Guides and deep dives.
How-to guides, pattern breakdowns, and project updates.
GitHub Code Quality vs aislop: Different Layers of the AI Code Quality Stack
GitHub Code Quality and aislop both provide deterministic quality gates, but they solve different adoption, scope, and workflow problems. This comparison shows where each fits—and when using both is sensible.
Read more →How to Review AI-Generated Code: A Four-Layer Checklist
A practical review order for AI-generated pull requests: prove behavior, verify intent, inspect security boundaries, then reduce maintenance cost. Includes a copyable checklist for teams.
Read more →Slopsquatting and Hallucinated Dependencies: A Software Supply-Chain Guide
Coding agents can invent plausible package names. Attackers can register them. Learn the slopsquatting attack chain, how to verify dependencies, and which controls belong before install, in CI, and at runtime.
Read more →How to Stop AI-Generated Technical Debt Before Merge
AI-generated debt is cheapest while the pull request is still open. Use this evidence-based workflow to detect unnecessary code, weak tests, unsafe shortcuts, and ownership gaps before they become maintenance work.
Read more →How to Vibe Code Without Degrading Your Codebase
A practical vibe-coding workflow that preserves speed without outsourcing understanding: small contracts, bounded diffs, executable gates, adversarial tests, minimum-patch review, and ownership.
Read more →A Good AI Code Reviewer Knows When to Stay Quiet
AI code review is useful when it removes work from human reviewers. Here is how engineering leaders can separate signal from activity and measure whether a review bot is genuinely helping.
Read more →aislop is now on the GitHub Marketplace
aislop just launched on GitHub Marketplace. Every PR gets scanned automatically, findings go back to the agent that wrote the code, and nothing merges below your quality threshold.
Read more →The Engineering Manager's Guide to AI-Generated Code Quality
A practical 30-day rollout for measuring AI code review pressure, piloting quality controls, tuning signal, and enforcing standards without invented ROI.
Read more →Automated Code Review for AI-Generated Code: The Workflow That Holds
AI-generated code changes the code review workflow. Here is how teams can combine deterministic quality gates, agent handoff, CI, and human judgment without drowning reviewers in bigger PRs.
Read more →We scanned gstack. The score was brutal, but the useful part was the verdict.
A real-world scan of gstack showed why AI-slop tooling needs more than a single score. aislop found two confirmed defects, several conservative security patterns, and a large amount of reviewable quality debt. Then we changed the output so teams can see the difference.
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