RRepoGEO

REPOGEO REPORT · LITE

nv-tlabs/LLaMA-Mesh

Default branch main · commit 82a36bc0 · scanned 6/24/2026, 6:52:46 PM

GitHub: 1,158 stars · 77 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
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
    Clarify 'LLaMA-Mesh' refers to 3D content, not GPU sharding

    Why:

    CURRENT
    Create 3D meshes by chatting.
    COPY-PASTE FIX
    LLaMA-Mesh is a research project focused on **3D mesh generation** using large language models, enabling conversational creation and understanding of 3D objects. This is distinct from distributed computing or GPU mesh sharding for LLMs.
  • mediumreadme#2
    Add a 'What problem does LLaMA-Mesh solve?' section

    Why:

    COPY-PASTE FIX
    ## What problem does LLaMA-Mesh solve?
Traditional 3D content creation is complex and requires specialized software. LLaMA-Mesh addresses this by enabling anyone to generate, understand, and interact with 3D meshes using natural language, leveraging the power of large language models. It bridges the gap between text and 3D, making 3D creation accessible and conversational.
  • mediumlicense#3
    Clarify the project's license(s) in the README

    Why:

    COPY-PASTE FIX
    ## License
This project is released under [Specify the actual license(s) here, e.g., a custom NVIDIA research license or a combination of licenses as found in the LICENSE file]. Please refer to the `LICENSE` file for full 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 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. Stable Diffusion · recommended 1×
  4. MVDream · recommended 1×
  5. Zero123 · recommended 1×
  • CATEGORY QUERY
    How can I generate 3D mesh models from natural language text prompts?
    you: not recommended
    AI recommended (in order):
    1. DreamFusion
    2. Magic3D
    3. Stable Diffusion
    4. MVDream
    5. Zero123
    6. Blockade Labs Skybox AI
    7. Spline
    8. Meshy.ai
    9. Luma AI
    10. Plask.ai
    11. Blender

    AI recommended 11 alternatives but never named nv-tlabs/LLaMA-Mesh. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What frameworks enable large language models to create and interpret 3D objects?
    you: not recommended
    AI recommended (in order):
    1. DreamFusion
    2. Magic3D
    3. Point-E
    4. GET3D
    5. Shap-E
    6. LGM

    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