RRepoGEO

REPOGEO REPORT · LITE

MLEveryday/practicalAI-cn

Default branch master · commit bbc707fb · scanned 5/29/2026, 12:43:41 PM

GitHub: 6,865 stars · 1,423 forks

AI VISIBILITY SCORE
22 /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
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 MLEveryday/practicalAI-cn, 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 the README's opening to clearly state it's a Chinese-language tutorial collection

    Why:

    CURRENT
    # AI实战-practicalAI 中文版
    COPY-PASTE FIX
    # AI实战-practicalAI 中文版：面向中文学习者的PyTorch机器学习与深度学习实践教程集
  • mediumtopics#2
    Add topics that clarify the repository's educational nature and target audience

    Why:

    CURRENT
    deep-learning, google-colab-notebook, jupyter-notebook, machine-learning, pytorch
    COPY-PASTE FIX
    deep-learning, google-colab-notebook, jupyter-notebook, machine-learning, pytorch, ai-tutorials, ml-education, chinese-language, practical-ai
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://nbviewer.jupyter.org/github/MLEveryday/practicalAI-cn/tree/master/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 MLEveryday/practicalAI-cn
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. Google Colaboratory (Colab) · recommended 1×
  3. Kaggle Kernels (now Kaggle Notebooks) · recommended 1×
  4. DeepLearning.AI Courses on Coursera/DeepLearning.AI Platform · recommended 1×
  5. TensorFlow.js · recommended 1×
  • CATEGORY QUERY
    Seeking practical machine learning and deep learning tutorials runnable directly in a browser.
    you: not recommended
    AI recommended (in order):
    1. Google Colaboratory (Colab)
    2. Kaggle Kernels (now Kaggle Notebooks)
    3. DeepLearning.AI Courses on Coursera/DeepLearning.AI Platform
    4. TensorFlow.js
    5. PyTorch
    6. fast.ai
    7. Hugging Face Spaces

    AI recommended 7 alternatives but never named MLEveryday/practicalAI-cn. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to learn building production-ready object-oriented machine learning applications from scratch?
    you: not recommended
    AI recommended (in order):
    1. Scikit-learn
    2. TensorFlow
    3. PyTorch
    4. FastAPI
    5. Docker
    6. MLflow
    7. Kubernetes

    AI recommended 7 alternatives but never named MLEveryday/practicalAI-cn. 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 MLEveryday/practicalAI-cn?
    pass
    AI did not name MLEveryday/practicalAI-cn — 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 MLEveryday/practicalAI-cn in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MLEveryday/practicalAI-cn 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 MLEveryday/practicalAI-cn solve, and who is the primary audience?
    pass
    AI did not name MLEveryday/practicalAI-cn — 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?

Embed your GEO score

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