X Algorithm Post Writing

X Algorithm: Writing Posts That Get Ranked

A single source of truth for writing on X, derived directly from the open-sourced For You algorithm. No folklore, no growth-hack myths — only what the actual ranker, retrieval system, and content classifiers reward or punish.

How the Feed Actually Works

Every For You impression is the output of this pipeline:

Sources                Filters               Scoring                 Selection
─────────              ───────               ───────                 ─────────
Thunder (in-network)   age, vf, muted        Phoenix transformer     top-K by score
Phoenix (OON ANN)      blocks, dedupe        → P(19 actions)         author diversity
ads / wtf / prompts    seen/served           weighted sum            ads blender
                       subscription gate     OON multiplier

Two ways into a user's feed:

1. Thunder (in-network) — they follow you. Sub-millisecond lookup. Always preferred.
2. Phoenix retrieval (out-of-network) — a two-tower model embeds your post and the user's engagement history into the same space, then ANN-searches. You land here when your content lives in the topical neighborhood of posts the user recently engaged with.

Then Phoenix ranking (a Grok-based transformer) predicts probabilities for ~19 engagement actions per candidate, the Weighted Scorer combines them, Author Diversity decays repeated authors, OON Scorer down-weights out-of-network, and the top-K wins.

There are no hand-engineered relevance features anymore. The transformer learns from the user's UserActionSequence (their recent aggregated actions). That is the entire feature set.

The 19 Actions That Define Your Score

The ranker predicts a probability for each. Final score = Σ(weight × P(action)).

Positive signals (you want these)

Negative signals (these subtract from your score)

The negative weights are real and subtractive — a post that gets scrolled past by many users actively pushes its own score down. "No engagement" is not neutral; not_dwelled is negative.

What This Means For How You Write

1. Optimize for diverse engagement, not just likes

The model weights 19 actions. A post that earns one reply and one share and one quote outperforms a post with three likes. Write things people want to reply to, quote, save, or DM.

2. The hook has to stop the scroll

Every scroll-past is a not_dwelled negative. The first visible line (and the thumbnail of any media) is the entire battle. If the user doesn't dwell, you don't just get zero — you go negative.

3. Longer is fine if it earns the dwell

cont_dwell_time is a continuous weight. A post people read for 12 seconds beats one read for 2. But that only works if the hook earns the read — pad-for-length kills you on not_dwelled.

4. Make it quotable, not just likeable

Quotes trigger quote_score + downstream quoted_click_score + quoted_vqv_score. A take that begs for "this, but also…" outperforms a self-contained one.

5. Convert viewers to followers

follow_author is heavily weighted. Every post should make the case for following you — clear identity, distinct voice, on-niche signal. Profile + pinned post matter because profile_click also scores.

6. Avoid anything that risks block / mute / report / not_interested

Rage-bait, slop, misleading hooks, engagement farming — these maximize short-term click but blow up not_interested/mute, which are weighted negatively. Net is often negative.

7. Video must be substantive

vqv only counts when video duration exceeds the minimum threshold. Sub-threshold loops literally cannot earn the video reward. There's also a quoted_vqv reward when your video is quoted — make videos that beg to be quoted.

8. Images: thumbnail-test everything

photo_expand is its own positive signal. The thumbnail has to make someone tap. Crops, faces, contrast, clear focal point.

9. Make content shareable, not just consumable

share, share_via_dm, share_via_copy_link are three independent rewards. Frameworks, lists, screenshots of useful info, before/after, "saved this for later" content — these all score.

What Will Quietly Kill Your Post

These run before scoring — if any fires for a viewer, your post is dropped from their candidate set entirely.

Practical implications:

• A "controversial topic hook" is also a "muted keyword" landmine. Calibrate.
• One conversation, one branch — replying 10 times to your own thread doesn't multiply reach.
• Mass-rebloging your own old content gets collapsed.

Content Understanding (Grox)

Beyond the ranker, a separate grox/ pipeline runs VLM-based classifiers on posts:

• Banger initial screen — a vision-language model scores quality_score (0–1), threshold 0.4 for positive. Also emits slop_score and has_minor_score, plus taxonomy categories.
• Post safety screen deluxe — VLM safety pass for PTOS policy.
• Spam detection — aggressive on accounts <1K followers replying. If you reply-spam from a small account, the spam classifier flags you and the in_reply_user_follower_count bucket determines logging.
• PTOS policy / safety_ptos_category — policy enforcement.
• Multimodal post embedder (v2 / v5) — multimodal embeddings used downstream.

So a post is also being judged on visual + textual quality by an LLM. AI-generated slop is detected and scored against you. Posts with minors flagged. Topical categorization happens automatically — you don't pick the category, the classifier does.

Distribution Mechanics

In-network vs Out-of-network

Out-of-network candidates are multiplied by OON_WEIGHT_FACTOR (< 1.0) in OONScorer / RankingScorer. In-network always wins on equal scores. The single highest-leverage growth move on X remains: be followed by people in the audience you want to reach.

Two exceptions where OON penalty softens:

1. Topic match — if the viewer follows topics that match the post, TopicOonWeightFactor replaces the regular OON factor (typically higher → easier OON reach).
2. New users — eligible new users (account younger than a threshold AND following at least NEW_USER_MIN_FOLLOWING) get NEW_USER_OON_WEIGHT_FACTOR instead.

Implication: Topical, categorizable posts travel further OON than generic ones. The Phoenix two-tower retrieval needs a coherent neighborhood to embed your post into.

Mutual follow Jaccard (MinHash)

MutualFollowJaccardHydrator computes the Jaccard similarity between the viewer's follow graph and the author's follow graph via MinHash (≥256 hashes). Authors whose graph overlaps the viewer's get a stronger signal. Tribe matters. Posts from authors followed by accounts the viewer also follows have a structural advantage.