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Building a Full-Stack AI-Powered Web Application

In this session, Ryan Booth demonstrated how to build a complete social media management tool for artists using an AI-driven development approach. The presentation showcased both the capabilities and current limitations of using AI as a primary development tool.

Project Overview​

Technology Stack​

  • Frontend: React, Next.js
  • Backend: FastAPI
  • Task Queue: Celery
  • Cache/Message Broker: Redis

AI-Driven Development Approach​

Key Principles​

  1. Pure AI Development

    • All code generated through AI prompts
    • No manual code editing
    • Focus on prompt engineering and architecture design

  2. Testing and Validation

    • Automated test generation
    • AI-driven debugging processes
    • Continuous validation of generated code

Implementation Process​

  1. Architecture Design

    • System component definition
    • Technology stack selection
    • Integration planning

  2. Core Features

    • Social media platform integration
    • Content management system
    • Automated posting capabilities
    • Analytics dashboard

  3. Advanced Implementations

    • OAUTH integration for social platforms
    • Automated testing suite
    • Performance optimization
    • Security implementations

Challenges and Solutions​

AI Limitations​

  1. Context Retention

    • Challenge: AI models losing context during long development sessions
    • Solution: Breaking down tasks into smaller, focused components
    • Documentation of context for each development phase

  2. Best Practices Enforcement

    • Challenge: Maintaining consistent coding standards
    • Solution: Implementing strict prompt templates
    • Regular code review checkpoints

  3. Integration Complexity

    • Challenge: Managing multiple service integrations
    • Solution: Modular development approach
    • Comprehensive testing strategy

Best Practices​

Prompt Engineering​

  1. Clear Architecture Definition

    • Detailed system requirements
    • Component relationships
    • Expected behaviors

  2. Iterative Development

    • Small, focused feature requests
    • Regular validation steps
    • Incremental improvements

  3. Documentation

    • Automated documentation generation
    • Code comment requirements
    • Architecture diagrams

Lessons Learned​

  1. AI Capabilities

    • Effective for standard patterns
    • Struggles with complex business logic
    • Requires clear context and boundaries

  2. Development Efficiency

    • Faster initial development
    • Additional time needed for validation
    • Balance between AI and human oversight

  3. Future Improvements

    • Better context management
    • Enhanced testing strategies
    • Improved error handling

Resources​