RRepoGEO

REPOGEO REPORT · LITE

EverMind-AI/MSA

Default branch main · commit 77fbdfde · scanned 6/26/2026, 4:28:20 AM

GitHub: 3,482 stars · 225 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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

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

OVERALL DIRECTION
  • highabout#1
    Refine the 'About' description for unambiguous categorization

    Why:

    CURRENT
    Memory Sparse Attention - A scalable, end-to-end trainable latent-memory framework for 100M-token contexts.
    COPY-PASTE FIX
    Memory Sparse Attention (MSA) for LLMs: A scalable, end-to-end trainable latent-memory framework for 100M-token contexts in large language models.
  • highlicense#2
    Add a LICENSE file to clarify usage rights

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with the MIT License text. Additionally, add a line to the README, e.g., "This project is licensed under the MIT License - see the LICENSE file for details."

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
Longformer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Longformer · recommended 2×
  2. LongRoPE · recommended 1×
  3. mit-han-lab/streaming-llm · recommended 1×
  4. Mixtral 8x7B · recommended 1×
  5. Grok-1 · recommended 1×
  • CATEGORY QUERY
    How to extend LLM context window beyond 1M tokens efficiently?
    you: not recommended
    AI recommended (in order):
    1. LongRoPE
    2. StreamingLLM (mit-han-lab/streaming-llm)
    3. Mixtral 8x7B
    4. Grok-1
    5. FAISS (facebookresearch/faiss)
    6. Pinecone
    7. Weaviate (weaviate/weaviate)
    8. Qdrant (qdrant/qdrant)
    9. LangChain (langchain-ai/langchain)
    10. LlamaIndex (run-llama/llama_index)
    11. H-Transformer
    12. Longformer
    13. FlashAttention (Dao-AILab/flash-attention)
    14. PagedAttention
    15. vLLM (vllm-project/vllm)

    AI recommended 15 alternatives but never named EverMind-AI/MSA. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking scalable, end-to-end trainable memory frameworks for extremely long context language models.
    you: not recommended
    AI recommended (in order):
    1. Transformer-XL
    2. Longformer
    3. Reformer
    4. Performer
    5. BigBird
    6. Differentiable Neural Computer - DNC
    7. REALM
    8. RAG

    AI recommended 8 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.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/EverMind-AI/MSA.svg)](https://repogeo.com/en/r/EverMind-AI/MSA)
HTML
<a href="https://repogeo.com/en/r/EverMind-AI/MSA"><img src="https://repogeo.com/badge/EverMind-AI/MSA.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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