RRepoGEO

REPOGEO REPORT · LITE

amitness/learning

Default branch master · commit e87fe0bb · scanned 6/29/2026, 8:58:52 AM

GitHub: 6,907 stars · 883 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
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 amitness/learning, 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
  • highabout#1
    Update the repository's 'About' description

    Why:

    CURRENT
    A log of things I'm learning
    COPY-PASTE FIX
    A curated collection of learning resources and structured study paths for generative AI, machine learning, and core software engineering. Includes roadmaps, recommended materials, and progress tracking.
  • highreadme#2
    Reposition README's opening paragraph to highlight its value as a curated resource

    Why:

    CURRENT
    A running log of things I'm learning to build strong core software engineering skills while also expanding my knowledge of adjacent technologies everyday.
    COPY-PASTE FIX
    A curated collection of learning resources and structured study paths for core software engineering, generative AI, and machine learning. This repository serves as a comprehensive roadmap, detailing essential skills, recommended materials, and progress tracking for advanced technical development.
  • mediumreadme#3
    Add a 'How to Use This Roadmap' section to the README

    Why:

    COPY-PASTE FIX
    ## How to Use This Roadmap

This repository is designed as a self-guided learning roadmap. Each section outlines key skills and provides a curated list of resources (books, courses, articles) to master them. Follow the 'Progress' column to track your journey or identify areas for further study.

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 amitness/learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Google's Machine Learning Crash Course (MLCC)
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Google's Machine Learning Crash Course (MLCC) · recommended 1×
  2. Google Cloud Skills Boost · recommended 1×
  3. Coursera · recommended 1×
  4. DeepLearning.AI · recommended 1×
  5. Machine Learning Specialization · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive learning roadmap for generative AI and machine learning engineering?
    you: not recommended
    AI recommended (in order):
    1. Google's Machine Learning Crash Course (MLCC)
    2. Google Cloud Skills Boost
    3. Coursera
    4. DeepLearning.AI
    5. Machine Learning Specialization
    6. Deep Learning Specialization
    7. Generative AI with Large Language Models
    8. Generative AI for Everyone
    9. OpenAI's Documentation and Cookbook
    10. GPT-3
    11. GPT-4
    12. DALL-E
    13. fast.ai's "Practical Deep Learning for Coders"
    14. fastai library
    15. PyTorch
    16. Stable Diffusion
    17. Hugging Face's " 🤗 Transformers Course"
    18. Hugging Face ecosystem
    19. Towards Data Science
    20. Medium
    21. Kaggle Learn

    AI recommended 21 alternatives but never named amitness/learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective resources for improving system design skills and foundational math for AI development?
    you: not recommended
    AI recommended (in order):
    1. Designing Data-Intensive Applications
    2. Grokking the System Design Interview
    3. Machine Learning System Design (Stanford CS 329S)
    4. Building Machine Learning Powered Applications
    5. AWS Well-Architected Framework (Machine Learning Lens)
    6. Mathematics for Machine Learning
    7. 3Blue1Brown
    8. Khan Academy
    9. Linear Algebra and Its Applications
    10. Probability and Statistics for Engineers and Scientists

    AI recommended 10 alternatives but never named amitness/learning. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 amitness/learning?
    pass
    AI named amitness/learning explicitly

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

  • If a team adopts amitness/learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named amitness/learning 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 amitness/learning solve, and who is the primary audience?
    pass
    AI named amitness/learning explicitly

    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 amitness/learning. 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)
[![RepoGEO](https://repogeo.com/badge/amitness/learning.svg)](https://repogeo.com/en/r/amitness/learning)
HTML
<a href="https://repogeo.com/en/r/amitness/learning"><img src="https://repogeo.com/badge/amitness/learning.svg" alt="RepoGEO" /></a>
Pro

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amitness/learning — 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