Building AI-Powered Code Review Tools at Apple

This past summer I returned to Apple, this time as a Watch software engineering intern. My project: bridge GitHub with Apple's internal bug reporting tool, then layer an AI analysis pass on top so reviewers see relevant context inline.

The first half was integration work. I wrote a script that watches incoming pull requests and matches the changes against the bug database, posting a comment with the most likely related issues. Sounds simple, but the matching logic is where the value lives - false positives erode trust quickly, so I spent most of my time tuning signal-to-noise rather than adding features.

The second half was the AI layer. I built a system that runs alongside human reviewers, flagging potential issues in changed files - missed nil checks, suspicious concurrency, regressions against patterns the team had already fixed. The team reported a 35% increase in bug detection during reviews where the tool was active. The more interesting result, to me, was the qualitative one: reviewers stopped re-reading the same kinds of mistakes and could focus on architecture and design.

What I took away: developer tools live or die on latency and trust. If the comment shows up after the PR has merged, no one cares. If it cries wolf, no one reads it. Build the slow, careful version first, then optimize toward the version that's fast enough to be useful.