RRepoGEO

REPOGEO REPORT · LITE

tugstugi/dl-colab-notebooks

Default branch master · commit 3622c82d · scanned 5/10/2026, 8:03:01 PM

GitHub: 1,778 stars · 461 forks

AI VISIBILITY SCORE
28 /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
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 tugstugi/dl-colab-notebooks, 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
    Reposition README opening to clarify repo type

    Why:

    CURRENT
    Try out deep learning models online on Colab with a single click.
    COPY-PASTE FIX
    This repository provides a curated collection of ready-to-run deep learning models as Google Colab notebooks, enabling quick experimentation with a single click. It's designed for practitioners and learners who want to explore various models without setting up complex environments.
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root containing the text of your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://colab.research.google.com/github/tugstugi/dl-colab-notebooks/

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 tugstugi/dl-colab-notebooks
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle Notebooks
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle Notebooks · recommended 2×
  2. Amazon SageMaker Studio Lab · recommended 2×
  3. Azure Machine Learning Notebooks · recommended 2×
  4. Google Colaboratory (Colab) · recommended 1×
  5. Paperspace Gradient · recommended 1×
  • CATEGORY QUERY
    How can I quickly experiment with deep learning models in an online notebook environment?
    you: not recommended
    AI recommended (in order):
    1. Google Colaboratory (Colab)
    2. Kaggle Notebooks
    3. Paperspace Gradient
    4. Deepnote
    5. Amazon SageMaker Studio Lab
    6. Azure Machine Learning Notebooks

    AI recommended 6 alternatives but never named tugstugi/dl-colab-notebooks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good options for running text-to-speech deep learning models in cloud notebooks?
    you: not recommended
    AI recommended (in order):
    1. Google Colaboratory (Colab) Pro/Pro+
    2. Kaggle Notebooks
    3. Amazon SageMaker Studio Lab
    4. Google Cloud Vertex AI Workbench (Managed Notebooks)
    5. AWS SageMaker Studio
    6. Azure Machine Learning Notebooks
    7. RunPod.io
    8. Vast.ai

    AI recommended 8 alternatives but never named tugstugi/dl-colab-notebooks. 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 tugstugi/dl-colab-notebooks?
    pass
    AI did not name tugstugi/dl-colab-notebooks — 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 tugstugi/dl-colab-notebooks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named tugstugi/dl-colab-notebooks 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 tugstugi/dl-colab-notebooks solve, and who is the primary audience?
    pass
    AI named tugstugi/dl-colab-notebooks 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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  • Brand-free category queries5 vs 2 in Lite
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