I asked AI to Crack the X Algorithm (It Did)

Quick Overview

The speaker successfully cracked the X (Twitter) algorithm by reverse-engineering its scoring logic, revealing that the algorithm predicts 18 potential user actions (positive and negative signals) on every post and weights them to calculate a final score, with key factors including in-network distribution, author diversity decay, and dwell time being crucial for reach.

Key Points: The X algorithm scores posts based on 18 potential actions, categorized into positive signals (like, reply, retweet, share, dwell time, follow) and negative signals (not interested, block, mute, report). The speaker verified the algorithm's success by achieving 2.13K profile visits from one viral post, netting $576.41 in revenue from various sources like Amazon and Mediavine. Key modifiers include the OONWEIGHTFACTOR penalizing out-of-network posts (multiplier < 1.0) and AUTHORDIVERSITYDECAY, which exponentially reduces scores for the 2nd, 3rd, and 4th posts in the same batch. For creators with over 50K followers (Scenario 1), the focus is on high-weight actions like replies to big accounts and prioritizing in-network distribution to avoid the OON penalty. For new accounts starting from zero (Scenario 2), the main challenge is the OON penalty, requiring significantly higher raw engagement rates, focusing on Phoenix Retrieval via user/post embedding similarity, and becoming a niche expert. Dwell time is considered underrated, with longer posts getting fully read providing a strong dwell time signal, and the formula for the final score being the sum of (weight of engagement probability of engagement). The speaker outlines a 3-month practical playbook for growth: Week 1-4 (Build Signal via replies/original posts), Week 5-8 (Compound via consistent embedding), and Month 3+ (Scale).

Context: The video features the creator detailing the proprietary algorithm used by X (formerly Twitter) for ranking content, which they cracked using AI and reverse-engineering leaked internal scoring data. The creator, Jacky Chou, presents this information as a detailed Standard Operating Procedure (SOP) for content growth, breaking down the scoring system into positive and negative signals, key modifiers, and specific growth scenarios based on follower count.