Learning Paths

Choose a learning path that matches your experience level and goals. Each path provides a structured journey with clear milestones and practical exercises.

Choose Your Path

Beginner Path

New to AI coding? Start here for a gentle introduction to Cursor and Claude Code basics.

Duration: 2-3 weeks Prerequisites: Basic programming knowledge

Intermediate Path

Already coding? Level up with advanced features, workflows, and productivity techniques.

Duration: 3-4 weeks Prerequisites: 6+ months coding experience

Expert Path

Master enterprise features, complex architectures, and team collaboration strategies.

Duration: 4-6 weeks Prerequisites: Senior developer experience

Migration Path

Switching from another AI tool? Fast-track your transition with focused comparisons.

Duration: 1 week Prerequisites: Experience with AI tools

Beginner Path

2-3 weeks

Perfect for developers new to AI-assisted coding. Build confidence with fundamental concepts and basic workflows.

Week 1: Foundation

1. Day 1-2: Tool Selection & Setup

   • Read: Cursor vs Claude Code comparison
   • Choose your primary tool
   • Complete installation: Cursor or Claude Code
   • Configure basic settings

2. Day 3-4: First AI Interactions

   • Cursor: Learn Ask vs Agent modes
   • Claude Code: Understand task delegation
   • Complete first feature: Cursor or Claude Code

3. Day 5-7: Context Fundamentals

   • Master @ symbols (Cursor) or CLAUDE.md (Claude Code)
   • Practice including files and documentation
   • Exercise: Build a simple todo app with AI assistance

Week 2: Core Workflows

1. Day 8-10: Planning & Implementation

   • Learn PRD → Plan → Todo methodology
   • Practice breaking down features
   • Exercise: Plan and implement a user authentication feature

2. Day 11-12: Testing & Debugging

   • AI-assisted test writing
   • Debugging with AI help
   • Exercise: Write tests for your todo app

3. Day 13-14: Version Control

   • Git integration basics
   • AI-generated commit messages
   • Exercise: Create a GitHub repository with proper commits

Week 3: Productivity Boost

1. Day 15-17: Efficiency Techniques

   • Keyboard shortcuts mastery
   • Basic prompt engineering
   • Exercise: Refactor code using only keyboard shortcuts

2. Day 18-19: First MCP Server

   • Install filesystem MCP
   • Basic file operations
   • Exercise: Automate file organization task

3. Day 20-21: Review & Practice

   • Build a complete small project
   • Document your learning
   • Share in community forums

Milestone Project

Build a weather dashboard application:

• Fetch data from API
• Display current weather
• Show 5-day forecast
• Add location search
• Include error handling
• Write tests

Intermediate Path

3-4 weeks

Elevate your skills with advanced features and professional workflows.

Week 1: Advanced Features

1. Day 1-3: Deep Reasoning & Planning

   • Cursor: Agent mode mastery
   • Claude Code: /think and /ultrathink
   • Exercise: Architect a microservices system

2. Day 4-5: Multi-file Operations

   • Complex refactoring techniques
   • Cross-file coordination
   • Exercise: Refactor a monolithic app to modules

3. Day 6-7: Custom Configuration

   • Cursor: Rules directory setup
   • Claude Code: Advanced CLAUDE.md
   • Exercise: Create project-specific AI rules

Week 2: MCP Mastery

1. Day 8-10: Essential MCP Servers

   • Install and configure 5 core MCPs
   • Database connections
   • API integrations
   • Exercise: Build database-backed API

2. Day 11-12: Browser Automation

   • Puppeteer MCP setup
   • Web scraping workflows
   • Exercise: Automate testing workflow

3. Day 13-14: Custom MCP Development

   • Build your first MCP server
   • Integration patterns
   • Exercise: Create MCP for internal API

Week 3: Professional Workflows

1. Day 15-17: Team Collaboration

   • Shared rules and configurations
   • Code review with AI
   • Exercise: Set up team workspace

2. Day 18-19: Performance Optimization

   • Token usage optimization
   • Cost management strategies
   • Exercise: Optimize a costly workflow

3. Day 20-21: Advanced Debugging

   • Complex issue resolution
   • Performance profiling
   • Exercise: Debug performance issue

Week 4: Integration & Automation

1. Day 22-24: CI/CD Integration

   • Automated testing with AI
   • Deployment automation
   • Exercise: Complete CI/CD pipeline

2. Day 25-26: Documentation

   • Auto-generated docs
   • API documentation
   • Exercise: Document entire project

3. Day 27-28: Capstone Project

   • Build full-stack application
   • Implement all learned techniques
   • Present to community

Milestone Project

Build a task management system:

• Multi-user support
• Real-time updates
• REST and GraphQL APIs
• Comprehensive tests
• CI/CD pipeline
• Production deployment

Expert Path

4-6 weeks

Master enterprise-scale development and team leadership with AI.

