Insights on AI code review, multi-agent systems, and developer productivity
Meet diffray's newest agent — SEO Expert catches missing meta tags, broken OpenGraph, invalid structured data, and more before they hurt your rankings. Now every PR is optimized for search.
diffray now supports rules that analyze the entire Pull Request — commit messages, PR descriptions, scope, and breaking changes. Enforce team conventions automatically with two new tags: pr-level and git-history.
How structured YAML rules transform AI code review from inconsistent suggestions into deterministic, predictable results. Learn why pattern matching and context curation make the difference.
Introducing diffray's 10 core review agents - specialized AI experts in security, SEO, performance, bugs, quality, architecture, and more. Each agent brings deep focus to their domain for thorough code reviews.
Research proves: fewer, highly relevant documents outperform large context dumps by 10-20%. Learn why models fail at ~25k tokens and how agentic retrieval achieves 7x improvements over static context injection.
Deep technical analysis of AI code review architectures. Learn why your current tool misses 67% of critical security vulnerabilities and how multi-agent systems achieve 3x better detection rates.
Discover why 78% of developers ignore AI code review feedback and how multi-agent architecture solves the noise problem.