Guides and deep dives.
How-to guides, pattern breakdowns, and project updates.
AI Code Review Metrics That Expose Noisy Tools
Measure whether AI code review reduces risk and human work. Track verified precision, issue coverage, dismissal cost, duplicates, latency, resolution, escaped defects, and cost per useful finding.
Read more →AI Code Security Review Checklist: From Prompt to Production
A practical security review for AI-generated code covering identity, data, execution, dependencies, operations, and the AI-specific ways controls get bypassed.
Read more →How to Review AI-Written Code: A Four-Layer Checklist
How to review AI-written code without reading the diff top to bottom: prove behavior, verify intent, inspect security boundaries, then cut maintenance cost. A copyable checklist.
Read more →20 AI Slop Code Examples: Slop Code, Named
AI slop code examples and slop examples in a repo: swallowed errors, fake safety, redundant abstractions, comments, dead paths, test weakening, dependency risk, and the cleaner pattern.
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 →What Should an AI Code Quality Checker Actually Check?
A vendor-neutral evaluation framework for AI code quality tools: coverage, reproducibility, signal, context, security, workflow, privacy, remediation, and measurable review value.
Read more →AI Coding Standards: A Copyable Rules File for Claude, Cursor, Codex, and Copilot
A concise, tool-portable coding standard for AI agents: repository context, task discipline, code boundaries, tests, security, review, and the quality gates that make instructions enforceable.
Read more →Deterministic vs AI Code Review: Use Each for the Work It Can Prove
Deterministic vs AI code review: a gate enforces repeatable facts, an agentic reviewer reasons about intent. Combine them without duplicate comments or unstable merge rules.
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.
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