RRepoGEO

REPOGEO REPORT · LITE

hemansnation/AI-Engineer-Headquarters

Default branch master · commit fd5c64d5 · scanned 6/21/2026, 6:08:09 PM

GitHub: 3,666 stars · 702 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 hemansnation/AI-Engineer-Headquarters, 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 README's opening paragraph to clarify its purpose as an advanced AI engineering guide.

    Why:

    CURRENT
    A drill of scientific methods, processes, algorithms, and systems to build stories & models. An in-depth learning resource for humans. This is a drill for people who aim to be in the top 1% of Data and AI experts.
    COPY-PASTE FIX
    This repository is the **AI Engineer Headquarters**, a meticulously curated and advanced learning path and resource collection for aspiring and current AI professionals aiming to be in the top 1% of Data and AI experts. Unlike generic courses or tools, it provides a structured 'drill' of scientific methods, processes, algorithms, and systems essential for building robust AI models and data-driven solutions.
  • highlicense#2
    Add a LICENSE file to the repository.

    Why:

    COPY-PASTE FIX
    (Choose and add a standard open-source license file, e.g., MIT, Apache-2.0, or GPL-3.0, to the repository root.)
  • mediumabout#3
    Update the repository's About description for clarity.

    Why:

    CURRENT
    A collection of scientific methods, processes, algorithms, and systems to build stories & models.
    COPY-PASTE FIX
    A curated, advanced learning path and resource collection for aspiring and current AI engineers, focusing on scientific methods, processes, algorithms, and systems to build robust AI models and data-driven solutions.

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 hemansnation/AI-Engineer-Headquarters
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle · recommended 2×
  2. MIT OpenCourseware - Mathematics for Computer Science (6.042J) · recommended 1×
  3. Khan Academy · recommended 1×
  4. Python for Everybody (University of Michigan on Coursera) · recommended 1×
  5. Automate the Boring Stuff with Python (Al Sweigart) · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive learning path for becoming a top AI engineer?
    you: not recommended
    AI recommended (in order):
    1. MIT OpenCourseware - Mathematics for Computer Science (6.042J)
    2. Khan Academy
    3. Python for Everybody (University of Michigan on Coursera)
    4. Automate the Boring Stuff with Python (Al Sweigart)
    5. HackerRank
    6. LeetCode
    7. Andrew Ng's Machine Learning (Coursera)
    8. DeepLearning.AI TensorFlow in Practice Specialization (Coursera)
    9. TensorFlow
    10. Scikit-Learn
    11. Keras
    12. Deep Learning Specialization (Andrew Ng on Coursera)
    13. fast.ai Practical Deep Learning for Coders
    14. Stanford CS224n: Natural Language Processing with Deep Learning
    15. Stanford CS231n: Convolutional Neural Networks for Visual Recognition
    16. "Speech and Language Processing" (Daniel Jurafsky and James H. Martin)
    17. Reinforcement Learning Specialization (University of Alberta on Coursera)
    18. "Reinforcement Learning: An Introduction" (Richard S. Sutton and Andrew G. Barto)
    19. Kaggle
    20. GitHub
    21. Hugging Face's Transformers library
    22. Kubeflow
    23. MLflow
    24. AWS SageMaker
    25. Google Cloud AI Platform

    AI recommended 25 alternatives but never named hemansnation/AI-Engineer-Headquarters. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best practical resources for mastering data science and machine learning concepts?
    you: not recommended
    AI recommended (in order):
    1. Coursera
    2. DeepLearning.AI
    3. IBM
    4. Google
    5. Kaggle
    6. Kaggle Learn
    7. Pandas (pandas-dev/pandas)
    8. Scikit-learn (scikit-learn/scikit-learn)
    9. fast.ai (fastai/fastai)
    10. Keras (keras-team/keras)
    11. TensorFlow (tensorflow/tensorflow)
    12. Udemy
    13. ChatGPT
    14. Towards Data Science
    15. StatQuest with Josh Starmer
    16. freeCodeCamp.org (freeCodeCamp/freeCodeCamp)

    AI recommended 16 alternatives but never named hemansnation/AI-Engineer-Headquarters. 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 hemansnation/AI-Engineer-Headquarters?
    pass
    AI did not name hemansnation/AI-Engineer-Headquarters — 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 hemansnation/AI-Engineer-Headquarters in production, what risks or prerequisites should they evaluate first?
    pass
    AI named hemansnation/AI-Engineer-Headquarters 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 hemansnation/AI-Engineer-Headquarters solve, and who is the primary audience?
    pass
    AI did not name hemansnation/AI-Engineer-Headquarters — 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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hemansnation/AI-Engineer-Headquarters — 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