Featured · Engineering
AI code review still needs a human decision
A practical framework for checking an AI finding: verify its evidence, reproduce its trigger, and decide what the change actually requires.
Read the story ↗ANTICODE / FIELD NOTES
Better reviews.
Thoughtful engineering.
Practical ideas for the code you write, review, and ship.
AI code review still needs a human decision
A practical framework for checking an AI finding: verify its evidence, reproduce its trigger, and decide what the change actually requires.
How to split a pull request without losing the story
Keep changes easy to understand by separating mechanical work, behavior changes, and rollout decisions into reviewable steps.
A practical triage workflow for code review findings
Turn review output into deliberate decisions while keeping accepted, dismissed, fixed, and verified distinct.
Connect GitHub and run your first AntiCode review
An honest first-run guide to repository authorization, syncing pull requests, manual review, and checking saved results.
Local vs cloud code review: ask where the code goes
Evaluate code-review privacy through data flow, permissions, retention, and the difference between local checks and hosted AI.
Reduce AI review noise with evidence, not fewer warnings
A useful finding connects a real line of code to a concrete failure. Learn how to evaluate that connection without suppressing useful signals.
API key hygiene for developer tools: prevent the second leak
Keep credentials out of code, reviews, and troubleshooting artifacts, and prepare a rotation process before you need one.
A security review checklist for a focused pull request
Trace the changed trust boundary, test failure paths, and check whether a proposed fix creates a new exposure.
Why a code review becomes stale when the base changes
A review belongs to a particular comparison. Understand why the head commit, base commit, and target branch all matter.
Write team review rules that people can actually follow
Define ownership, blocking feedback, freshness, and escalation without turning every pull request into a policy exercise.
Code review metrics: measure what happened, not what sounds good
Define cohorts and denominators, separate activity from outcomes, and resist turning finding counts into productivity claims.
Run an AI code review pilot your team can learn from
Choose a narrow scope, evaluate known defects and safe changes, and decide from evidence rather than a memorable demo.