RRepoGEO

REPOGEO REPORT · LITE

jquesnelle/yarn

Default branch master · commit 995db5b5 · scanned 5/24/2026, 7:13:23 AM

GitHub: 1,719 stars · 132 forks

AI VISIBILITY SCORE
66 /100
Needs work
Category recall
1 / 2
Avg rank #2.0 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface jquesnelle/yarn, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a disambiguation statement to the README title

    Why:

    CURRENT
    # YaRN
    COPY-PASTE FIX
    # YaRN: Efficient Context Window Extension for Large Language Models (NOT the JavaScript package manager)
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    large-language-models, llm, context-window, deep-learning, machine-learning, nlp, yarn, rope-scaling
  • mediumhomepage#3
    Add the paper's arXiv link to the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/abs/2309.00071

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
1 / 2
50% of queries surface jquesnelle/yarn
Avg rank
#2.0
Lower is better. #1 = top recommendation.
Share of voice
6%
Of all named tools, what % are you?
Top rival
LongRoPE
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LongRoPE · recommended 1×
  2. NTK-aware Scaled RoPE · recommended 1×
  3. FlashAttention-2 · recommended 1×
  4. Multi-Query Attention (MQA) / Grouped-Query Attention (GQA) · recommended 1×
  5. Sparse Attention Mechanisms · recommended 1×
  • CATEGORY QUERY
    How to efficiently increase the context window size of large language models?
    you: #2
    AI recommended (in order):
    1. LongRoPE
    2. YaRN ← you
    3. NTK-aware Scaled RoPE
    4. FlashAttention-2
    5. Multi-Query Attention (MQA) / Grouped-Query Attention (GQA)
    6. Sparse Attention Mechanisms
    7. Retrieval-Augmented Generation (RAG)
    Show full AI answer
  • CATEGORY QUERY
    Seeking methods to enable large language models to process very long documents efficiently.
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. Chroma (chroma-core/chroma)
    4. FAISS (facebookresearch/faiss)
    5. LangChain (langchain-ai/langchain)
    6. LlamaIndex (run-llama/llama_index)
    7. OpenAI API
    8. Anthropic Claude
    9. Hugging Face Transformers (huggingface/transformers)
    10. Google Gemini 1.5 Pro
    11. OpenAI GPT-4 Turbo

    AI recommended 11 alternatives but never named jquesnelle/yarn. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of jquesnelle/yarn?
    pass
    AI named jquesnelle/yarn explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts jquesnelle/yarn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jquesnelle/yarn explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo jquesnelle/yarn solve, and who is the primary audience?
    pass
    AI named jquesnelle/yarn explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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jquesnelle/yarn — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite