x-algorithm-optimizer

X Algorithm Optimizer

Optimize content for X's 2026 neural recommendation system. Based on technical analysis of the xai-org/x-algorithm codebase, this skill provides both tactical templates for immediate use and deep architectural understanding for strategic advantage.

The Paradigm Shift: Neural > Heuristics

X has abandoned the legacy heuristic approach (counting followers, matching hashtags, PageRank reputation). The new system uses end-to-end neural architecture:

• Thunder: In-network retrieval (posts from accounts you follow)
• Phoenix: Out-of-network retrieval + neural ranking (the "For You" discovery engine)
• Grok-1 Transformer: The "Heavy Ranker" that reads and scores every candidate post

The implication: You can't "hack" hashtags or posting times anymore. The algorithm reads your content semantically and predicts specific engagement probabilities. Alignment beats manipulation.

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Quick Start

Analyze a post: "Score this X post against the algorithm: [paste]"

Generate optimized content: "Write an algorithm-optimized X post about [topic]"

Deep dive: "Explain why this post would/wouldn't perform using Phoenix mechanics"

Debug underperformance: "Why isn't this post getting reach? Analyze against weighted scorer"

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Cheat Sheet

DO:

• Maximize P(reply): questions, fill-blanks, "hot take + nuance"
• Include media (image/video) for +P(video_view), +P(photo_expand)
• Space posts 4-6+ hours apart (author diversity penalty)
• Post when audience active (time decay is exponential)
• Build concentrated niche (strengthens your User Tower embedding)

DON'T:

• Use irrelevant hashtags (Grok detects semantic mismatch)
• Post rage-bait (high blocks destroy score even with high engagement)
• Link-only posts (forfeit all media probability terms)
• Posting sprees (3rd post = ~10% reach of 1st)
• Engagement pods (Grok detects coordination patterns)

TARGET: <0.5% block rate. Above 1% = systematic demotion.

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The Weighted Scorer Formula

Every post receives a score computed as:

Score = Σ (w_i × P(action_i))

Where the Phoenix transformer predicts probabilities for each action type, and configured weights determine their impact.

Engagement Hierarchy (Inferred Weights)

Tier	Actions	Est. Weight	Strategic Implication
1. Multipliers	P(reply), P(repost), P(quote)	w > 10.0	High friction, network-extending actions. Algorithm prioritizes conversation.
2. Validators	P(like), P(video_view), P(photo_expand)	w ≈ 1.0	Lower friction, validates relevance. Necessary but insufficient alone.
3. Signals	P(click), P(dwell), P(profile_click)	w < 1.0	Passive signals for model training. Low weight prevents clickbait farming.
4. Destroyers	P(block), P(mute), P(report)	w ≈ -1000	Catastrophic negative weight. One block negates hundreds of likes.

The Math in Practice

Engaging post:

P(reply)=0.15, P(like)=0.08, P(repost)=0.02, P(block)=0.001
Score = (15×0.15) + (1×0.08) + (12×0.02) + (-1000×0.001)
Score = 2.25 + 0.08 + 0.24 - 1.0 = 1.57 ✓

Rage-bait post:

P(reply)=0.25, P(like)=0.05, P(repost)=0.03, P(block)=0.02
Score = (15×0.25) + (1×0.05) + (12×0.03) + (-1000×0.02)
Score = 3.75 + 0.05 + 0.36 - 20.0 = -15.84 ✗ (demoted)

Key insight: The rage-bait generated MORE engagement but scored WORSE because of the 2% block rate.

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The Seven Alpha Mechanics

These are the specific, hard-coded mechanisms that determine content fate—derived directly from the codebase.

#	Mechanic	Key Insight	Action
1	Candidate Isolation	Posts scored independently, not on a curve	Focus on intrinsic quality
2	Author Diversity Penalty	0.3-0.7x penalty per consecutive post	Space posts 4-6+ hours apart
3	Negative Signal Asymmetry	Blocks weighted ~1000x vs likes	Target <0.5% block rate
4	Multimodal Bonus	Media adds P(video_view), P(photo_expand)	Always include media
5	Grok Semantic Reading	LLM detects topic mismatch, spam patterns	Hashtags must match content
6	Two-Tower Retrieval	dot(User, Item) vectors for discovery	Build concentrated niche first
7	Time Decay	Exponential decay after 24-48h	Post when audience is active

1. Candidate Isolation (Fair Scoring)

The Phoenix transformer uses a custom attention mask that prevents "context bleeding" between posts in the same scoring batch. Each post is scored independently against only the user context.

