LESS AI SLOP.
MORE REAL PROOF.
Small local models propose improvements. Simulated player personas test real interactions. Evidence gates separate what was observed from what was imagined. No fake humans, no automatic production edits.
Model work runs privately on your Hostinger VPS. This page serves approved read-only receipts, not private prompts, credentials, model endpoints, or privileged job execution.
Actual changes. Actual tests.
Verified bugs and tested repairs are shown separately from model-generated guesses.
AI personas. Not fake people.
Each persona reviews a specified public source excerpt using an actual local model. Quotes must match source lines. An anchored quote is evidence the source contains those bytes; it does not prove the model’s criticism is correct.
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Your local model roster.
Models take specialized tasks, but the independent quality rubric—not their names—determines whether a proposal advances.
Break things on purpose.
Real automated Chromium browser interactions, driven by scripted synthetic personas. These tests verify observed UI behavior. They do not replace people, screen readers, or human usability research.
The anti-slop protocol.
Point to a specific approved file, line and hash. Don't invent test results.
Ask what can fail, who experiences it, why it matters and what evidence is missing.
Write a concrete proposed change plus a reproducible acceptance test. No vague “enhance UX.”
Run browser and automated code tests; consider accessibility, data privacy and failure cases.
Keep a reviewer and rollback requirement for real production changes.
Make your questions more useful.
Turn an initial idea into a testable, precise build or review request. This builder runs entirely in your browser; it doesn't call an AI model or upload your text.
Enter your challenge and generate a testable request.
Play the Slop Hunter.
Six real examples of what AI reviews get right or wrong. Decide what the evidence actually supports. The game is local-only and does not contact the models.
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Real feedback is different.
Invite a person to try the live game and write what actually happened. This form downloads their observations as a JSON file; it does not falsely submit anything to a private server.
Did they understand what to do without explanation? Could they recover from mistakes? Which moment felt rewarding? Did they trust the results? Would they play again—and why?
Do not upload passwords, contact details or private files. Keep real-person feedback separate from synthetic-model impressions. The completed file can be reviewed and imported into a guarded workflow later.