A public resource of reusable code review prompts extracted from real-world open-source projects. Designed to help anyone from OSS maintainers to AI agent builders embed best practices into their workflows without starting from scratch.
Every engineering team has unspoken rules, the kind of standards you’ve repeated in PRs a hundred times but never bothered to write down. With AI code reviewers, those patterns don’t need to stay scattered.
AwesomeReviewers demonstrates how code review discussions can turn into guidelines that capture the team’s collective wisdom. In this post, we’ll share how it works under the hood, and what we learned building it.
Introducing Custom Reviewers, powered by your team’s own pull request history. Paired with the Prompt Playground, this lets you encode, test, and evolve your team’s engineering brain into a system of principles that AI can actually use.
Achieving reliable structured output in production can be tricky, especially with extended thinking mode. By using approaches like no thinking mode, careful prompting, or combining reasoning with a separate structuring model, you can effectively work around these limitations and integrate structured outputs into your GenAI applications.
Get high-signal, secure AI code reviews in any IDE with Baz’s new MCP server. No repo integration needed. Just connect, diff, and review. Works with Cursor, Claude, VS Code, and more.
We're finally making Baz available for everyone. An AI Code Reviewer that helps teams build great software in the age of AI.
Discover how Module Memory enhances code reviews through structured, persistent context. See functional shifts, reduce cognitive load, and improve AI-assisted workflows—see this in action with the Change Request Graph in Baz.
Enhancing AI-driven code reviews with Git Diff, Difftastic, and Tree Sitter. Discover how syntax-aware diffing, structured parsing, and historical tracking improve AI’s ability to understand and optimize code changes.
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