Can One Person Really Develop Entire Projects? 200% AI Utilization with CDK + Lambda + Cursor

Development environment managing everything from infrastructure to frontend with monorepo structure

🤦‍♂️ How I Used to Develop in the Past

Problem: Creating one repository per Lambda function

10 projects = 10 repositories
Copy-paste hell for common code
Jumping between 10 repositories every deployment

Current: Managing everything in one project with CDK + Lambda + Cursor

IaC code, server code, frontend code, demo pages all in one place
AI understands the entire context and assists development
One person can develop entire projects

// Now managing everything in one project like this
project/
├── infrastructure/     # CDK code
├── lambda-functions/   # Server logic
├── frontend/          # Frontend
├── demo-pages/        # Demo pages
└── docs/              # Rulebook and guides

🚀 The Magic of Simultaneous Development Speed and Maintainability Improvement

200% Development Speed Improvement

The Power of AI Context Sharing

Cursor understands the entire project structure
Auto-generates server code by looking at infrastructure code
Auto-generates frontend integration code by looking at server API
Quickly adds new features with consistent patterns

Real Experience: Adding one new API

Define Lambda function in CDK (30 seconds)
Cursor generates server code based on existing patterns (1 minute)
Auto-generates frontend integration code (1 minute)
Deployment script follows existing patterns (30 seconds)

Total: 3 minutes. Used to take at least 30 minutes before.

Dramatically Improved Maintainability

Code Consistency Assurance

// All Lambda functions use the same pattern
export const handler = async (event: APIGatewayProxyEvent) => {
  try {
    // Apply common middleware
    const result = await processRequest(event);
    return successResponse(result);
  } catch (error) {
    return errorResponse(error);
  }
};

Simplified Version Control

Track all changes in one repository
Component-based folder structure instead of feature-based branches
Deployment can be done all at once or selectively

💡 But There Were These Challenges Too

Biggest Challenge: Rulebook Management

The Trap of Massive Source Code

Too complex for AI to understand the entire project
AI repeating past trial-and-error mistakes
Inconsistent code patterns confusing AI

Solution: Systematic Rulebook Creation

# Project Rulebook (docs/rulebook.md)

## 1. Lambda Function Writing Rules
- All functions use common/middleware.ts
- Error handling uses standardError class
- Environment variables managed in config/environment.ts

## 2. CDK Infrastructure Patterns
- Lambda functions use constructs/lambda-construct.ts
- API Gateway paths unified in kebab-case
- Project tags mandatory for all resources

## 3. Prohibited Actions
- Direct AWS SDK calls prohibited (use wrapper functions)
- Hardcoded ARNs prohibited (use CDK references)
- Use structured logging instead of console.log

When this rulebook is properly recognized by Cursor, AI develops with consistent patterns.

🎯 Now One Person Can Handle Entire Projects

Benefits of MSA Design + Monorepo Management

Separated Design, Integrated Management

Category	Old Method	Current Method
Repository	Function-based separation	Project integration
Deployment	Individual deployment	Selective batch deployment
Code Reuse	Copy-paste	Common modules
AI Utilization	Limited	Full context
Development Speed	Slow	Fast

Actual Project Structure

my-fullstack-project/
├── cdk/
│   ├── lib/
│   │   ├── api-stack.ts      # API Gateway + Lambda
│   │   ├── frontend-stack.ts  # S3 + CloudFront
│   │   └── database-stack.ts  # DynamoDB
│   └── bin/app.ts
├── lambdas/
│   ├── user-service/
│   ├── auth-service/
│   └── common/               # Common utilities
├── frontend/
│   ├── src/
│   └── dist/
├── demo/
│   └── landing-page/
└── docs/
    ├── rulebook.md           # Rulebook for AI
    └── architecture.md

🔧 Development Workflow with Cursor

Process of Adding New Features

Requirements Definition (1 minute)
- “Create user profile lookup API”
Cursor Auto-generates (2 minutes)
- Add Lambda function to CDK stack
- Implement Lambda function (based on rulebook)
- Generate frontend integration code
Deploy and Test (2 minutes)
- npm run deploy
- Test immediately on demo page

Total: 5 minutes. This is the power of 200% AI utilization.

Token Consumption Optimization Tips

What I Learned Using Ultra Version

Well-made rulebook prevents AI from wandering
Include only relevant files in context window
Register frequently used patterns as snippets

// Register frequently used Lambda function template as snippet
const lambdaTemplate = `
export const handler = async (event: APIGatewayProxyEvent) => {
  // Standard pattern based on rulebook
};
`;

💡 Conclusion: Solo Fullstack Development Became Reality

Summary of Benefits

200% development speed improvement
Dramatically improved maintainability
Consistent code quality through AI context sharing
One person can develop entire projects

Precautions

Rulebook management is key
Initial structure design requires time investment
Consider token consumption (Ultra version recommended)

Developing this way really makes a difference in productivity. I’m curious about how others with similar experiences manage their projects!

If you have better tips, please share them in the comments 🙏