Building AI Harvest Timesheet: My Journey with Cursor IDE

As a developer who values both productivity and accuracy in time tracking, I embarked on a journey to create AI Harvest Timesheet, an innovative application that bridges the gap between Git commits and Harvest time entries. In this blog post, I'll share my experience building this application using Cursor, a cutting-edge IDE that made the development process smoother and more efficient.

The Project Vision

The idea was born from a common pain point many developers face: maintaining accurate time records while staying focused on coding. I envisioned an application that would:

  • Automatically convert Git commits into Harvest time entries
  • Intelligently distribute hours across commits
  • Provide a modern, user-friendly interface
  • Handle multiple repositories seamlessly
  • Integrate smoothly with Harvest's API

Why Cursor?

Cursor proved to be an invaluable tool throughout the development process. Its AI-powered features and intuitive interface significantly enhanced my development workflow. Here's how Cursor helped at various stages:

 

1. Project Setup and Structure

Cursor's intelligent code suggestions helped me set up the project structure efficiently. The application is built with Electron, and Cursor's understanding of the framework made it easier to:

  • Set up the initial boilerplate
  • Organize the codebase into logical components
  • Manage dependencies effectively

2. Feature Implementation

Some of the key features where Cursor's AI capabilities shined include:

 

Smart Git Integration

Cursor helped me implement complex Git operations with its context-aware code completion and documentation integration. The IDE's suggestions were particularly helpful when working with:

  • Repository management
  • Commit history processing
  • Branch pattern recognition

Harvest API Integration

When implementing the Harvest API integration, Cursor's ability to understand API documentation and suggest relevant code patterns made the process much smoother. This included:

  • API authentication
  • Project and task synchronization
  • Time entry creation

User Interface Development

The modern Material UI implementation was streamlined thanks to Cursor's:

  • Component suggestions
  • CSS assistance
  • Layout optimization hints

 

3. Error Handling and Testing

Cursor's AI capabilities were instrumental in:

  • Identifying potential edge cases
  • Suggesting robust error handling patterns
  • Helping write comprehensive tests

Key Technical Challenges and Solutions

1. Time Distribution Algorithm

One of the most complex aspects was developing an algorithm for intelligent hour distribution. Cursor helped by:

  • Suggesting optimal data structures
  • Identifying edge cases in the distribution logic
  • Providing relevant mathematical implementations

2. Multi-Repository Management

Managing multiple Git repositories required careful state management. Cursor assisted with:

  • Efficient data storage patterns
  • Concurrent operation handling
  • Repository settings persistence

3. Webhook Integration

Implementing webhook support for custom message formatting was made easier with Cursor's:

  • API endpoint suggestions
  • JSON parsing implementations
  • Error handling patterns

Lessons Learned

Throughout this project, I learned several valuable lessons:

  1. AI-Powered Development: Cursor's AI capabilities significantly reduced development time and improved code quality.

  2. Code Organization: The importance of maintaining a clean, modular codebase was reinforced by Cursor's suggestions.

  3. Documentation: Cursor helped maintain comprehensive documentation alongside the code.

Conclusion

Building AI Harvest Timesheet with Cursor was an enlightening experience that showcased the power of AI-assisted development. The final product is a robust, user-friendly application that solves a real problem for developers.

The combination of Cursor's intelligent features and my vision for automated time tracking resulted in an application that I'm proud of and that others find valuable.

For developers interested in trying out AI Harvest Timesheet or contributing to its development, you can find the project at GitHub repository link.