Using AI to Debug Code Without Losing Your Mind

Short summary

A practical guide to debugging code with AI that emphasizes showing the problem rather than describing it. The author demonstrates how providing heap snapshots, error stacks, git diffs, and reproduction cases to Claude yields specific, actionable fixes instead of generic checklists. The key insight: AI debugging works best when you supply concrete evidence, not vague problem descriptions.

• •Show AI the actual problem: error stacks, code snippets, heap snapshots, and reproduction cases
• •Ask specific questions about concrete code rather than vague problem descriptions
• •Use tools like Chrome DevTools heap snapshots, clinic.js, and git diff to give AI precise context

Generated with AI, which can make mistakes.