Building a Modern Tech Blog with AI: A Journey with Cursor and Claude

Building a modern tech blog used to mean weeks of setup, configuration, and debugging. With AI assistance through Cursor IDE and Claude 3.5/4 Sonnet, I built this entire platform in days, not weeks. Here’s how AI transformed my development workflow and what we built together.

The AI Development Experience

Working with Cursor and Claude felt like pair programming with an expert developer who never gets tired, never judges your questions, and always suggests improvements. Instead of googling syntax or debugging configuration files for hours, I could focus on the creative aspects of building.

What AI Handled:

• ⚡ Configuration files and complex setups
• 🐛 Debugging and error resolution
• 📝 Code suggestions and optimizations
• 🔧 Best practices implementation
• 📚 Documentation and explanations

What I Focused On:

• 🎨 Design decisions and user experience
• 🏗️ Architecture and feature planning
• 📖 Content creation and strategy
• 🚀 Performance optimization goals

Technology Stack

The AI helped me choose and configure a modern stack that prioritizes performance, developer experience, and maintainability:

Project Architecture

Frontend: Astro with Strategic React

AI suggested Astro for its “islands” architecture - perfect for a content-focused site. Pages load with zero JavaScript by default, then selectively add interactivity where needed.

// AI-generated component structure
export const VisitCounter: React.FC<{ postPath: string }> = ({ postPath }) => {
  const [count, setCount] = useState<number | null>(null);

  useEffect(() => {
    // AI suggested this error-handling pattern
    const fetchCount = async () => {
      try {
        const response = await fetch(`/api/visit-counter${postPath}`, {
          method: 'POST',
        });
        if (response.ok) {
          const data = await response.json();
          setCount(data.total);
        }
      } catch (error) {
        console.warn('Visit counter unavailable:', error);
      }
    };

    fetchCount();
  }, [postPath]);

  return count ? (
    <div className={styles.counter}>
      👁️ {count.toLocaleString()} views
    </div>
  ) : null;
};

Backend: Cloudflare Workers + KV

AI recommended Cloudflare’s edge infrastructure for global performance. When I mentioned wanting visit counters and analytics, it immediately suggested KV storage and provided the complete implementation:

// AI-generated serverless function
export const onRequest: PagesFunction<Env> = async (context) => {
  try {
    const { request, env } = context;
    const path = `/blog/${context.params.path}`;

    // AI suggested this error-handling pattern
    if (!env.VISIT_COUNTS) {
      throw new Error('VISIT_COUNTS KV namespace is not bound');
    }

    // Get and increment count
    let currentCount: VisitCount = (await env.VISIT_COUNTS.get(path, 'json')) || { total: 0 };

    const newCount = { total: currentCount.total + 1 };
    await env.VISIT_COUNTS.put(path, JSON.stringify(newCount));

    return new Response(JSON.stringify(newCount), {
      headers: {
        'Content-Type': 'application/json',
        'Access-Control-Allow-Origin': 'https://blog.radaideh.info',
      },
    });
  } catch (error) {
    // AI added comprehensive error handling
    console.error('Visit counter error:', error);
    return new Response(JSON.stringify({ error: 'Internal server error' }), {
      status: 500,
      headers: { 'Content-Type': 'application/json' },
    });
  }
};

AI-Powered Development Workflow

The development process showcased AI’s strength in automation and optimization:

How AI Accelerated Development

Configuration Management: Instead of spending hours debugging YAML syntax, AI generated complete GitHub Actions workflows with proper error handling and optimization.

Code Quality: AI automatically suggested TypeScript interfaces, error boundaries, and best practices I wouldn’t have thought of:

// AI-suggested type safety patterns
interface Env {
  VISIT_COUNTS: KVNamespace;
  LIKES: KVNamespace;
}

interface VisitCount {
  total: number;
}

// AI added this validation function
const validateEnvironment = (env: Env): void => {
  if (!env.VISIT_COUNTS) {
    throw new Error('VISIT_COUNTS KV namespace not configured');
  }
};

Performance Optimization: When I mentioned slow builds, AI immediately suggested:

• Parallel job execution
• Smart dependency caching
• Build artifact reuse
• Result: 80% faster CI/CD pipeline (15 minutes → 3 minutes)

Key Features Built with AI

📊 Real-time Analytics

AI suggested and implemented visit counters using Cloudflare KV, complete with error handling and CORS configuration.

💬 GitHub-Powered Comments

AI recommended Giscus for comments and provided the complete integration code.

🚀 Optimized CI/CD

AI designed a 6-stage pipeline with intelligent caching and parallel execution.

🌐 Global Edge Deployment

AI configured Cloudflare Pages deployment with custom domains and environment management.

Results: What AI Made Possible

Development Speed:

• Traditional approach: 2-3 weeks
• AI-assisted: 3-4 days

Code Quality:

• Zero runtime errors in production
• 100% TypeScript coverage
• Comprehensive error handling

Performance Achievements:

• ⚡ 95+ Lighthouse scores across all metrics
• 🚀 Sub-second page loads globally
• 📱 Perfect mobile experience
• ⚙️ 80% faster build pipeline

Features Delivered:

• Static-first architecture with selective hydration
• Real-time visit tracking and analytics
• GitHub-integrated comment system
• Automated deployment and monitoring
• Enterprise-grade security implementation

The AI Advantage

Working with AI didn’t replace my engineering skills - it amplified them. I could focus on:

Strategic Decisions: What features to build, how to structure the user experience Creative Problem-Solving: Novel approaches to content management and interactivity
Architecture Planning: High-level system design and performance goals

While AI handled:

Implementation Details: Configuration syntax, boilerplate code, error handling Best Practices: Security patterns, performance optimizations, type safety Debugging: Root cause analysis and solution suggestions

Lessons Learned

1. AI is a Force Multiplier: It doesn’t replace developer skills but dramatically accelerates implementation
2. Focus on Intent: Describe what you want to achieve; let AI figure out the how
3. Iterate Quickly: AI enables rapid prototyping and experimentation
4. Trust but Verify: AI suggestions are usually excellent, but understanding the code remains crucial

What’s Next

This project demonstrates what’s possible when human creativity meets AI capability. Future improvements will continue leveraging AI for:

• Enhanced Search: Semantic search implementation
• Content Optimization: AI-powered content suggestions
• Performance Monitoring: Intelligent alerting and optimization
• User Experience: Personalized content recommendations

Conclusion

Building this blog with AI assistance wasn’t just faster - it was more enjoyable. Instead of fighting configuration files and debugging obscure errors, I spent time on design, content, and user experience. The result is a production-ready platform that would have taken weeks to build traditionally.

The complete source code is available on GitHub, showcasing how AI can transform modern web development. This isn’t just about automation - it’s about fundamentally changing how we approach building software.

The future of development isn’t human vs. AI - it’s human with AI. And that future is incredibly exciting.