RRepoGEO

REPOGEO REPORT · LITE

amrzv/awesome-colab-notebooks

Default branch main · commit b488b970 · scanned 6/22/2026, 3:07:40 PM

GitHub: 1,639 stars · 277 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
12 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
0 pass · 1 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 amrzv/awesome-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
    Create a README.md with clear positioning

    Why:

    COPY-PASTE FIX
    # Awesome Colab Notebooks
    
    A curated collection of high-quality Google Colaboratory notebooks for fast and easy machine learning and deep learning experiments. This repository serves as a central hub for discovering runnable examples across various domains like CNNs, GANs, and general machine learning, helping practitioners and students quickly get started without complex setups.
    
    ## What is this collection?
    This repository is an 'awesome list' of Colab notebooks, carefully selected for their utility, clarity, and immediate applicability. It is not a library, framework, or the Google Colaboratory platform itself, but rather a guide to excellent resources available on Colab.
    
    ## How to Use
    Browse the categorized list of notebooks to find examples relevant to your interests. Each notebook link will take you directly to Google Colab where you can run it in your browser.
    
    ## Contributing
    We welcome contributions! Please see our contributing guidelines (link to be added) for how to submit new notebooks or improve existing entries.
    
    ## License
    This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
  • mediumabout#2
    Update the repository description

    Why:

    CURRENT
    Collection of google colaboratory notebooks for fast and easy experiments
    COPY-PASTE FIX
    A curated collection of awesome Google Colaboratory notebooks for fast and easy machine learning and deep learning experiments.
  • lowhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/amrzv/awesome-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 amrzv/awesome-colab-notebooks
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google Colaboratory (Colab)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google Colaboratory (Colab) · recommended 1×
  2. keras-team/keras · recommended 1×
  3. Lightning-AI/lightning · recommended 1×
  4. fastai/fastai · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    Where can I find readily available deep learning examples for cloud-based interactive environments?
    you: not recommended
    AI recommended (in order):
    1. Google Colaboratory (Colab)

    AI recommended 1 alternative but never named amrzv/awesome-colab-notebooks. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need quick setup code for machine learning models, including generative and convolutional networks.
    you: not recommended
    AI recommended (in order):
    1. Keras (keras-team/keras)
    2. PyTorch Lightning (Lightning-AI/lightning)
    3. Fast.ai (fastai/fastai)
    4. TensorFlow (tensorflow/tensorflow)
    5. Hugging Face Transformers (huggingface/transformers)
    6. scikit-learn (scikit-learn/scikit-learn)

    AI recommended 6 alternatives but never named amrzv/awesome-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
    fail

    Suggestion:

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 amrzv/awesome-colab-notebooks?
    pass
    AI did not name amrzv/awesome-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 amrzv/awesome-colab-notebooks in production, what risks or prerequisites should they evaluate first?
    pass
    AI named amrzv/awesome-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 amrzv/awesome-colab-notebooks solve, and who is the primary audience?
    pass
    AI did not name amrzv/awesome-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?

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amrzv/awesome-colab-notebooks — 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