I've tried dozens of AI tools. These five are the only ones I keep coming back to.
1. Code Completion (GitHub Copilot / Cursor)
Not just autocomplete. Real context-aware code generation that understands your project structure.
Use it for: boilerplate, test generation, unfamiliar APIs
2. Documentation (Mintlify / Docusaurus AI)
AI-powered docs that write themselves from your code. Still need human review, but the first draft is 80% there.
Use it for: API docs, README files, internal docs
3. Code Review (CodeRabbit / GitHub Copilot Review)
AI that reviews pull requests, catches bugs, suggests improvements. Not a replacement for human review, but a great first pass.
Use it for: catching obvious issues before human review
4. Error Debugging (Cursor / Claude)
Paste an error, get context-aware debugging suggestions. Better than Stack Overflow because it knows your specific code.
Use it for: mysterious bugs, unfamiliar error messages
5. Migration Assistance (GitHub Copilot)
Converting codebases between languages or frameworks. Not perfect, but gets you 70% of the way on boring migrations.
Use it for: language upgrades, framework migrations
The Key Insight
AI tools amplify what you already know. If you're a weak programmer, AI makes you mediocre. If you're a strong programmer, AI makes you 10x. Focus on building fundamentals first. For extension-specific development, use ExtensionBooster's tools which handle the boilerplate automatically.
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