What this means: Your content is judged solely on its relationship with the viewer—not graded on a curve against whatever else is in that millisecond's batch. Quality is intrinsic.

2. Author Diversity Penalty (Anti-Spam)

The AuthorDiversityScorer tracks which authors have already been selected for the timeline. If Author A appears at position 1, their next post gets an attenuation penalty (estimated 0.3-0.7x) for positions 2+.

Optimal strategy:

• Space posts 4-6+ hours apart to reset fatigue
• Quality over quantity is mathematically enforced
• "Posting sprees" compound penalties—your 3rd post in an hour may score 0.3 × 0.3 = 0.09x

3. Negative Signal Asymmetry (Harm Reduction)

Negative weights are ~1000x positive weights. The system operates on harm reduction over engagement maximization.

The propagation effect: A block doesn't just lower one post's score—it:

• Affects your reputation within that user's cluster
• Demotes future content to similar users
• Contributes to a hidden "author health" score

Safe zone: Target <0.5% block rate. Above 1% = systematic demotion.

4. Multimodal Shadow Algorithms

Phoenix predicts media-specific probabilities: P(video_view), P(photo_expand). These are distinct scoring terms.

Text-only posts forfeit these entirely:

Text:  Score = w_reply×P(reply) + w_like×P(like)
Media: Score = w_reply×P(reply) + w_like×P(like) + w_video×P(video_view)

The media bonus is structural, not optional.

5. Grok Semantic Understanding

The Heavy Ranker is adapted from Grok-1. It doesn't just count features—it reads the content.

What Grok detects:

• Semantic topic relevance to user's interest embedding
• Tone/style matching (formal, casual, technical, humorous)
• Sarcasm and irony patterns
• Hashtag-content mismatches (spam signal)
• Linguistic authenticity markers

The death of hacks: Irrelevant trending hashtags now hurt you. Grok sees the semantic mismatch and flags it as spam-like behavior.

6. Two-Tower Retrieval (Cold Start Solution)

Out-of-network discovery uses a Two-Tower Neural Network:

• User Tower: Encodes your engagement history, demographics, negative feedback into vector U
• Item Tower: Encodes post content, media, author features into vector I
• Similarity: dot(U, I) determines retrieval

Cold start strategy: New accounts have weak User Tower embeddings. Solutions:

1. Ride trending topics (aligns with global context vector)
2. Build niche first (concentrated topic cluster builds clear embedding)
3. Engage authentically (your reply history shapes your User Tower)

7. Time Decay Filter

The AgeFilter applies exponential decay. Content from 24 hours ago can survive with strong signals; 48+ hours requires exceptional engagement.

Optimal timing: Post when your audience is active to maximize early engagement velocity. Early signals compound through the decay function.

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Content Generation Framework

digraph content_flow {
    rankdir=TB;
    node [shape=box];

    goal [label="What's your goal?" shape=diamond];
    reach [label="Max Reach\nOptimize P(repost)+P(quote)"];
    engage [label="Engagement Depth\nOptimize P(reply)"];
    grow [label="Follower Growth\nOptimize P(profile_click)"];
    safe [label="Safe Growth\nMinimize P(block)"];
    reply_opt [label="Apply reply patterns\n(questions, fill-blanks, controversy)"];
    media [label="Add media\n(+P(video_view), +P(photo_expand))"];
    scan [label="Negative signal scan\n(<0.5% block rate?)" shape=diamond];
    post [label="Post" shape=doublecircle];
    revise [label="Revise content"];

    goal -> reach [label="virality"];
    goal -> engage [label="replies"];
    goal -> grow [label="followers"];
    goal -> safe [label="safety"];
    reach -> reply_opt;
    engage -> reply_opt;
    grow -> reply_opt;
    safe -> reply_opt;
    reply_opt -> media;
    media -> scan;
    scan -> post [label="yes"];
    scan -> revise [label="no"];
    revise -> scan;
}

Step 1: Choose Optimization Target

Goal	Primary Metric	Format Bias
Maximum reach	P(repost) + P(quote)	Shareable insights, data, frameworks
Engagement depth	P(reply)	Questions, debates, incomplete statements
Follower growth	P(profile_click)	Thread hooks, expertise signals
Safe growth	Low P(block)	Nuanced takes, inclusive framing

Step 2: Apply the Reply Optimization

P(reply) carries ~10-15x weight. Structure content to maximize it:

High P(reply) patterns:

• Open questions demanding specific experience: "What's your biggest [X] failure?"
• Fill-in-the-blank: "The most underrated skill is ___"
• Intentional incompleteness: List with obvious gap
• Nuanced controversy: "Hot take: [statement]. But here's the nuance..."
• Correctability: Slightly wrong statement experts will correct

Low P(reply) patterns:

• Rhetorical questions (no answer expected)
• Perfect statements (nothing to add)
• Closed conclusions ("In summary...")

