Building on Solana with AI coding assistants isn't about typing 10 words and getting a full application. It's about structuring your inference process with good engineering practices. This guide shows you how to "vibe code" effectively on Solana using TDD and modern AI tools.

Step 1: Setup Your AI-Powered Development Environment

Having the right tools makes all the difference. Here's what you need (expect this list to evolve rapidly):

Essential Tools

• Powerful AI Models:

  • Gemini 2.5
  • Claude 4
  • OpenAI O1-O3
  • DeepSeek v3/R1
  • Note: COT (Chain of Thought) models work better for planning

• Solana Development Stack: Install the complete Solana toolchain on your machine

• AI-Enabled Code Editors: Choose one with agent and MCP support:

  • Zed
  • Cursor
  • Cline
  • Claude Code
  • Roo

• MCP Integrations: Install one or both:

  • Context7 MCP
  • Solana MCP

Pro Tip: Understand Your Stack

You can't AI your way around every error. Understanding your technology stack yields better results:

• Read documentation thoroughly
• Use Notebook LM for reviewing complex concepts
• Build some programs manually first

Step 2: Setting Up for Success

With your tools ready, the key is steering the automation effectively.

Project Initialization

anchor init your-project-name

Architecture-First Approach

1. Generate Architecture: Start your AI model without context and generate a high-level architecture
2. Review Globally: Don't write a PRD yet - review the architecture from a bird's eye view
3. Generate PRD: Use Context7 or Solana MCP to generate a PRD with the latest docs
4. Create Task List: Break down the PRD into manageable tasks
5. Focus on POC: Keep documents focused on proof-of-concept, avoiding enterprise-level complexity

Organize Your Project

Create a docs/ folder containing:

• Architecture overview
• PRD document
• Task breakdown

Configure Your AI Agent

Set up agent rules and files to help the AI understand patterns quickly:

• Use claude /init or similar commands
• Create a basic memory system for consistent conversations
• Ensure the agent adapts to your project's patterns

Step 3: TDD Implementation Loop

Why TDD? Code generation is essentially text prediction. TDD ensures you reach an acceptable state and acts as specifications for your coding agent.

For Each Task:

1. Refresh Context: Ensure the coding agent reads library docs fresh
2. Start with Tests: Write tests first, following TDD principles
3. Implement: Let the AI implement until tests pass
4. Mark Complete: Track progress systematically

Watch Out For:

• Is the bot properly using your boilerplate tools?
• Are you accumulating technical debt?
• Consider adding constraints like:
  • Keep files under 500 lines
  • Modularize the program structure
  • Follow consistent naming conventions

Step 4: Verify Good Practices

Before considering your vibe coding session complete, ensure you're following good practices:

Security Considerations

• Get your code audited
• Follow the Solana Security Roadmap
• Plan for a beta testing phase

Code Quality Checks

• Review generated code for anti-patterns
• Ensure proper error handling
• Verify state management logic
• Check for potential race conditions

Conclusion

This workshop presents a technique, not a rigid recipe. The skill lies in adapting this workflow to your use case and exploring new possibilities as LLMs evolve weekly.

Remember: While this represents a new way of building software, the engineering approach of defining specifications and meeting them remains unchanged. It's still your responsibility to:

• Set proper constraints
• Iterate on implementations
• Ensure quality and security

The magic isn't in the AI - it's in how you structure and guide it. Happy vibe coding!