RRepoGEO

REPOGEO REPORT · LITE

DataExpert-io/ai-engineer-handbook

Default branch main · commit 6d86c39d · scanned 5/15/2026, 1:43:15 PM

GitHub: 1,115 stars · 178 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 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 DataExpert-io/ai-engineer-handbook, 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 relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-engineering, machine-learning, llm, prompt-engineering, mlops, handbook, resources, learning-path, career-development, data-science
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the root of the repository, containing the text of the MIT License.
  • highhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Add a relevant homepage URL (e.g., https://dataexpert.io/ai-engineer-handbook or https://dataexpert.io) to the repository's "About" section.

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 DataExpert-io/ai-engineer-handbook
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Coursera
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Coursera · recommended 1×
  2. DeepLearning.AI · recommended 1×
  3. fast.ai · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. edX · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources to start learning AI engineering fundamentals and advanced topics?
    you: not recommended
    AI recommended (in order):
    1. Coursera
    2. DeepLearning.AI
    3. fast.ai
    4. PyTorch (pytorch/pytorch)
    5. edX
    6. Google Developers
    7. TensorFlow (tensorflow/tensorflow)
    8. Keras (keras-team/keras)
    9. O'Reilly Books
    10. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
    11. Scikit-Learn (scikit-learn/scikit-learn)
    12. Designing Machine Learning Systems
    13. Kaggle Learn

    AI recommended 13 alternatives but never named DataExpert-io/ai-engineer-handbook. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best curated resources to stay current with AI engineering trends and career development?
    you: not recommended
    AI recommended (in order):
    1. Towards Data Science
    2. The Batch
    3. Hugging Face Blog
    4. Google AI Blog
    5. Meta AI Blog
    6. Microsoft AI Blog
    7. Kaggle
    8. MLOps Community
    9. Lex Fridman Podcast

    AI recommended 9 alternatives but never named DataExpert-io/ai-engineer-handbook. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 DataExpert-io/ai-engineer-handbook?
    pass
    AI did not name DataExpert-io/ai-engineer-handbook — 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 DataExpert-io/ai-engineer-handbook in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DataExpert-io/ai-engineer-handbook 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 DataExpert-io/ai-engineer-handbook solve, and who is the primary audience?
    pass
    AI did not name DataExpert-io/ai-engineer-handbook — 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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