AI product
An AI-powered code-review platform that automatically analyzes pull requests in repositories hosted on platforms such as GitHub and GitLab, producing review comments, summaries, and suggestions within source-control and code-hosting workflows.
12 uses taken from transcripts — each links to the moment in the video.
Automatically reviews code.
Provides automated security reviews for pull requests, checks dependencies for security issues, and audits entire codebases. Its AI feature can also file pull requests to fix detected problems.
An AI code review service that analyzes code, detects bugs, suggests fixes and improvements, and provides summaries and an agent chat for understanding code changes.
An AI code review service for GitHub and GitLab that organizes pull request diffs into related chunks, shows function definitions inline, answers questions about changes, and prioritizes critical issues.
Review bot whose findings were answered or fixed before AI-generated pull requests were merged.
An AI code reviewer that reviews code in GitHub, IDEs, and the CLI. It also provides agents that can fix issues, monitor pull requests, and work through Slack with context from tools such as Linear, Jira, and Datadog.
AI-assisted pull-request review tool used to identify bugs, edge cases, quality issues, and security issues.
An AI code reviewer that reviews code changes. Its Change Stack feature organizes pull-request changes into understandable sections, summarizes what changed and why, shows security risk through a blast-radius diagram, and provides an activity view for quickly catching up on changes.
Dedicated automated pull-request reviewer.
Reviews code during automated implementation loops and supplies feedback for the agent to address.
An AI code review app for GitHub and GitLab; its review UI splits diffs into small related chunks, shows function definitions inline, lets you ask questions about changes, and flags critical issues first to cut review time and bugs.
AI code review tool that provides security callouts in pull requests with one-click fixes, plus deep scans that run across an entire repo and surface findings. It has approvals to let models give real security feedback that other models refuse to provide.
12 in the library.