A production mode (in Higgsfield's Supercomputer) where the user states a measurable pass condition instead of a single prompt — e.g. "10 approved ad variants using my Soul ID character, each scoring above 70 on the virality predictor" — and the system repeatedly generates, tests, and reviews its own output until that condition is satisfied, regenerating failures and keeping passes, with progress visualized on a Kanban-style board.
This turns content production into unattended batch optimization (e.g. running overnight) rather than one-shot generation. It needs two ingredients: (1) a fixed, reusable asset for consistency — here Soul ID: Personal Likeness Training from Reference Photos — and (2) an automated scoring function to gate what passes, which the virality predictor supplies. The pattern generalizes beyond Higgsfield to any workflow that pairs an automated quality scorer with a regenerate-until-threshold loop, similar in spirit to Multi-Model Testing to Select Best Output but driven by a numeric goal rather than manual comparison.