Open Source

X Algorithm Open Source 2026

Elon Musk open-sources X's "For You" feed algorithm powered by Grok AI. A complete technical breakdown of how 600 million users' feeds are curated.

January 20, 2026
15 min read
600M+ Users Affected

What Happened?

On January 10, 2026, Elon Musk announced that X (formerly Twitter) would open-source its entire recommendation algorithm. A week later, the complete codebase was published on GitHub under the Apache 2.0 license. This includes all code used to determine what organic posts and advertisements appear in your "For You" feed.

"We will make the new X algorithm, including all code used to determine what organic and advertising posts are recommended to users, open source in 7 days. This will be repeated every 4 weeks, with comprehensive developer notes."

— Elon Musk, January 10, 2026

Timeline

January 10, 2026

Elon Musk announces algorithm will go open source in 7 days

January 17, 2026

Full algorithm code released on GitHub (xai-org/x-algorithm)

Every 4 Weeks

Promised updates with comprehensive developer notes

Technology Stack

Languages

Rust62.9%
Python37.1%

Details

  • Apache 2.0 License
  • Based on xAI's Grok-1 Architecture
  • 20,000 GPUs at Colossus Data Center
  • Monthly Updates Promised

How the Algorithm Works

STAGE 1

Query Hydration

Fetches user engagement history and metadata to understand preferences

STAGE 2

Candidate Sourcing

Retrieves posts from Thunder (in-network) and Phoenix Retrieval (out-of-network)

STAGE 3

Candidate Enrichment

Adds core post data, author information, and media details

STAGE 4

Pre-Scoring Filtering

Removes duplicates, aged content, blocked authors, and muted keywords

STAGE 5

Scoring

Phoenix transformer predicts engagement probabilities with weighted scoring

STAGE 6

Selection

Sorts by score and selects top K candidates for your feed

STAGE 7

Post-Selection Validation

Final visibility checks and deduplication before display

Key Features

Zero Hand-Engineered Features

The system relies entirely on Grok-based transformer to learn relevance from user engagement sequences

Candidate Isolation

During ranking, posts cannot attend to each other, ensuring consistent, cacheable scores

15+ Action Predictions

Predicts probabilities for likes, replies, reposts, clicks, blocks, mutes, reports, and more

Real-Time Processing

Thunder enables sub-millisecond in-network lookups via in-memory storage

Modular Architecture

Composable pipeline framework allowing easy addition of new components

20,000 GPUs

Powered by xAI's Colossus data center in Memphis for massive scale inference

Why This Matters for Crypto Traders

Crypto Twitter Influence

X (formerly Twitter) is the primary social platform for crypto news, alpha, and community engagement. Understanding how the algorithm works helps you:

  • Get crypto news and alpha faster in your feed
  • Understand why certain posts go viral
  • Optimize your own crypto content for visibility
  • Filter out noise and focus on quality signals

Transparency Wins

Open-sourcing algorithms aligns with crypto's core values of transparency and decentralization. This move sets a precedent for:

  • Auditable social media algorithms
  • Community-driven improvements
  • Reduced manipulation concerns
  • Trust through verification

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Frequently Asked Questions

Where can I view the X algorithm source code?

The complete source code is available on GitHub at github.com/xai-org/x-algorithm under the Apache 2.0 license.

What is Phoenix in the X algorithm?

Phoenix is the Grok-based transformer model that predicts engagement probabilities for each post. It uses a two-tower retrieval model with user and candidate embeddings.

What is Thunder in the X algorithm?

Thunder is an in-memory post store that enables sub-millisecond lookups for in-network content. It consumes Kafka events and maintains per-user post collections.

How often will X update the open-source code?

Elon Musk promised updates every 4 weeks with comprehensive developer notes explaining what changed.

Does the algorithm use hand-engineered features?

No. The system relies entirely on the Grok-based transformer to learn relevance from user engagement sequences with zero hand-engineered features.

Sources

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