RRepoGEO

REPOGEO REPORT · LITE

SandAI-org/MagiAttention

Default branch main · commit 696b4f20 · scanned 6/14/2026, 12:06:55 PM

GitHub: 848 stars · 58 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 SandAI-org/MagiAttention, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    attention-mechanism, transformer, long-context, linear-scalability, distributed-training, deep-learning, ai-training, heterogeneous-data, flash-attention, cuda, blackwell, ampere, pytorch
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://magi.sand.ai
  • mediumreadme#3
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    # MagiAttention

<p align="center">
    <a href="https://arxiv.org/pdf/2505.13211"></a>
    <a href="https://SandAI-org.github.io/MagiAttention/docs/"></a>
    <a href="https://SandAI-org.github.io/MagiAttention/docs/main/blog/magi_attn.html"></a>
    <a href="https://github.com/SandAI-org/MagiAttention/releases"></a>
    <a href="https://github.com/SandAI-org/Magi/LICENSE"></a>
</p>
    COPY-PASTE FIX
    # MagiAttention

MagiAttention is an open-source library providing a distributed attention mechanism designed for linear scalability with ultra-long context and heterogeneous data training.

<p align="center">
    <a href="https://arxiv.org/pdf/2505.13211"></a>
    <a href="https://SandAI-org.github.io/MagiAttention/docs/"></a>
    <a href="https://SandAI-org.github.io/MagiAttention/docs/main/blog/magi_attn.html"></a>
    <a href="https://github.com/SandAI-org/MagiAttention/releases"></a>
    <a href="https://github.com/SandAI-org/Magi/LICENSE"></a>
</p>

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 SandAI-org/MagiAttention
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
FlashAttention / FlashAttention-2
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. FlashAttention / FlashAttention-2 · recommended 1×
  2. LongFormer · recommended 1×
  3. BigBird · recommended 1×
  4. Performer · recommended 1×
  5. Reformer · recommended 1×
  • CATEGORY QUERY
    How to achieve linear scalability for attention mechanisms with ultra-long context in AI training?
    you: not recommended
    AI recommended (in order):
    1. FlashAttention / FlashAttention-2
    2. LongFormer
    3. BigBird
    4. Performer
    5. Reformer
    6. Hyena Hierarchy
    7. Monarch Mixer (M2)

    AI recommended 7 alternatives but never named SandAI-org/MagiAttention. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools support distributed attention for training deep learning models with heterogeneous data?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. PyTorch Distributed
    3. FairScale
    4. TensorFlow
    5. TensorFlow Distributed
    6. Keras
    7. DeepSpeed
    8. Ray
    9. Ray Core
    10. Ray Train
    11. Horovod

    AI recommended 11 alternatives but never named SandAI-org/MagiAttention. 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 SandAI-org/MagiAttention?
    pass
    AI named SandAI-org/MagiAttention explicitly

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

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

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

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SandAI-org/MagiAttention — 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