Based on a tutorial by Clean Flutter
Struggling with writing repetitive Flutter code? Tired of spending hours on boilerplate when you could be focusing on features that matter?
I’ve been there too. That’s why I’m excited to share this game-changing tutorial that shows how to build a complete Flutter weather app using Cursor AI – without writing a single line of code manually. The best part? The resulting code follows clean architecture principles and remains maintainable and scalable.
Quick Navigation
- Setting Up Cursor AI & Project Rules (00:00-03:45)
- Building the UI Interface First (03:46-08:20)
- Implementing the Domain Layer (08:21-12:15)
- Creating the Infrastructure Layer (12:16-16:40)
- Building the Application Layer (16:41-20:30)
- Integrating Real Weather API (20:31-24:15)
- Final Results & Key Takeaways (24:16-25:00)
Setting Up Cursor AI & Project Rules
The magic starts with proper setup. Instead of jumping straight into coding, the tutorial emphasizes creating a foundation that guides AI to write maintainable code.
Essential Setup Steps:
- Download Cursor AI from the official website (link provided in original video description)
- Install Super Whisper for voice commands to the AI
- Create comprehensive cursor rules that define your coding standards
- Establish clear folder structure and naming conventions
My Take:
This is brilliant. Most developers skip the rules setup and wonder why AI generates messy code. Taking time to define your preferences upfront saves hours of refactoring later.
Building the UI Interface First
Rather than starting with backend logic, the approach begins with the visual interface. This gives immediate feedback and helps clarify requirements.
UI Components Created:
- City selector/search functionality
- Weather icon display
- Temperature display with unit switching
- Weather condition indicators (sunny, cloudy, rainy)
- Humidity and wind data sections
- Celsius/Fahrenheit toggle button
The AI successfully created a weather page following the clean architecture folder structure: lib/weather/presentation/pages/weather_page.dart
My Take:
Starting with UI makes perfect sense. You can immediately see if the AI understands your requirements, and it’s easier to iterate on visual elements than complex business logic.
Implementing the Domain Layer
The domain layer comes next because it’s the most independent layer. This reduces the chance of AI hallucination and creates a solid foundation.
Domain Layer Components:
- Weather model using Freezed for immutability
- IWeatherService interface with clear method signatures
- Custom Result type for error handling
- WeatherException class extending Exception
- Comprehensive error types (network, city not found, server, unknown)
// Example of the clean interface structure
abstract class IWeatherService {
Future<Result<WeatherModel, WeatherException>> getWeatherForCity({
required String cityName,
});
Future<Result<WeatherModel, WeatherException>> getWeatherForCurrentLocation();
}
My Take:
I love how the tutorial emphasizes using named parameters and proper error handling from the start. These practices prevent technical debt that usually accumulates in rushed projects.
Creating the Infrastructure Layer
The infrastructure layer implements the domain interfaces and handles external data sources. The tutorial shows how to start with mock data before connecting real APIs.
Infrastructure Components:
- WeatherDto (Data Transfer Object) with JSON serialization
- Extension methods for converting DTOs to domain models
- WeatherService implementation with mock data initially
- Proper error handling and success/failure responses
The AI correctly separated concerns by creating DTOs for external data and keeping domain models clean. Extension methods provide elegant conversion between layers.
My Take:
Starting with mock data is genius. It allows you to test the entire flow before dealing with API complexities. Plus, it makes your code naturally testable.
Building the Application Layer
The application layer contains business logic and state management. The tutorial uses BLoC/Cubit pattern for predictable state management.
Application Layer Features:
- WeatherCubit with proper state management
- DataState wrapper for loading, success, and error states
- Temperature unit conversion logic
- Testable business logic separated from UI
- Clean dependency injection through constructors
The cubit manages weather data fetching, unit conversion, and error handling while keeping the UI reactive to state changes.
My Take:
The state management setup is textbook clean architecture. Having loading, success, and error states clearly defined makes the UI behavior predictable and user-friendly.
Integrating Real Weather API
The final step replaces mock data with OpenWeatherMap API integration. This demonstrates how clean architecture makes swapping data sources effortless.
API Integration Steps:
- OpenWeatherMap API key setup and configuration
- HTTP package integration for network requests
- Updated DTOs to match real API response structure
- Constants file for API endpoints and keys
- Error handling for network issues and API limitations
The beauty of this approach becomes clear here – changing from mock data to real API requires minimal changes because the architecture is properly layered.
My Take:
This is why clean architecture matters. The fact that switching data sources required almost no changes to business logic or UI proves the approach works.
Final Results & Key Takeaways
The final app displays real-time weather data for any city with proper error handling and smooth state transitions. Users can search for cities and toggle between temperature units seamlessly.
What the Final App Includes:
- Real-time weather data from OpenWeatherMap
- City search functionality (evolved from dropdown to search bar)
- Temperature unit conversion (Celsius/Fahrenheit)
- Humidity and wind speed display
- Proper loading states and error handling
- Clean, maintainable code structure
My Take:
The most impressive part? Zero lines of manually written code, yet the result follows industry best practices. This shows AI’s potential when guided properly with clear rules and architecture principles.
Important Lessons Learned:
The tutorial reveals that AI sometimes makes unexpected changes (like converting the city dropdown to a search bar). This highlights the importance of reviewing AI-generated code and providing clear, specific instructions.
The key to success with AI-assisted development isn’t just prompting – it’s setting up proper foundations with coding rules, clean architecture, and iterative refinement.
