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The entire exhibition framework, examples and field guide remain available below without scripts. See the field guide for manual judging worksheets.
One prompt. Four plausible outputs. Six lenses. No borrowed authority.
What happens when we stop asking which model is “best” and begin asking what the work actually does? This is a transparent editorial demonstration—not a live AI benchmark.
OPEN THE COMPARISON CHAMBER ↘DIFFERENCE ≠ ERROR
CONSENSUS ≠ TRUTH
Every shipped entry is fictional editorial sample work. Edit freely; all changes and votes stay in this browser unless you export them.
A new prompt version does not regenerate existing outputs; the entries remain illustrative until manually edited or imported.
The entire exhibition framework, examples and field guide remain available below without scripts. See the field guide for manual judging worksheets.
Scores are local, subjective ordinal ratings (1–5), not measurements of actual model capability. A vote is a preference in this browser session, not public polling.
Document what broke instead of quietly removing it from the gallery.
Enable JavaScript to manage the editable archive. Example cases and method remain in the field guide.
Does the response find a fresh mechanism, not merely fresh adjectives?
Do its parts fit together without contradictions?
Could a visitor understand what to do and what changes?
Are conditions, dependencies and steps clear enough to repeat?
Are keyboard, text, motion and contrast needs addressed?
Does the described result truly operate? Text alone is not proof.
A prompt defines a task, not a universal intelligence test. A polished paragraph may win for narrative clarity but lose for execution. A prototype may be original and still inaccessible. When the evaluation criteria change, the apparent winner can change with them. That is a property of judging, not evidence of the superiority of a particular model.
Evidence boundary: The four named specimens in this edition are author-written demonstration artifacts; provider labels are explicitly fictional. Their rubric values illustrate how judging works, not measured model performance. Manual scores, votes and your edits are locally entered data. The interface makes no network requests.
A · Blindfold the brand. Score each specimen without its label, then reveal the labels. Notice what you expected, but do not infer an experimental effect from one encounter.
B · Shift the brief. Add a stricter accessibility or reproducibility constraint. Commit a prompt version, annotate which samples would need revision, then rescore.
C · Preserve the failure. Deliberately test a claim that is not implemented. File it in the failure archive with a reproducible observation.
A multi-page original workbook with rubric anchors, experiments, interpretation cautions, annotated examples and reading paths.