SuperDiffs

Superdiffs is an AI code review tool for GitHub pull requests that focuses on verified findings rather than broad or vague review comments. It reads the pull request diff and surrounding code, tests potential issues, and only posts inline comments when the finding has a concrete failure scenario behind it.

The workflow stays inside GitHub. Superdiffs can post a review summary, inline comments, and a check run, while also showing the model and prompt used for the review and what was skipped. That gives teams more context for judging whether a finding is worth acting on.

Key Features

Proof-Backed Review Comments

Attach evidence to each code review finding.

Superdiffs explains the failure scenario behind a comment, giving developers more context than a generic warning about a potentially problematic line.

This makes it easier to understand why an issue was flagged and decide whether it needs attention.

Verified Findings

Check potential comments before publishing them.

The system evaluates candidate findings and removes comments that do not survive verification. This is designed to reduce false positives and keep low-confidence observations out of the pull request.

GitHub Pull Request Workflow

Review code directly where the development team already works.

Superdiffs provides:

• Pull request summaries
• Inline review comments
• GitHub check runs
• PR commands
• Resolved finding tracking

This keeps the review process connected to the existing GitHub workflow rather than requiring a separate review dashboard.

Public Accuracy Metrics

Make review quality easier to evaluate.

Superdiffs publishes information about its own misses and benchmark performance, including comment precision. This gives teams a way to assess the reviewer using published evidence rather than relying only on a proprietary quality score.

Model and Prompt Visibility

See what produced a review.

The product can surface:

• Model used
• Prompt used
• Findings generated
• Findings skipped

This provides additional context for teams that want to understand how an AI review was produced.

Disposable Code Sandbox

Review code with scoped access.

Superdiffs reads the repository code in a disposable sandbox and limits its ability to write back to GitHub to pull requests and checks.

This approach is designed to keep the review process separated from the main development environment.

Repository Controls

Control where the reviewer is installed and how it operates.

Teams can install Superdiffs on:

• Individual repositories
• Organizations

PR commands can also be used to steer the review workflow.

Resolved Finding Tracking

Keep previously resolved issues from returning after changes.

Superdiffs tracks resolved findings so they remain suppressed across rebases, reducing the chance that the same issue repeatedly appears during later iterations of a pull request.

Data Deletion

Remove stored data when the integration is uninstalled.

Superdiffs states that uninstalling the service deletes the associated data.

Built for Evidence-Based Code Review

Superdiffs combines AI analysis, verification, GitHub integration, and public quality reporting into a code review workflow focused on actionable findings.

Key benefits include:

• Review GitHub pull requests with AI
• Attach failure scenarios to findings
• Verify comments before publishing
• Reduce vague or low-confidence review comments
• Post inline comments directly on pull requests
• Generate review summaries and check runs
• Show the model and prompt behind reviews
• Show skipped findings
• Track resolved findings across rebases
• Publish review precision and benchmark information
• Use disposable sandboxed code access
• Control repository-level installation
• Steer reviews through PR commands
• Delete data when the integration is uninstalled

Built For

• Software Developers
• Engineering Teams
• Tech Leads
• Engineering Managers
• Open Source Maintainers
• Startup Teams
• SaaS Companies
• Development Agencies

Common Use Cases

• Reviewing GitHub pull requests
• Finding bugs before merge
• Reducing noisy AI review comments
• Verifying potential code issues
• Adding automated checks to PRs
• Reviewing changes with surrounding code context
• Investigating why an AI reviewer flagged a line
• Keeping resolved findings suppressed across rebases
• Evaluating AI review quality through public benchmarks
• Adding automated review to open source repositories

Why It Matters

AI code review can produce a large number of comments, but volume alone does not make a review useful. Developers need to understand what could actually fail and whether the finding is worth acting on.

Superdiffs takes a verification-first approach. It analyzes the diff and surrounding code, tests candidate findings against a failure scenario, and only publishes comments that pass its verification process.

The product also makes its review process more inspectable. Teams can see the model and prompt used, understand what was skipped, and evaluate published accuracy metrics. Resolved findings remain suppressed across rebases, which helps prevent repetitive comments as a pull request evolves.

Review GitHub Pull Requests With Verified Findings

Connect Superdiffs to a repository, let it analyze the pull request and surrounding code, verify potential issues against concrete failure scenarios, and review the resulting summary, inline comments, and check run directly inside GitHub.