Why it matters
AI code review gives ForkMesh a concrete mechanism for line-anchored AI findings that turn into reviewable fix suggestions. One click asks an AI reviewer for findings, each anchored to the exact lines as a mini-diff suggestion. Apply a single quick-fix or fix everything at once.
Review becomes risky when merge state, discussion, and final application are trapped in a central service. In ForkMesh, AI code review addresses that need at the feature level, keeping the behavior close to the repository instead of buried in a detached hosted layer.
How ForkMesh handles it
ForkMesh handles AI code review through the same split it uses across the product: ForkMesh derives diffs from live branches or signed patches, checks mergeability against the live base, and records review activity as portable data.
The behavior is intentionally narrow: One click asks an AI reviewer for findings, each anchored to the exact lines as a mini-diff suggestion. Apply a single quick-fix or fix everything at once. Maintainers can verify what changed, who proposed it, and whether it will merge before touching the base branch.
Where it fits
Use AI code review when contributors need familiar review mechanics without making one hosted PR database the source of truth.
It pairs with the rest of Pull requests & review because AI code review keeps line-anchored AI findings that turn into reviewable fix suggestions connected to the repository, its signatures, and the nodes that serve it.