Building a Database Patterns Explorer with Claude & Cursor

During a recent consulting project, I needed to recommend a database layer refactoring strategy to a client. While I had a solution in mind, I wanted to explore multiple approaches and include comparative analysis in my report.

The Power of Single-Purpose Tools

Before diving into the details, I want to highlight something important: this project represents my philosophy of quickly building targeted tools that solve specific problems. For technical and non-technical folks alike, there's immense value in creating small, single-purpose applications when you need them.

The tool I'm about to describe isn't perfect - it has its quirks and rough edges - but it's functional and helped me solve a real problem. As Voltaire famously said, "Perfect is the enemy of good." For single-use tools, having something that works today is infinitely more valuable than a perfect solution that never ships.

The Research Process

I decided to conduct a "Deep Research" experiment comparing two AI approaches:

1. Perplexity Deep Search: A specialized AI research tool
2. Claude Desktop with MCP: Using Claude 3 Opus with sequential thinking and Tavily Search

After running both experiments with identical prompts asking for database access patterns suitable for Node.js projects with SQL, I found Claude's responses more comprehensive and well-structured. Claude provided:

• More thorough pattern descriptions
• Better comparative analysis
• More nuanced implementation considerations for each framework
• Clearer trade-offs between patterns

From Research to Visualization

With the research complete, I needed a way to visualize and compare these patterns. Here's the process I followed:

1. Research Synthesis: Asked Claude to identify the 12 most relevant patterns for SQL-based Node.js applications

2. Visualization Planning: Prompted Claude to design a data structure and interaction model for comparing patterns based on:

   • Abstraction level
   • Separation of concerns
   • Complexity
   • Scalability
   • Implementation considerations for Next.js and Remix

3. Artifact Creation: Had Claude generate a self-contained React component with:

   • Interactive filter controls
   • Pattern detail views
   • SVG architecture diagrams
   • Grid and list viewing options

Integration with Cursor IDE

The artifact from Claude was functional but needed integration into my site structure. I used Cursor IDE to:

1. Refactor the component into a proper site structure:

   components/database-patterns/
   ├── data.ts              # Pattern data and types
   ├── explorer.tsx         # Main UI component  
   ├── pattern-diagram.tsx  # Diagram selection component
   ├── diagrams/            # Individual SVG diagrams
   │   ├── repository.tsx
   │   ├── active-record.tsx
   │   └── ... (10 more patterns)
   └── README.md            # Documentation
   

2. Apply site styling and UI components:

   • Use shadcn/ui components
   • Follow site color scheme and design patterns
   • Ensure responsive layout

3. Optimize SVG diagrams:

   • Break out into individual components
   • Improve accessibility with ARIA labels
   • Fix rendering issues

Embracing the Imperfect

Let me be candid - the current implementation has some warts. The SVG diagrams could be more polished, some of the filtering logic could be optimized, and there are edge cases I haven't addressed.

But here's the thing: this tool solved my immediate problem. It helped me visualize database patterns, communicate options to my client, and ultimately make better recommendations. Had I waited until everything was perfect, I might still be working on it while the client opportunity passed by.

This pragmatic approach is especially powerful for technical and non-technical users alike. Anyone can leverage today's AI tools to quickly build single-use applications that solve specific problems without getting bogged down in perfectionism.

Results & Lessons

The final Database Patterns Explorer provides an interactive way to compare different database access patterns. The experience highlighted several insights:

1. AI-enhanced research can provide a thorough overview of technical options and trade-offs

2. Multi-step AI processes work best when:

   • Breaking tasks into research, planning, and implementation phases
   • Using the right AI capabilities for each phase
   • Maintaining human direction and quality control

3. Team collaboration between AI systems can be effective, with:

   • Claude excelling at deep research and initial artifact creation
   • Cursor IDE streamlining integration and refactoring tasks

4. Shipping beats perfecting - creating something functional quickly provides immediate value and practical learning opportunities

This approach saved me significant time in both the research and development process, allowing me to focus on the higher-level analysis and recommendations for my client.

Try It Yourself

I encourage you to consider where in your own work a quick, single-purpose tool might save you time and provide clarity - even if it's not perfect.