Our review queue had a familiar shape: half the comments on every pull request were things a machine could have said. Unused imports, a hardcoded token in a test, a missing timeout on an HTTP call, a new endpoint without a test. Reviewers spent their attention on those and skimmed the parts that needed a human, like the data model change. We built a local review gate that runs before git push: fast deterministic checks first, then security scanners, then the test suite in Docker, and finally an...
An AI code review gate in git hooks and Docker: LLM review, security scans and tests
We built a pre-push gate that runs linters, secret and SAST scans, containerised tests and an LLM review of the diff before code leaves a laptop. The hard parts were not the model call: ordering, cost caps, redaction, fail-open rules and keeping CI as the real authority.
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I am a Senior Software Engineer with over a decade of hands-on experience building, scaling, and maintaining complex systems across the full stack—spanning frontend, backend, and DevOps. Over the last 10 years, I have navigated diverse architectures and untangled some of the most frustrating, elusive bugs in production. I believe that every production incident is a massive learning opportunity. Having discovered hibug.me, I am excited to contribute to this knowledge base, breaking down root causes, and sharing verified fixes to help fellow developers debug faster and build more resilient systems.
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