Resources

Guides, benchmarks, and comparisons.

Guides, the measured benchmark, and head-to-head comparisons. The numbers come from 87 matched bug-fix cases against Cursor: 5× fewer tokens, 1.7× faster, 87 of 87 verified.

Resources

Benchmark report

Method and results for Codna against Cursor on 87 matched bug-fix cases.

Open
01

Autonomous code repair

Why agents need a deterministic map before they fix.

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02

AI code review

How severity, confidence and a clear verdict make pull request review faster.

Read
03

Sentry to PR

Turn a production failure into a fix pull request with root cause attached.

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Frequently asked

On 87 matched bug-fix cases against Cursor, Codna used 5× fewer tokens and ran 1.7× faster. Both verified 87 of 87 fixes. Codna averaged about $0.02 of model spend per fix.

Total tokens, wall-clock time, model cost and verified fixes per case, averaged over 87 matched cases run on identical checkouts.

Codna maps the repository without a model, for zero tokens, and hands the agent an evidence bundle scoped to the issue. It prints the raw-to-bundle size on every run.

The repository map is built from import patterns, so it is not tied to one language. codna impact narrows a diff to the tests it can affect. On-device recall covers Python, JavaScript, TypeScript, Go, Rust, Java, C, C++, C#, PHP and Ruby.

Codna locates the affected code with its dependency and blast-radius graph, fixes from the evidence bundle, and reports root cause, confidence, blast radius and regression risk. On GitHub, it opens a pull request that explains itself. You merge.

Understanding runs on your machine. Only the evidence bundle or the diff reaches your model provider, with your key. Set privacy.egress to fail-closed in codna.yaml and Codna runs your tests only under kernel-level network denial.