Over the past few weeks, I’ve been experimenting with various AI coding assistants including Qwen, Gemini, Cursor, and Claude Code. My experience has been a mix of successes and setbacks.

The Good: Efficiency Boost

When these tools work well, they are very effective. I’ve seen them:

  • Generate entire component files from simple descriptions
  • Refactor complex code blocks with perfect accuracy
  • Suggest optimizations I wouldn’t have thought of
  • Explain intricate concepts in accessible terms

At times, the AI produces the exact code needed, which shows the potential of AI in software development.

The Frustrating: Breaking What Already Works

However, there are issues. I’ve encountered these issues:

  • The AI breaks existing functionality while trying to implement new features
  • It repeatedly makes the same mistakes even after I point them out
  • It gets stuck in loops trying to fix simple issues without identifying the root cause
  • It confidently provides solutions that look correct but fail in subtle ways

The cycle of asking the AI to generate code, which then breaks existing code, and then asking for a fix that introduces new issues is a problem.

The Paradox: Powerful Yet Limited

These tools are sophisticated in some areas while failing at basic tasks in others. They can architect complex systems but stumble on simple syntax errors. They understand high-level concepts but miss implementation details that are obvious to any experienced developer.

The Verdict: Worth Trying, But Manage Expectations

Despite the inconsistencies, these tools are worth exploring. They offer productivity gains when they work correctly. However, they are not yet reliable partners.

My current recommendation:

  • Use them for brainstorming and initial implementation
  • Always review and test their output thoroughly
  • Don’t rely on them for critical or complex systems without extensive validation
  • Remember that they’re assistants, not replacements - your expertise is still essential

The technology is heading in the right direction, but it’s still in the early adopter phase where human oversight is required.