wondering:

Can someone with minimal mobile development experience build and launch an app using AI tools?

Instead of spending months studying tutorials, I decided to try something practical:

Build a simple app from start to finish and ship it to the App Store.

My goal wasn’t to build a startup or make money.
I simply wanted to understand the end-to-end mobile app development process.

The App Idea: A Routine Tracker That Won’t Let You Quit

The app I built is called Cluck.

It’s a simple morning and night routine tracker designed to help people stay consistent with daily habits.

The core concept is intentionally simple:

Users can:

• Set a routine start time
  
  

• Add habits they want to complete
  
  

• Trigger an alarm when the routine begins
  
  

• Receive constant reminders until the routine is finished
  
  

What makes it different is the notification style.

Instead of normal reminders, the app plays rooster “clucking” sounds repeatedly — essentially nagging the user until they complete their routine.

The idea is psychological:

If the reminder becomes annoying enough, users will finish the routine just to stop the sound.

Step 1: Using AI to Generate the App Idea

I didn’t start with this idea myself.

I used an AI prompt that generates startup and app concepts.

After running the prompt, the AI suggested several ideas, including:

• productivity tools
  
  

• journaling apps
  
  

• habit trackers
  
  

• scheduling tools
  
  

Most ideas were generic, but one stood out:

A morning and night routine tracker.

This immediately resonated with me because staying consistent with routines is something I personally struggle with.

So I decided to build it.

Step 2: Planning the App With AI

Before jumping into development, I spent time planning the app with AI.

I used AI conversations to define things like:

• the core features
  
  

• user flow
  
  

• screen structure
  
  

• functionality
  
  

• development approach
  
  

This step helped transform the rough idea into a clear product plan.

Once the concept was finalized, it was time to start designing.

Step 3: Creating App UI Mockups

For the design phase, I used an AI design tool to generate mockups of the app screens.

The tool allowed me to:

• describe the app
  
  

• generate screen layouts
  
  

• iterate on the design
  
  

• refine UI details
  
  

Through several iterations, I designed the main screens:

• Routine setup screen
  
  

• Habit checklist screen
  
  

• Alarm and reminder settings
  
  

• Routine progress tracker
  
  

The ability to quickly iterate with AI made this process surprisingly fast.

Step 4: Creating a Development Plan

Once the UI was ready, I asked AI to generate a build plan.

This included:

• project structure
  
  

• required libraries
  
  

• development steps
  
  

• feature implementation order
  
  

The plan was exported as a Markdown document, which served as a roadmap for development.

Step 5: Choosing the Tech Stack

For development, I chose a stack I was somewhat familiar with.

Framework

React Native

Tooling

Expo

This combination allowed me to build a cross-platform mobile app while keeping development relatively simple.

The AI tool then helped generate much of the initial codebase.

Step 6: Building the App With AI Assistance

This stage involved the most work.

Although AI generated a lot of code, it still required:

• testing
  
  

• debugging
  
  

• iterative fixes
  
  

• manual adjustments
  
  

The development process became a back-and-forth cycle:

1. Generate code
   
   

2. Run the app
   
   

3. Identify issues
   
   

4. Ask AI to fix problems
   
   

5. Repeat
   
   

Over time, the app started becoming functional.

Challenges I Faced During Development

While AI helped significantly, some features were difficult to implement.

Alarm Functionality

One major challenge was creating the alarm trigger that starts the routine.

Ensuring the audio played reliably at the scheduled time required multiple adjustments.

Push Notifications

Push notifications were also tricky.

Handling:

• background processes
  
  

• reminder loops
  
  

• notification triggers
  
  

required several rounds of testing.

App Refresh Issues

Another problem involved ensuring the app refreshed properly when routines were updated.

This required additional debugging and code fixes.

Step 7: Finding Free Audio Assets

Since this was a personal project, I wanted to keep costs as close to zero as possible.

I needed audio clips for the rooster reminders, so I searched for free resources.

Eventually, I found suitable sounds on Pixabay, which offers a large library of free audio samples.

These sounds worked perfectly for the “nagging rooster” concept.

Step 8: Generating the App Icon With AI

For the app icon, I used an AI image generator.

I first asked AI to create a prompt describing the icon, then used that prompt to generate the final image.

Within minutes, I had a clean and usable app icon for the project.

This saved a lot of time compared to designing one manually.

Step 9: Publishing the App to the App Store

The final step was submitting the app to Apple’s App Store.

This process was more detailed than expected.

Apple requires several things, including:

• app descriptions
  
  

• screenshots
  
  

• privacy policies
  
  

• metadata
  
  

• testing information
  
  

Since my app is very simple and does not collect user data, the review process was relatively straightforward.

To my surprise, the app was approved on the first submission.

The approval took about three days.

App Architecture

The app is intentionally minimal.

There is:

• no backend
  
  

• no user accounts
  
  

• no external database
  
  

All routine data is stored locally on the user’s phone.

This kept the architecture simple and avoided additional infrastructure costs.

Lessons I Learned

Building this app taught me several important things.

AI Dramatically Lowers the Barrier to Entry

Even without deep mobile development experience, AI tools made it possible to build a working app.

Iteration Is Still Necessary

AI doesn’t eliminate development challenges.

You still need to:

• test frequently
  
  

• debug issues
  
  

• guide the AI toward correct solutions.
  
  

Simple Apps Are the Best Starting Point

Starting with a small, focused app makes the learning process manageable.

Trying to build something complex as a first project would likely be overwhelming.

Shipping Is the Most Important Step

Many people start projects but never finish them.

Getting an app all the way to the App Store provides valuable real-world experience.

Final Thoughts

This project was primarily an experiment to understand the modern AI-assisted development workflow.

What surprised me the most was how quickly an idea could go from concept to a real product.

The entire process — from idea to App Store launch — was significantly faster than traditional development.

AI tools are not perfect, but they are becoming powerful collaborators for developers and creators.

For anyone who has been thinking about building an app but hasn’t started yet:

Now might be the best time to try.

The tools available today make it easier than ever to turn ideas into real products.