Sloth-ninja makes AI changes earn their way into JessicaOSS
This fork is putting a measurable quality gate between AI changes and a production merge.
Sloth-ninja has added a disciplined evaluation setup: known examples are scored, a baseline is recorded, and the automated checks can block a change that makes those results worse.
That matters because legal AI quality often erodes quietly. A polished interface can conceal weaker answers, missed details, or less reliable workflows until users spot the problem. Here, the fork treats tested AI behaviour as something that must be preserved, not merely demonstrated once.
The team has also adjusted its automated checks to tolerate a known test-runner glitch only when every test has otherwise passed. It is a practical choice, but worth inspecting on GitHub: the safeguard is only credible if it cannot wave through a genuine failure.
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