Guides to AI code review
Long-form guides for engineering teams: how AI review works, what to check in a pull request, and how to adopt it without adding noise.
What is AI code review?
What AI code review does, how it differs from linters and static analysis, why codebase context and verification matter, and how to evaluate a tool.
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A code review checklist for human and AI-assisted review
A practical code review checklist for correctness, security, concurrency, errors, migrations, API contracts, tests and performance, and what AI review covers.
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Build or buy: an in-house LLM code reviewer
The components you would need to build a production code reviewer on a language model, what it costs to keep running, and when building is the right call.
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Why the agent that wrote the code should not be its only reviewer
Separation of duties, shared blind spots and auditability in AI-assisted development, and why code from coding agents still needs an independent review.
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Diff-only versus codebase-aware review: what a diff hides
Why many serious bugs sit outside the lines a pull request changes, three concrete examples, and how MergeHarbor indexes, traces and verifies to find them.
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See how a review works, stage by stage
From the repository index to verified findings, with measured results on real bugs.
Review every pull request with the context of your whole codebase
Connect a code host in minutes. Reviews start right away, and codebase context is added as indexing completes.