Stooky

X ALGORITHM: ATTENTION VS AI SPAM 🚨 X's published code separates ranking from visibility filtering. Mixing them up leads to bad growth advice. tl;dr: - ranking weights aren't a price list for reach - reading time matters, but padding isn't value - AI-related enforcement labels exist - a probable-spam screenshot doesn't reveal which label caused it full details: 1. The numbers need context Published ranking defaults include likes at 0.708, replies at 5 and shares via copy link at 20. These weights combine predictions about what a viewer might do. They don't mean one copied link guarantees more reach than 28 likes. 2. Attention isn't a length contest The repository describes systems that predict reading and time spent. That supports making posts worth reading. It doesn't support stretching one useful sentence into ten paragraphs. Put the point near the top. Use the rest to explain or demonstrate it. 3. Small-account boosts aren't guarantees The published defaults include a new-author boost with follower, impression and post-age thresholds. That is a mechanism, not guaranteed distribution. X runs experiments, and public defaults don't explain every individual result. 4. Video needs the same caution The model includes predictions for video continuation and profile visits. A zero video-open weight in one configuration doesn't mean video is worthless everywhere on X. Show the result early. Give people a clear reason to follow. 5. The AI labels are real Published enforcement rules include: - llm_slop_post: RiskyHighVizReply, 30-day duration - llm_slop_user: SpamHighRecall, seven-day duration Those are different rules at different levels. Neither proves that every AI-assisted reply restricts an entire account. 6. Don't diagnose from a screenshot “Probable spam” alone cannot tell you whether an AI label applied. Manual posting isn't proof of immunity either. And public configuration files don't promise a reset date. What I'd focus on: - specific observations - evidence and real examples - readable screenshots - useful demonstrations - a profile that matches the posts - replies that add something new What I'd stop doing: - generic praise - paraphrasing the original post - forced questions - invented personal experience - padding posts for reading time - treating every weak result as a restriction - presenting algorithm theories as platform rules The goal isn't to imitate whatever the algorithm might reward. It's to publish something worth reading.