ZTHR proposes a pattern language where gates replace human diff review

Little's executive summary argues that AI has made writing code cheap but has not made deciding whether it is right cheaper, and that human review under real volume becomes either a bottleneck or a rubber stamp. His answer is a factory of checks: "Let AI write the code, and build a factory that cannot be fooled about whether the code works." No author, reviewer, model or report gains authority by asserting success, and a test that has never been shown to fail is not trusted to pass.

The summary says every change is reviewed by at least three models from different families before commit: one given the context, one given only the diff, and one checking the code independently. A fourth reviews it in place before full promotion. When a defect slips through, a new check is added so that kind of defect cannot pass again. People define intent and govern the checks; destructive or multi-step changes, any change to the AI itself and any agent request for wider permissions still need a person's sign-off.

The author says ZTHR is in production use for greenfield systems in a regulated industry, where it "has proven highly effective." The summary gives no figures for that claim, and we read only the executive summary, not the five-page brief or the full paper with its thirty-three patterns.

Why it matters: The design question is concrete: what evidence does a change need that its author cannot supply? The multi-model review rule and the "new defect, new check" loop are specific enough to test in your own pipeline. The effectiveness claim is the author's own and unmeasured in what we read.