RRepoGEO

REPOGEO REPORT · LITE

nivu/ai_all_resources

Default branch master · commit 1dddc60a · scanned 5/21/2026, 7:45:38 PM

GitHub: 1,477 stars · 309 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
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 nivu/ai_all_resources, 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
    Clarify repo's nature as a curated list in the README's opening

    Why:

    CURRENT
    # An Ultimate Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning

## _Knowledge Not Shared is wasted. - Clan Jacobs_

### This collection is a compilation of Excellent ML and DL Tutorials created by the people below
    COPY-PASTE FIX
    # An Ultimate, Curated Compilation of AI Resources for Mathematics, Machine Learning and Deep Learning

This repository serves as a comprehensive, curated compilation of high-quality AI resources, including excellent tutorials, blogs, and communities. It is designed for learners and practitioners seeking to deepen their understanding of Artificial Intelligence, Machine Learning, and Deep Learning.

## _Knowledge Not Shared is wasted. - Clan Jacobs_

### This collection is a compilation of Excellent ML and DL Tutorials created by the people below
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the root directory with the text of the MIT License.
  • mediumhomepage#3
    Populate the Homepage field in repository settings

    Why:

    COPY-PASTE FIX
    Set the repository's homepage URL to: `https://github.com/nivu/ai_all_resources#readme`

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 nivu/ai_all_resources
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Coursera
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Coursera · recommended 2×
  2. fast.ai · recommended 1×
  3. fastai/fastai · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. edX · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of resources to learn machine learning and deep learning?
    you: not recommended
    AI recommended (in order):
    1. Coursera
    2. fast.ai
    3. fastai library (fastai/fastai)
    4. PyTorch (pytorch/pytorch)
    5. edX
    6. Google Developers
    7. TensorFlow (tensorflow/tensorflow)
    8. Kaggle
    9. Pandas (pandas-dev/pandas)
    10. scikit-learn (scikit-learn/scikit-learn)
    11. DeepLearning.AI
    12. Keras (keras-team/keras)

    AI recommended 12 alternatives but never named nivu/ai_all_resources. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best beginner-friendly tutorials and guides for getting started with AI and Python?
    you: not recommended
    AI recommended (in order):
    1. Google's Machine Learning Crash Course (MLCC)
    2. TensorFlow
    3. freeCodeCamp
    4. Scikit-learn
    5. Pandas
    6. IBM's "Applied AI with Python" (Coursera Specialization)
    7. Coursera
    8. Kaggle Learn
    9. DataCamp
    10. NumPy
    11. Sentdex

    AI recommended 11 alternatives but never named nivu/ai_all_resources. 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 nivu/ai_all_resources?
    pass
    AI named nivu/ai_all_resources explicitly

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

  • If a team adopts nivu/ai_all_resources in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nivu/ai_all_resources 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 nivu/ai_all_resources solve, and who is the primary audience?
    pass
    AI did not name nivu/ai_all_resources — 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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nivu/ai_all_resources — 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