The AI Tool Landscape
The past 18 months have seen an explosion of AI developer tools. Most are noise. A few are genuinely transformative. Here's what we actually use.
Code Completion & Generation
GitHub Copilot is the baseline — it's good at boilerplate and completing patterns it's seen before. But the real productivity gains come from using it as a thinking partner, not just an autocomplete.
Cursor has become our editor of choice for complex refactors. The codebase-aware context means it can reason across files in ways that single-file tools cannot.
Documentation Generation
AI-generated documentation has a reliability problem — it confidently hallucinates API details. We use AI for documentation structure and initial drafts, but always verify against the actual source.
Code Review
The tools that scan diffs for obvious issues (security vulnerabilities, performance anti-patterns) have earned their place. They catch the things tired reviewers miss at 6pm on a Friday.
What Hasn't Changed
AI doesn't replace system design. It doesn't replace the judgment call on trade-offs. It doesn't replace the understanding of your users' actual problems. The engineers who treat AI as a collaborator rather than a replacement are shipping better, faster.
The Bottom Line
AI tools have made our team ~30% more productive on implementation tasks. The architectural, product, and human judgment work is still entirely ours.