Step 3: Add Media (Structural Bonus)

Media adds probability terms you otherwise forfeit.

Image optimization:

• Vertical aspect ratios get cropped → forces P(photo_expand)
• Data visualizations invite inspection
• High contrast catches scroll

Video optimization:

• Hook in first 3 seconds (before scroll-away)
• Captions for sound-off (80% watch muted)
• Loop-worthy endings increase replay

Step 4: Negative Signal Scan

Before posting, check:

• Could any audience segment find this block-worthy?
• Is the engagement mechanism genuine or annoying?
• Does controversial content include nuance to reduce polarization?

The rule: If your post might generate 5+ blocks per 1000 impressions, reconsider.

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Thread Strategy (Per-Tweet Scoring)

Threads are scored per tweet. The algorithm evaluates Tweet 1 independently.

Tweet 1 (The Hook):

• Must work standalone—this is what gets scored for reach
• Include the value proposition clearly
• Don't waste on "Thread!" or "🧵" alone

Tweet 2-N (The Delivery):

• Scored only for users who click through
• Deliver on the hook's promise
• Each tweet should have standalone value

Final Tweet (The CTA):

• Clear call to action (follow, comment, share)
• Summary of key insight
• Bookmark-worthy standalone

Anti-pattern: "Thread! 🧵" as Tweet 1 with thin content = spam signal to Grok.

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Platform Specs (Quick Reference)

Element	Optimal	Why
Characters	71-100 (max 280)	No "Show more" friction
Hashtags	0-1, semantically matched	Grok detects mismatch as spam
Images	1200×675px or vertical	Full preview; vertical forces expand
Video	<2:20, hook in 3s, captioned	Autoplay + 80% watch muted
Media source	Native upload only	Links don't trigger media probabilities

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Debugging Underperformance

When content underperforms, diagnose against the weighted scorer:

Low Reach Despite Engagement

Likely cause: High P(block) rate negating positive signals Check: Is content polarizing? Does it generate negative reactions alongside positive?

High Impressions, Low Engagement

Likely cause: Weak P(reply) and P(repost) optimization Check: Does content invite response? Is it shareable?

New Account Struggling

Likely cause: Weak User Tower embedding, cold start problem Solution: Build concentrated topic presence, ride trends, engage authentically

Declining Reach Over Time

Likely cause: Author health score degradation from accumulated negative signals Solution: Audit recent content for block-generating patterns, rebuild with safer content

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Anti-Patterns (Algorithmic Self-Sabotage)

Engagement Pods

Grok detects artificial coordination patterns. Clustered engagement from the same accounts with unusual timing = spam signal.

Hashtag Stuffing

Multiple irrelevant hashtags = semantic mismatch detection = spam adjacent score.

Link-Only Posts

Zero media probability terms + low text engagement = structural disadvantage.

Rage-Farming

High engagement + high blocks = net negative score. The math doesn't lie.

Posting Sprees

Author diversity penalty compounds. Post #3 in an hour may reach 10% of Post #1's audience.

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Reference Files

File	Contents
references/phoenix-architecture.md	Two-Tower model, Grok adaptation, embedding dynamics
references/weighted-scorer.md	Complete weight hierarchy, probability math, examples
references/post-templates.md	12+ proven formats with algorithm alignment notes

Analysis Script

Run the analyzer directly:

python scripts/analyze_x_post.py

Or import in Python:

from scripts.analyze_x_post import analyze_post, format_report, calculate_weighted_score

# Analyze a post
result = analyze_post("Your post text", include_media=True, media_type="image")
print(format_report(result))

# Calculate raw weighted score
score = calculate_weighted_score(p_reply=0.15, p_like=0.08, p_block=0.001)

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The Meta-Strategy

Old paradigm: Game the algorithm with hacks (hashtags, timing, pods) New paradigm: Align with the neural network's objective function

The algorithm optimizes for:

1. Conversation (P(reply) weighted highest)
2. Network extension (P(repost), P(quote))
3. User satisfaction (negative signals weighted catastrophically)
4. Semantic relevance (Grok reads everything)

Your strategy: Create content that genuinely maximizes these. The era of manipulation is over; the era of alignment has begun.