RRepoGEO

REPOGEO REPORT · LITE

curiousily/AI-Bootcamp

Default branch master · commit 13353f77 · scanned 6/10/2026, 10:19:53 AM

GitHub: 905 stars · 289 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
33 /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
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 curiousily/AI-Bootcamp, 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 the README's opening paragraph to specify Generative AI focus

    Why:

    CURRENT
    The "Get Shit Done with AI" Bootcamp focuses on real-world applications that will equip you with the skills and knowledge to become a great AI engineer.
    COPY-PASTE FIX
    The "Get Shit Done with AI" Bootcamp is a self-paced, code-first curriculum focused on practical Generative AI applications. It equips you with the skills to build robust AI agents, RAG systems, and master modern LLM development.
  • mediumreadme#2
    Add a 'What You'll Master' section to the README

    Why:

    COPY-PASTE FIX
    ## What You'll Master
This bootcamp provides hands-on tutorials covering:
- **Generative AI Fundamentals:** LLMs, prompt engineering, fine-tuning.
- **Practical AI Systems:** Building RAGs (Retrieval Augmented Generation) and AI Agents (CrewAI, DSPy).
- **Modern AI Frameworks:** LangChain, LangGraph, Ollama.
- **Leading Models:** Working with ChatGPT, gpt-oss, Claude, Qwen, Gemma, Llama, Gemini.
  • lowreadme#3
    Add a 'Why This Bootcamp?' section to the README

    Why:

    COPY-PASTE FIX
    ## Why This Bootcamp?
This bootcamp offers a free, self-paced, and highly practical, code-first curriculum. It's designed to get you building real-world Generative AI applications using modern tools and frameworks, structured like a premium online academy, unlike generic ML courses or tool documentation.

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 curiousily/AI-Bootcamp
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Coursera: Andrew Ng's Machine Learning Specialization
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Coursera: Andrew Ng's Machine Learning Specialization · recommended 1×
  2. fast.ai: Practical Deep Learning for Coders · recommended 1×
  3. PyTorch · recommended 1×
  4. Google's Machine Learning Crash Course · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    Looking for a structured learning path to master generative AI for practical applications.
    you: not recommended
    AI recommended (in order):
    1. Coursera: Andrew Ng's Machine Learning Specialization
    2. fast.ai: Practical Deep Learning for Coders
    3. PyTorch
    4. Google's Machine Learning Crash Course
    5. TensorFlow
    6. Coursera: DeepLearning.AI Generative AI with Large Language Models (LLMs) Specialization
    7. Hugging Face
    8. Udemy: Generative Adversarial Networks (GANs) - Build your own GANs
    9. Hugging Face Transformers Library Documentation & Tutorials
    10. LangChain Documentation & Tutorials
    11. LlamaIndex Documentation & Tutorials
    12. Streamlit
    13. Gradio
    14. AWS SageMaker
    15. Google Cloud Vertex AI
    16. Azure Machine Learning
    17. arXiv.org
    18. Papers With Code
    19. Kaggle Competitions

    AI recommended 19 alternatives but never named curiousily/AI-Bootcamp. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to build robust AI agents and RAG systems with modern development practices?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/Haystack)
    4. OpenAI API
    5. Azure OpenAI Service
    6. Pinecone
    7. Weaviate
    8. Chroma
    9. Weights & Biases
    10. Docker
    11. Kubernetes

    AI recommended 11 alternatives but never named curiousily/AI-Bootcamp. 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 curiousily/AI-Bootcamp?
    pass
    AI did not name curiousily/AI-Bootcamp — 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 curiousily/AI-Bootcamp in production, what risks or prerequisites should they evaluate first?
    pass
    AI named curiousily/AI-Bootcamp 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 curiousily/AI-Bootcamp solve, and who is the primary audience?
    pass
    AI named curiousily/AI-Bootcamp explicitly

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

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curiousily/AI-Bootcamp — 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