# How to Use This Implementation Plan with AI Coding Assistants

1. **Save this plan** as a markdown (.md) file in your project directory
2. **Open the file** in your preferred code editor with Cursor or Windsurf AI assistant
3. **Copy and paste** the following prompt to your AI assistant:

   "I want to implement this project following the implementation plan in this file. Let's start with the first unchecked item. For each step: 1) Ask me any clarifying questions if something is unclear before proceeding, 2) Explain what needs to be done, 3) Write the necessary code, 4) Check the box when complete (change `- [ ]` to `- [x]`), then 5) Move to the next unchecked item. After completing logical groups of steps, commit the changes with a descriptive message. Let's begin with step #1."

4. **Track your progress** by checking off completed items as you go
5. **Commit regularly** after logical groups of steps are completed

Implementation Plan for LoopLife

Project Architecture Overview

LoopLife will consist of a frontend built with React.js and React Native, a backend powered by Node.js and Express, and PostgreSQL for database management. AI functionalities will be implemented using AWS SageMaker. The architecture will ensure modularity for scalability and maintainability while adhering to best practices for software security and user data privacy.

Phase 1: Project Setup

• [ ] 1. Create a new Git repository for LoopLife
• [ ] 2. Initialize the project with npm init and create package.json with basic metadata
• [ ] 3. Create a .gitignore file with entries for node_modules and environment variables
• [ ] 4. Create a README.md file describing the project, installation, and usage instructions
• [ ] 5. Create a src/ directory for the frontend source code
• [ ] 6. Create a backend/ directory for the backend source code
• [ ] 7. Create a tests/ directory for unit and integration tests
• [ ] 8. Install React and React DOM: npm install react react-dom
• [ ] 9. Install React Native for the mobile interface: npm install react-native
• [ ] 10. Install backend dependencies: npm install express pg dotenv cors
• [ ] 11. Install development dependencies: npm install --save-dev webpack webpack-cli babel-loader @babel/core @babel/preset-env @babel/preset-react
• [ ] 12. Create webpack.config.js with basic configuration for bundling frontend code
• [ ] 13. Create a .babelrc file with React and environment presets
• [ ] 14. Create src/index.js as the main entry point of the React app
• [ ] 15. Create backend/index.js as the entry point for the Express server
• [ ] 16. Create a basic CI workflow in .github/workflows/ci.yml for continuous integration
• [ ] 17. Write a unit test to verify the project setup using Jest

Phase 2: User Registration Feature

• [ ] 18. Create src/components/auth/ directory for authentication components
• [ ] 19. Create src/components/auth/SignupForm.js component with a skeleton structure
• [ ] 20. Add an email input field to SignupForm.js
• [ ] 21. Add a password input field to SignupForm.js
• [ ] 22. Add a submit button to SignupForm.js
• [ ] 23. Implement form validation for the email field in SignupForm.js
• [ ] 24. Implement form validation for the password field in SignupForm.js
• [ ] 25. Write unit tests for email validation in SignupForm.test.js
• [ ] 26. Write unit tests for password validation in SignupForm.test.js
• [ ] 27. Create backend/routes/auth.js for authentication routes
• [ ] 28. Implement user registration API endpoint in auth.js
• [ ] 29. Write unit tests for user registration API in auth.test.js
• [ ] 30. Connect SignupForm submit handler to the API call in SignupForm.js

Phase 3: Daily Insights Dashboard Feature

• [ ] 31. Create src/components/dashboard/ directory for dashboard components
• [ ] 32. Create src/components/dashboard/Dashboard.js with layout structure
• [ ] 33. Query the backend for user emotional data and present it in a visual format
• [ ] 34. Create a simple chart component to display emotional trends in Dashboard.js
• [ ] 35. Add a “Check In” button to access the daily emotion check-in feature within Dashboard.js
• [ ] 36. Implement state management with React Context API for global user data
• [ ] 37. Write unit tests for Dashboard.js to verify correct rendering and data presentation
• [ ] 38. Implement user feedback feature in the dashboard for gathering insights and improvements

Phase 4: AI-Driven Reflection Prompts

• [ ] 39. Create a file src/services/aiService.js for managing AI prompts
• [ ] 40. Build an API call to AWS SageMaker for generating reflection prompts based on user data
• [ ] 41. Write unit tests for AI prompt generation function in aiService.test.js
• [ ] 42. Create a function in Dashboard.js to handle fetching and displaying AI-driven prompts
• [ ] 43. Implement logic to connect daily emotional check-in results to AI service for personalized prompts

Phase 5: Community Forum Feature

• [ ] 44. Create src/components/forum/ directory for forum components
• [ ] 45. Create src/components/forum/Forum.js for displaying community posts
• [ ] 46. Implement authentication checks before allowing users to access the Forum
• [ ] 47. Add functionality for users to create new posts in the forum
• [ ] 48. Add a comment section under each forum post for user discussions
• [ ] 49. Write integration tests for the forum functionality, validating post creation and comment submissions

Phase 6: Testing & Quality Assurance

• [ ] 50. Set up Jest and React Testing Library for unit and integration tests
• [ ] 51. Write integration tests for user registration feature, covering all edge cases
• [ ] 52. Conduct usability testing sessions with target users, gathering feedback for improvements
• [ ] 53. Implement basic user analytics integration to track user engagement metrics

Phase 7: Deployment

• [ ] 54. Prepare the AWS setup for application hosting, including EC2 and RDS
• [ ] 55. Configure environment variables and secret management on AWS
• [ ] 56. Set up Docker containerization for both frontend and backend components
• [ ] 57. Write deployment scripts for CI/CD pipeline with GitHub Actions
• [ ] 58. Deploy the MVP to production on AWS and monitor the performance

Phase 8: Post-Launch

• [ ] 59. Collect user feedback through in-app surveys and community interactions
• [ ] 60. Analyze engagement metrics from user analytics, making iterative improvements
• [ ] 61. Plan for future features based on user demand and feedback gathered during the MVP phase

This detailed implementation plan will guide the development of LoopLife, ensuring that every step is clear, actionable, and focused on delivering key functionalities that align with the project objectives.