RRepoGEO

REPOGEO REPORT · LITE

nv-tlabs/LLaMA-Mesh

Default branch main · commit 82a36bc0 · scanned 5/14/2026, 6:37:40 AM

GitHub: 1,150 stars · 78 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 nv-tlabs/LLaMA-Mesh, 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 'What is LLaMA-Mesh?' section to clarify its purpose and counter misinterpretations

    Why:

    COPY-PASTE FIX
    ## What is LLaMA-Mesh?
LLaMA-Mesh is a pioneering framework that unifies 3D mesh generation with large language models, allowing you to create and understand 3D objects through natural language conversations. **It is specifically designed for 3D content creation and understanding, not for distributed LLM inference or deployment.**
  • mediumreadme#2
    Enhance the README's opening to highlight the unique LLM-3D integration

    Why:

    CURRENT
    Create 3D meshes by chatting.
    COPY-PASTE FIX
    Create 3D meshes by chatting. LLaMA-Mesh is the first to demonstrate that LLMs can be fine-tuned to acquire complex spatial knowledge for 3D mesh generation in a text-based format, effectively unifying the 3D and text modalities.
  • lowreadme#3
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is licensed under the terms specified in the [LICENSE](LICENSE) file. Please refer to the file for full details on the applicable licenses.

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 nv-tlabs/LLaMA-Mesh
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DreamFusion
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DreamFusion · recommended 2×
  2. Magic3D · recommended 2×
  3. Shap-E · recommended 2×
  4. Point-E · recommended 2×
  5. Blender · recommended 1×
  • CATEGORY QUERY
    How can I use large language models to generate 3D mesh objects from text descriptions?
    you: not recommended
    AI recommended (in order):
    1. DreamFusion
    2. Magic3D
    3. Shap-E
    4. Point-E
    5. Blender
    6. GPT-4
    7. Claude 3 Opus
    8. Unity
    9. Unreal Engine
    10. ChatGPT
    11. Llama 3
    12. Neuralangelo

    AI recommended 12 alternatives but never named nv-tlabs/LLaMA-Mesh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best methods for generating 3D models directly from natural language prompts?
    you: not recommended
    AI recommended (in order):
    1. DreamFusion
    2. Magic3D
    3. Point-E
    4. Shap-E
    5. Luma AI
    6. Spline

    AI recommended 6 alternatives but never named nv-tlabs/LLaMA-Mesh. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 nv-tlabs/LLaMA-Mesh?
    pass
    AI named nv-tlabs/LLaMA-Mesh explicitly

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

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

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

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nv-tlabs/LLaMA-Mesh — 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