RRepoGEO

REPOGEO REPORT · LITE

google-gemma/cookbook

Default branch main · commit 10e976bc · scanned 5/17/2026, 11:42:39 PM

GitHub: 3,559 stars · 591 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
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 google-gemma/cookbook, 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
  • hightopics#1
    Add descriptive topics for guides, examples, and responsible AI

    Why:

    COPY-PASTE FIX
    codegemma, gemma, paligemma, recurrentgemma, llm-tutorials, generative-ai-examples, responsible-ai, ai-development-guides, gemma-applications
  • highreadme#2
    Reposition README's opening to highlight developer focus and responsible AI

    Why:

    CURRENT
    This is a collection of guides and examples for Google Gemma.
    COPY-PASTE FIX
    This is the official collection of practical guides and examples for developers and researchers building with Google's Gemma open models. Explore tutorials, full-stack application demos, and dedicated notebooks for responsible AI development.
  • mediumabout#3
    Refine repository description to be more action-oriented and include responsible AI

    Why:

    CURRENT
    A collection of guides and examples for the Gemma open models from Google.
    COPY-PASTE FIX
    Official guides and examples for developers building with Google's Gemma open models, including responsible AI applications.

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 google-gemma/cookbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. GPT-2 · recommended 1×
  3. Llama 2 · recommended 1×
  4. Stable Diffusion · recommended 1×
  5. Towards Data Science · recommended 1×
  • CATEGORY QUERY
    Where can I find practical guides and examples for building with open generative AI models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face (huggingface/transformers)
    2. transformers library (huggingface/transformers)
    3. GPT-2
    4. Llama 2
    5. Stable Diffusion
    6. Towards Data Science
    7. Medium
    8. GitHub
    9. Awesome Generative AI (filipecalegario/awesome-generative-ai)
    10. GANs
    11. Kaggle
    12. PyTorch (pytorch/pytorch)
    13. TensorFlow (tensorflow/tensorflow)
    14. YouTube
    15. AI Coffee Break with Letitia
    16. The AI Epiphany

    AI recommended 16 alternatives but never named google-gemma/cookbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tutorials for developing responsible AI applications using smaller, efficient generative models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. Google's Responsible AI Toolkit
    3. Model Card Toolkit
    4. Microsoft's Responsible AI Dashboard
    5. Fairlearn
    6. IBM AI Fairness 360 (AIF360)
    7. PyTorch Lightning
    8. Fast.ai's "Practical Deep Learning for Coders"

    AI recommended 8 alternatives but never named google-gemma/cookbook. 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 google-gemma/cookbook?
    pass
    AI named google-gemma/cookbook explicitly

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

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

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

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google-gemma/cookbook — 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