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Evidence, not vibes.
Notes on AI code review, AI PR review, agentic software delivery, production-aware validation, and evidence-backed review.
Code review that reads your system
A concrete walkthrough of intent capture, local review, PR review, production evidence, validation, and the final merge signal.
Read moreYour backend and frontend shouldn't share one code reviewer
A concrete guide to configuring repo-specific review rules, tools, validation profiles, and privacy boundaries.
Read moreWhy not just use an LLM to review your pull requests?
Pasting a diff into an LLM can help once. Teams need independent review, production evidence, validation, privacy, and audit.
Read moreYour CI runs the tests you wrote. Spinal writes the one you didn't.
CI protects known behavior. Spinal turns PR-specific production risk into focused validation before merge.
Read moreProduction-aware code review: the discipline AI-era teams are missing
Production-aware review checks a change against intent, repo rules, runtime behavior, and validation evidence.
Read moreAI code review tools compared: how to choose in the agent era
The strongest AI reviewers no longer just summarize diffs. Compare them by the evidence they can use before a PR merges.
Read moreSelf-hosted AI code review is not enough. Regulated teams need a private evidence layer.
Agentic SDLC review touches code, intent, CI, telemetry, and validation results. Regulated teams need control over the full evidence path.
Read moreWhat is Spinal? The production-aware trust layer for agentic SDLC
Spinal gives AI-assisted change an evidence trail: intent, local and PR review, observability-aware validation, and memory.
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