Chris Roadfeldt. Five minutes.
1 / 7Most of the code in any product was written by someone else, and nobody on our side has tested it.
AI writes tests for every piece of code that enters a product, including the packages our packages depend on. The tests run in a sealed box. The results are scored. A person decides. Everything is signed.
flowchart LR
A[Code arrives] --> B[AI writes tests] --> C[Sealed box runs them] --> D[Results scored] --> E[A person decides] --> F[Signed record]Nothing merges, publishes, or deletes without a person. The harness proposes; a reviewer decides.
Two ways to use it: a developer runs it while working and the tests go in with their pull request; the pipeline runs it on every change and proposes a test pull request. Either way, tests arrive through a pull request and then run with the normal suite.
3 / 7One real pull request on one of my services set out to fix four packages with known vulnerabilities. The harness judged it:
| Package | What the change did | What the harness proved |
|---|---|---|
| python-jose | updated | one vulnerability closed |
| pyasn1 | silently downgraded | one vulnerability introduced |
| starlette | left alone | one vulnerability live today, and the upgrade path |
No person wrote any of that. The downgrade is the finding a reviewer would have missed: the change never mentions it.
4 / 7It does not fix code. A separate fix pipeline does; this one hands it the failing test that proves the problem, and tests whatever comes back. The evidence stays independent of the fix, and the identity that proposes tests never needs the credentials that change production code.
5 / 7Every limit is written into the result. That is the point: the system reports what it could not do.
6 / 7The one number I will report: regressions and vulnerabilities caught by harness tests that nothing else would have caught, every quarter.
Details: the case, the roadmap, and the evidence are one click each on the project site.
7 / 7