RRepoGEO

REPOGEO REPORT · LITE

NVlabs/vibetensor

Default branch main · commit fe85461f · scanned 6/15/2026, 12:28:11 PM

GitHub: 628 stars · 48 forks

AI VISIBILITY SCORE
33 /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
2 / 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 NVlabs/vibetensor, 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
    Explicitly differentiate VibeTensor from 3D pose estimation projects in README

    Why:

    COPY-PASTE FIX
    To be clear, VibeTensor is *not* related to the VIBE project for 3D human pose and shape estimation; its name refers to 'Vibe Coding' – the process of generating code through iterative agentic feedback loops.
  • hightopics#2
    Update topics to accurately reflect core purpose and remove misleading ones

    Why:

    CURRENT
    cuda, cutlass, machine-learning, pytorch, vibe-coding
    COPY-PASTE FIX
    cuda, cutlass, machine-learning, pytorch, ai-generated-code, agentic-programming, deep-learning-systems, code-generation, runtime-engineering
  • mediumabout#3
    Enhance the repository description to emphasize its unique AI-generated nature and research focus

    Why:

    CURRENT
    Our first fully AI generated deep learning system
    COPY-PASTE FIX
    A research deep learning system, fully generated by AI agents, demonstrating the capabilities of AI-assisted software engineering for complex system development.

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 NVlabs/vibetensor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ludwig
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ludwig · recommended 1×
  2. AutoKeras · recommended 1×
  3. Google Cloud AutoML · recommended 1×
  4. Hugging Face AutoTrain · recommended 1×
  5. PyTorch Lightning · recommended 1×
  • CATEGORY QUERY
    Need tools for automatically generating deep learning system software from high-level specifications.
    you: not recommended
    AI recommended (in order):
    1. Ludwig
    2. AutoKeras
    3. Google Cloud AutoML
    4. Hugging Face AutoTrain
    5. PyTorch Lightning
    6. Keras Tuner

    AI recommended 6 alternatives but never named NVlabs/vibetensor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to achieve low-level control over deep learning runtime and CUDA memory management?
    you: not recommended
    AI recommended (in order):
    1. CUDA C/C++
    2. cuDNN
    3. cuBLAS
    4. TensorRT
    5. PyTorch
    6. TensorFlow
    7. JAX
    8. DLPack

    AI recommended 8 alternatives but never named NVlabs/vibetensor. 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 NVlabs/vibetensor?
    pass
    AI did not name NVlabs/vibetensor — likely talking about a different project

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

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

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

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NVlabs/vibetensor — 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