Building a Modern Blog Platform: My Journey with AI Pair Programming

Luiz Cavalieri -

The decision to build a technical blog often leads developers down the familiar path of WordPress or static site generators. However, my recent project took an unconventional turn when I explored the intersection of modern DevOps practices and a modern blogging platform that helps scale with the assistance of AI. This post details my experience building a Ghost CMS platform with the help of two AI models: Claude 3.5 Sonnet and ChatGPT 4.

The Architecture Decision

The traditional blogging platforms didn't quite align with what I was looking for: a modern, maintainable system that could serve as both a learning platform and a practical example of contemporary DevOps practices. After evaluating several options, I settled on Ghost CMS for its Node.js foundation, clean architecture, and developer-friendly approach.

The technical stack emerged from a series of deliberate choices:

  • Ghost CMS: Offering a modern publishing platform without the complexity of WordPress
  • Docker: Ensuring consistency across environments and simplified deployments
  • AWS EC2: Providing a flexible hosting environment with good cost control
  • Terraform: Managing infrastructure through code, enabling repeatable deployments
  • GitHub Actions: Automating the CI/CD pipeline
  • NGINX: Handling reverse proxy and SSL termination
  • MySQL: Providing reliable data persistence
  • Let's Encrypt: Managing SSL certificates automatically

Exploring AI-Assisted Development

What started as a straightforward infrastructure project evolved into an interesting experiment in AI-assisted development. Claude and ChatGPT served distinct roles in the development process, often unexpectedly complementing each other.

Infrastructure Design and Implementation

Claude proved particularly adept at infrastructure-related tasks. During the Terraform configuration phase, it helped identify potential issues with security group rules and suggested improvements to the EC2 instance provisioning process. Here's an example of how the interaction enhanced the development process:

When dealing with SSL certificate automation, Claude proposed a more elegant solution than my initial manual approach:

resource "null_resource" "https_setup" {
  triggers = {
    instance_id = local.instance_exists ? data.aws_instance.existing_instance[0].id : aws_instance.ghost_server[0].id
    domain = var.ghost_domain
    nginx_setup_id = null_resource.nginx_setup.id
  }
  
  # Implementation details that handled both initial setup and renewal
}

Deployment Pipeline Optimization

ChatGPT's strength lies in optimizing the deployment pipeline. It suggested several improvements to the GitHub Actions workflow, particularly around Docker layer caching and efficient theme deployment strategies. The resulting workflow significantly reduced deployment times and improved reliability.

Technical Challenges and Solutions

Database Persistence and Backup Strategy

One of the first significant challenges was implementing a robust database backup solution. The initial setup lost data during container updates – a critical issue for a production blog. The solution involved:

  1. Implementing proper volume mounting for MySQL data
  2. Creating an automated backup system using shell scripts
  3. Setting up a recovery testing protocol
# Backup script excerpt
tar -czf "$BACKUP_DIR/ghost_content_$TIMESTAMP.tar.gz" -C app/data content
docker-compose exec db mysqldump -u root -p"$MYSQL_ROOT_PASSWORD" ghostdb > "$BACKUP_DIR/ghost_db_$TIMESTAMP.sql"

Theme Deployment Automation

Theme deployment presented another interesting challenge. The solution evolved into a scripted process that handles the following:

  • Theme compression and transfer
  • Permission management
  • Ghost service restart
  • Cleanup operations

This automation eliminated several manual steps and reduced the chance of human error during deployments.

Lessons in Modern Development Practices

Infrastructure as Code Evolution

The project reinforced the value of infrastructure as code but also revealed some nuanced learning points:

  1. State management in Terraform requires careful consideration, especially when dealing with resources needing manual intervention.
  2. Separating the infrastructure into logical modules early pays dividends as the system grows.
  3. Using data sources effectively can make your Terraform configurations more resilient.

Container Orchestration Insights

Working with Docker in both development and production environments yielded valuable insights:

  1. The importance of multi-stage builds for optimization
  2. Effective strategies for secret management
  3. Volume management best practices for stateful applications

The Role of AI in Modern Development

What stands out from this experience is how AI tools are evolving from simple code completion to becoming effective development partners. They excel at:

  1. Providing alternative approaches to architectural decisions
  2. Identifying potential security concerns
  3. Optimizing existing configurations
  4. Debugging complex infrastructure issues

However, it's crucial to understand their limitations. They work best when you approach them with well-defined problems and verify their suggestions against established best practices.

Future Improvements

The current implementation has room for several enhancements:

  1. Performance Optimization
    • CDN integration for static assets
    • Redis caching layer implementation
    • Image optimization pipeline
  2. Monitoring and Observability
    • Comprehensive CloudWatch metrics
    • Error tracking integration
    • Performance monitoring
  3. Content Workflow
    • Staging environment for content preview
    • Automated content backup to S3
    • Custom editorial workflows

Conclusion

Building this blog has involved combining modern DevOps practices with emerging AI capabilities. The result is a robust, maintainable platform that serves as both a publishing tool and a practical example of contemporary development practices.

The code is available on GitHub for those interested in implementing similar solutions or contributing improvements. I welcome discussions about your experiences with AI-assisted development and modern DevOps practices.

This post is part of a series on modern development practices. Follow along as we explore more technical implementations and DevOps strategies.