Party of One for Code Review!
A tutorial by Kent Beck. Featured in the OTF curated resource library.
The Solo Review Problem
Code review is one of the most effective quality practices in software engineering. It catches bugs, improves architecture, enforces conventions, and spreads knowledge. But it requires another person — which solo developers and small teams don't always have.
The result: solo developers ship unreviewed code. Small teams rubber-stamp reviews because everyone's busy. Quality suffers, bugs slip through, and technical debt accumulates.
AI-assisted code review solves this by providing an always-available, thorough reviewer. It's not as good as a senior engineer's review, but it's vastly better than no review. And for specific categories — security vulnerabilities, missing error handling, test coverage gaps — AI review often catches things humans miss.
AI-Assisted Review Workflow
A practical review workflow for solo developers.
Write the code (with or without AI)
Develop your feature as usual. Whether you write code manually or use AI assistance, the review step is separate and happens after the code is written.
Run the AI reviewer
Use Claude Code's `/review` command or Cursor's review feature to analyze your changes. The AI reads the diff and provides feedback on code quality, potential bugs, and missing patterns.
Address the feedback
Review the AI's feedback. Fix genuine issues, dismiss false positives, and note patterns to improve. The AI catches things you missed because you're too close to the code.
Commit with confidence
After addressing review feedback, commit knowing your code has been reviewed for common issues, security concerns, and quality patterns.
Effective Review Prompts
General Quality Review
'Review my staged changes. Check for: bugs, edge cases, error handling, naming clarity, and code duplication. Suggest improvements.'
Security Review
'Review these changes for security issues: SQL injection, XSS, CSRF, exposed secrets, insecure data handling. Flag anything concerning.'
Architecture Review
'Review whether these changes follow good separation of concerns. Is business logic separated from presentation? Are there any tight couplings that should be loosened?'
Test Coverage Review
'Review the test coverage for these changes. What edge cases are untested? What error scenarios are missing? Suggest additional tests.'
Building the Review Habit
The hardest part of solo code review is making it a habit. Here's what works:
Make it automatic. Add a pre-commit hook or CI step that prompts for review before merging. When review is part of the pipeline, you can't skip it.
Keep it fast. A focused AI review takes 2-3 minutes. If it takes longer, you're reviewing too much at once. Commit smaller changes more frequently.
Track what it catches. Keep a mental (or written) log of real bugs the AI reviewer catches. This builds motivation — each caught bug is a production incident prevented.
Don't review everything equally. Critical paths (authentication, payments, data handling) get thorough security + quality reviews. Trivial changes get a quick quality scan.
