RRepoGEO

REPOGEO REPORT · LITE

orico/www.mlcompendium.com

Default branch main · commit 4c787070 · scanned 6/29/2026, 5:48:02 PM

GitHub: 2,190 stars · 236 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
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 orico/www.mlcompendium.com, 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 core value proposition to the top of README

    Why:

    CURRENT
    The README currently begins with Gitbook layout configuration before the main descriptive text.
    COPY-PASTE FIX
    Move the paragraph starting 'Covering approximately **500 topics**, the ML & DL Compendium includes summaries, links, and articles...' to be the very first human-readable content in the README, immediately after the H1, and before any Gitbook configuration blocks or embeds.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. Given the project's nature as a knowledge compendium, consider a permissive license like Creative Commons Attribution 4.0 International (CC-BY-4.0) for the content.
  • mediumtopics#3
    Refine repository topics for clarity

    Why:

    CURRENT
    algorithms, data-science, deep-learning, full-stack, gitbook, machine-learning, marketing, mlcompendium, probability, product-management, statistics, ux-design, ux-experience, ux-research
    COPY-PASTE FIX
    machine-learning, deep-learning, data-science, knowledge-base, compendium, educational-resource, learning-path, ml-concepts, ai-strategy, gitbook, mlcompendium

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 orico/www.mlcompendium.com
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepLearning.AI
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepLearning.AI · recommended 2×
  2. scikit-learn/scikit-learn · recommended 1×
  3. tensorflow/tensorflow · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. fastai/fastai · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive knowledge base covering machine learning and deep learning topics?
    you: not recommended
    AI recommended (in order):
    1. scikit-learn (scikit-learn/scikit-learn)
    2. TensorFlow (tensorflow/tensorflow)
    3. PyTorch (pytorch/pytorch)
    4. fast.ai (fastai/fastai)
    5. DeepLearning.AI
    6. Machine Learning Mastery
    7. Wikipedia

    AI recommended 7 alternatives but never named orico/www.mlcompendium.com. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources offer insights into data science management, product strategy, and AI development?
    you: not recommended
    AI recommended (in order):
    1. O'Reilly Media
    2. Harvard Business Review
    3. DeepLearning.AI
    4. McKinsey & Company
    5. Boston Consulting Group
    6. Gartner
    7. Towards Data Science
    8. Competing in the Age of AI
    9. Google

    AI recommended 9 alternatives but never named orico/www.mlcompendium.com. 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 orico/www.mlcompendium.com?
    pass
    AI did not name orico/www.mlcompendium.com — 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 orico/www.mlcompendium.com in production, what risks or prerequisites should they evaluate first?
    pass
    AI named orico/www.mlcompendium.com 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 orico/www.mlcompendium.com solve, and who is the primary audience?
    pass
    AI did not name orico/www.mlcompendium.com — 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

Drop this badge into the README of orico/www.mlcompendium.com. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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orico/www.mlcompendium.com — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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orico/www.mlcompendium.com — RepoGEO report