RRepoGEO

REPOGEO REPORT · LITE

lyuchenyang/Macaw-LLM

Default branch main · commit 06f6f22b · scanned 5/10/2026, 9:22:59 PM

GitHub: 1,591 stars · 131 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 lyuchenyang/Macaw-LLM, 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 more specific topics to highlight multi-modal LLM integration

    Why:

    CURRENT
    deep-learning, language-model, machine-learning, multi-modal-learning, natural-language-processing, neural-networks
    COPY-PASTE FIX
    deep-learning, language-model, machine-learning, multi-modal-learning, natural-language-processing, neural-networks, multi-modal-llm, unified-multi-modal-ai, foundation-models
  • mediumhomepage#2
    Add the paper link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://tinyurl.com/4rsexudv
  • lowreadme#3
    Clarify Macaw-LLM's role as a reference for multi-modal integration in the README

    Why:

    CURRENT
    Macaw-LLM is an exploratory endeavor that pioneers multi-modal language modeling by seamlessly combining image🖼️, video📹, audio🎵, and text📝 data, built upon the foundations of CLIP, Whisper, and LLaMA.
    COPY-PASTE FIX
    Macaw-LLM is an exploratory endeavor that pioneers multi-modal language modeling by seamlessly combining image🖼️, video📹, audio🎵, and text📝 data, built upon the foundations of CLIP, Whisper, and LLaMA. This project serves as a comprehensive reference and demonstration for researchers and developers exploring unified multi-modal AI architectures.

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 lyuchenyang/Macaw-LLM
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch Lightning · recommended 2×
  3. TensorFlow · recommended 2×
  4. Diffusers · recommended 1×
  5. Audiocraft · recommended 1×
  • CATEGORY QUERY
    How to build a language model that understands images, audio, and video inputs?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Diffusers
    3. Audiocraft
    4. PyTorch Lightning
    5. torchvision
    6. torchaudio
    7. TensorFlow
    8. Keras
    9. TensorFlow Hub
    10. MediaPipe
    11. OpenAI API
    12. GPT-4V
    13. DALL-E 3
    14. Whisper
    15. DeepSpeed
    16. JAX
    17. Flax

    AI recommended 17 alternatives but never named lyuchenyang/Macaw-LLM. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools help integrate diverse media types like video and text into a single AI model?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch Lightning
    3. TensorFlow
    4. OpenMMLab
    5. MMAction2
    6. MMFlow
    7. Perceiver IO
    8. PytorchVideo

    AI recommended 8 alternatives but never named lyuchenyang/Macaw-LLM. 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 lyuchenyang/Macaw-LLM?
    pass
    AI named lyuchenyang/Macaw-LLM explicitly

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

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

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

Embed your GEO score

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lyuchenyang/Macaw-LLM — 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