Week 1-2: Enterprise Architecture

1. Large Codebase Management

   • Million+ LOC strategies
   • Monorepo management
   • Exercise: Analyze and refactor legacy system

2. Microservices & Distributed Systems

   • Service design with AI
   • Inter-service communication
   • Exercise: Design service mesh

3. Database Architecture

   • Complex schema design
   • Migration strategies
   • Exercise: Design multi-tenant database

Week 3-4: Security & Compliance

1. Security Implementation

   • Enterprise security standards
   • Threat modeling with AI
   • Exercise: Security audit

2. Compliance Automation

   • GDPR/HIPAA compliance
   • Audit trail implementation
   • Exercise: Compliance checklist automation

3. Cost Governance

   • Enterprise cost management
   • ROI measurement
   • Exercise: Implement cost controls

Week 5-6: Team Leadership

1. Team Enablement

   • Onboarding processes
   • Best practices documentation
   • Exercise: Create team playbook

2. Process Optimization

   • Workflow automation
   • Productivity metrics
   • Exercise: Optimize team workflow

3. Innovation Projects

   • AI-first architecture
   • Future-proofing systems
   • Exercise: Propose innovative solution

Milestone Project

Lead enterprise transformation:

• Migrate legacy system
• Implement microservices
• Ensure compliance
• Optimize costs
• Document everything
• Train team

Migration Path

1 week

Fast-track for developers switching from other AI tools.

Day 1-2: Tool Comparison

From Copilot

• Read: vs GitHub Copilot
• Key differences worksheet
• Feature mapping guide

From ChatGPT

• Read: vs ChatGPT
• Workflow translation
• Integration patterns

Day 3-4: Skill Transfer

1. Map existing workflows

   • Identify current patterns
   • Find equivalent features
   • Practice translations

2. Advanced features

   • Features not in previous tool
   • New possibilities
   • Efficiency gains

Day 5-7: Full Migration

1. Migrate active project

   • Set up configurations
   • Transfer customizations
   • Implement workflows

2. Optimize and improve

   • Leverage new capabilities
   • Measure improvements
   • Share experience

Specialized Paths

Frontend Developer Path

2 weeks

Focus on UI/UX development with AI:

• Component generation
• Design system integration
• Responsive layouts
• Accessibility compliance
• Performance optimization

Backend Developer Path

2 weeks

Master server-side development:

• API design and implementation
• Database optimization
• Microservices patterns
• Security best practices
• Scalability solutions

DevOps Engineer Path

2 weeks

Infrastructure and automation focus:

• IaC with AI assistance
• CI/CD pipeline optimization
• Container orchestration
• Monitoring setup
• Incident response

Data Engineer Path

2 weeks

Data pipeline and analytics:

• ETL pipeline design
• Data modeling
• Query optimization
• Visualization automation
• ML pipeline integration

Learning Resources

Practice Exercises

Coding Challenges AI-assisted problem solving

Project Ideas Real-world applications

Workflow Templates Ready-to-use patterns

Study Materials

1. Video Courses

   • Official YouTube channels
   • Community tutorials
   • Conference talks

2. Reading Lists

   • Blog posts
   • Case studies
   • Technical papers

3. Interactive Labs

   • Hands-on exercises
   • Sandbox environments
   • Guided tutorials

Tracking Progress

Self-Assessment Checklist

Beginner Milestones:

• Completed first AI-assisted feature
• Used context effectively
• Written AI-generated tests
• Managed git with AI
• Built complete small app

Intermediate Milestones:

• Mastered advanced modes
• Configured custom rules
• Integrated 3+ MCP servers
• Optimized token usage
• Led code review with AI

Expert Milestones:

• Managed 100k+ LOC project
• Implemented enterprise security
• Designed distributed system
• Trained team on AI tools
• Achieved measurable ROI

Certification Path

Coming soon:

• Official certification program
• Skill assessments
• Badge system
• Career pathways

Tip

Remember: Learning AI-assisted development is a journey, not a destination. The tools evolve rapidly, so continuous learning is key to staying effective.

Next Steps

After completing your chosen path:

1. Share your journey in community forums
2. Mentor others starting their path
3. Contribute to documentation and tools
4. Stay current with updates and features
5. Innovate with new workflows and techniques

Ready to start? Pick your path above and begin your journey to AI-assisted development mastery! 🚀