deslopifylisted

# Deslopify Strip the AI fingerprints out of a text and make it good in its genre. The target is prose that reads like one person wrote it for one audience about something they actually know. Detector scores are beside the point; text can score human and still be unreadable. ## Why this loops The "not X but Y" family and its relatives are generative habits. Preference tuning rewards balanced, contrastive, comprehensive-sounding framing, so the contrast move sits deep in the model's priors and surfaces about once a paragraph. Two consequences shape this skill: 1. **You cannot see your own slop.** The priors that produce the pattern also make it invisible on re-read. So detection runs mechanically, as regex against a fixed catalog. "Does this look AI to me?" is not a detection method. 2. **Rewriting reintroduces slop.** Ask a model to remove "it's not just X, it's Y" and out comes "this is less about X than Y", the same move in a wig. Every rewrite therefore gets re-scanned, and the loop runs until a scan comes back clean. Workflow: **Scan → Diagnose → Rewrite by meaning → Re-scan → (repeat) → Register check.** ## Phase 0: Fix the target Before touching the text, establish: - **Genre and venue.** Academic article, tweet, reddit post, LinkedIn, email, blog, docs, marketing. Ask when it isn't stated and isn't obvious from the text. Genre decides which tells are fatal and what "good" means; see [references/voices.md](references/voices.md). - **Audience and stance.** Who re