Using Factory AI for code review has become a standard part of our PR workflow at the logistics marketplace platform where I work. It comments directly inside the pull request the way a human reviewer would, so there’s no separate tool to open and no extra dashboard to keep checking. That convenience is a big reason it actually gets used consistently instead of being ignored. What stands out most is how it catches the kinds of issues that can slip through when someone is reviewing a large diff under time pressure: missed edge cases, inconsistent error handling, or a pattern that doesn’t match the rest of the codebase. By the time a human reviewer looks at the PR, a lot of the surface-level items have already been flagged, so the conversation stays focused on architecture and business logic rather than small nitpicks. It’s also fast enough that it doesn’t become a bottleneck. Even on larger PRs that touch multiple services in our backend, the feedback comes back quickly instead of making people wait before merging. Setup on our repos didn’t take much either; it started giving relevant feedback on our codebase without us needing to hand-hold it through our conventions first. The time savings add up in a way that’s easy to underestimate. Every issue it catches before a human even opens the PR is time a reviewer doesn’t spend on something that didn’t need their judgment, and across a sprint’s worth of PRs that turns into a meaningful amount of reclaimed review time.

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Factory Droids don't just write snippets; they take real action. They can autonomously run terminal commands, spin up environments, execute test suites, and read the resulting error logs to iteratively debug their own code before submitting a pull request.