Effectively using AI coding assistants (sometimes called "Vibe Coding") means treating them like professional collaborators, not magic boxes. Success requires active guidance and integrating AI into disciplined software engineering practices.

Key takeaways:

Plan Thoroughly: Work with the AI to create a detailed project plan before coding.

Build Incrementally: Implement the plan section by section, verifying, testing, and committing (using Git) each piece before moving on.

Use Git Religiously: Version control is crucial for managing AI changes and recovering from errors (don't be afraid to git reset).

Prioritize High-Level Tests: Focus on integration tests that simulate user flows to catch regressions AI might introduce.

Debug Smartly: Feed error messages directly to the AI; reset to a clean state before applying fixes to avoid accumulating "crust."

Customize Your AI: Use instruction files and provide local documentation for better accuracy.

Structure Code Well: Modularity helps both humans and AI understand the codebase.

Stay Flexible: Experiment with different tools (like Cursor, Windsurf) and models (GPT, Claude, Gemini), and use various inputs (screenshots, voice).

Human Oversight is Essential: Always review, test, and understand AI-generated code for quality, security, and maintainability.