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name: x-retrieval-systems description: Use this skill when analyzing how the system narrows down 500 million daily tweets to a candidate pool of ~1,500. This is the "top-of-funnel" engineering that determines what is even eligible to be ranked. version: 1.0.0 license: MIT

X Retrieval Systems

Expertise in X's multi-stage retrieval architecture, including Earlybird search indexing and Phoenix-based ANN similarity search.

Context

Retrieval at X is split between In-Network (content from people you follow) and Out-of-Network (discovery). In-Network retrieval relies on Earlybird (Lucene-based search), while Out-of-Network retrieval uses Phoenix (Two-Tower embeddings) and ANN (Approximate Nearest Neighbor) algorithms like HNSW.

• Earlybird Search Index
• ANN & Embedding Retrieval

What it does

• Decodes In-Network Sourcing: Explains how Earlybird shards the index into Realtime, Protected, and Archive clusters.
• Explains Discovery Logic: Details how Two-Tower models enable "semantic" search for content you don't follow.
• Analyzes Latency: Breaks down the single-writer/multi-reader concurrency model that allows for sub-second global retrieval.

Example Trigger Prompts

• "/find-candidates how Earlybird shards real-time index"
• "/find-candidates retrieving 1,500 candidates from 500M tweets"
• "Role of HNSW in embedding-based discovery"
• "In-Network (Thunder) vs Out-of-Network (Phoenix) retrieval"
• "Trace 'Discovery' request: User Embedding → Candidate Source"