X open-sources For You ranking pipeline with Phoenix transformer model

X released the code behind its For You recommendation feed on GitHub this week, giving practitioners a rare look at how a major social platform assembles and ranks content at scale. The repository, xai-org/x-algorithm, includes the full pipeline from candidate selection through final ranking, with the Phoenix transformer model—built on Grok architecture—at the center of the scoring system.

The feed draws from two sources: posts from accounts a user follows and posts from the global corpus surfaced through machine-learning retrieval. Phoenix evaluates each candidate by predicting user actions—likes, replies, reposts, clicks, and other engagement signals—then combines those probabilities into a final score that determines what surfaces in the timeline.

What stands out

1. 01Phoenix is a Grok-based transformer. The ranking model shares architecture with X's Grok LLM, adapted for engagement prediction rather than text generation. It outputs probabilities for multiple user actions simultaneously.
2. 02Two-stage retrieval precedes ranking. The system first pulls candidates from the user's follow graph and a separate ML-powered global search, then Phoenix scores the merged set. This two-stage design is standard for large-scale recommendation but rarely documented in production code.
3. 03Engagement signals are weighted explicitly. The repository exposes which actions contribute to the final score—likes, replies, reposts, clicks—and how they combine. Practitioners can trace exactly how a high-repost, low-like post might rank differently than the inverse.
4. 04Content filtering happens before ranking. The pipeline includes pre-ranking filters that remove certain content categories before Phoenix ever sees them. The code shows where those filters sit in the stack, though it does not detail every filter rule.