RRepoGEO

REPOGEO REPORT · LITE

EverMind-AI/MSA

Default branch main · commit 77fbdfde · scanned 5/15/2026, 12:24:03 PM

GitHub: 3,417 stars · 217 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
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 EverMind-AI/MSA, 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
    Clarify "MSA" in README H1 and description

    Why:

    CURRENT
    # MSA: Memory Sparse Attention
*A scalable, end-to-end trainable latent-memory framework for 100M-token contexts*
    COPY-PASTE FIX
    # MSA: Memory Sparse Attention for LLMs
*A scalable, end-to-end trainable latent-memory framework for **Large Language Models (LLMs)**, enabling **100M-token contexts**.*
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    llm, large-language-models, long-context, memory-sparse-attention, attention-mechanisms, deep-learning, machine-learning, nlp, ai-memory, scalable-llm
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    (Create a LICENSE file, e.g., MIT or Apache-2.0, and add it to the repository root.)

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
0 / 2
0% of queries surface EverMind-AI/MSA
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 1×
  2. Weaviate · recommended 1×
  3. Chroma · recommended 1×
  4. Faiss · recommended 1×
  5. GPT-4o / GPT-4 Turbo · recommended 1×
  • CATEGORY QUERY
    How to extend LLM effective context length beyond 1M tokens efficiently?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Chroma
    4. Faiss
    5. GPT-4o / GPT-4 Turbo
    6. Claude 3 Opus / Sonnet / Haiku
    7. Gemini 1.5 Pro
    8. Longformer
    9. BigBird
    10. Reformer
    11. RWKV
    12. Mamba
    13. Hiera

    AI recommended 13 alternatives but never named EverMind-AI/MSA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an end-to-end trainable framework for managing extremely long LLM contexts.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. FlashAttention-2
    3. xFormers
    4. PyTorch
    5. LongRoPE
    6. NTK-RoPE
    7. YaRN
    8. DeepSpeed
    9. JAX
    10. Flax
    11. Megatron-LM

    AI recommended 11 alternatives but never named EverMind-AI/MSA. 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 EverMind-AI/MSA?
    pass
    AI named EverMind-AI/MSA explicitly

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

  • If a team adopts EverMind-AI/MSA in production, what risks or prerequisites should they evaluate first?
    pass
    AI named EverMind-AI/MSA 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 EverMind-AI/MSA solve, and who is the primary audience?
    pass
    AI named EverMind-AI/MSA explicitly

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

Embed your GEO score

Drop this badge into the README of EverMind-AI/MSA. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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EverMind-AI/MSA — 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