RRepoGEO

REPOGEO REPORT · LITE

ashishps1/learn-ai-engineering

Default branch main · commit 3e7ccb4e · scanned 5/17/2026, 9:08:13 PM

GitHub: 5,536 stars · 1,372 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
35 /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
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 ashishps1/learn-ai-engineering, 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 to emphasize 'structured learning roadmap'

    Why:

    CURRENT
    # Learn AI Engineering

A comprehensive collection of free resources to learn everything about AI/ML, LLMs and Agents.
    COPY-PASTE FIX
    # Learn AI Engineering

A curated and structured learning roadmap, providing a comprehensive collection of free resources to master AI/ML, LLMs, and Agents from scratch.
  • mediumtopics#2
    Add topics that describe the repository's function as a learning resource

    Why:

    CURRENT
    agentic-ai, agents, ai, deep-learning, generative-ai, large-language-models, llm, machine-learning, mcp, ml, prompt-engineering, rag
    COPY-PASTE FIX
    agentic-ai, agents, ai, deep-learning, generative-ai, large-language-models, llm, machine-learning, mcp, ml, prompt-engineering, rag, learning-path, education, roadmap, curated-resources, guide
  • lowabout#3
    Add a homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://[your-project-homepage-url-here]

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 ashishps1/learn-ai-engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. tensorflow/tensorflow · recommended 2×
  3. pandas-dev/pandas · recommended 2×
  4. fast.ai · recommended 1×
  5. Hugging Face · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive free resources to learn AI, ML, and large language models?
    you: not recommended
    AI recommended (in order):
    1. fast.ai
    2. Hugging Face
    3. Transformers library (huggingface/transformers)
    4. Google's Machine Learning Crash Course
    5. TensorFlow (tensorflow/tensorflow)
    6. Coursera
    7. deeplearning.ai
    8. Stanford University
    9. Kaggle Learn
    10. Python
    11. Pandas (pandas-dev/pandas)
    12. freeCodeCamp.org
    13. OpenAI Cookbook
    14. GPT-3.5
    15. GPT-4

    AI recommended 15 alternatives but never named ashishps1/learn-ai-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good structured learning path for AI engineering, including LLMs and agents?
    you: not recommended
    AI recommended (in order):
    1. NumPy (numpy/numpy)
    2. Pandas (pandas-dev/pandas)
    3. Scikit-learn (scikit-learn/scikit-learn)
    4. TensorFlow (tensorflow/tensorflow)
    5. PyTorch (pytorch/pytorch)
    6. NVIDIA CUDA
    7. Google Colab
    8. NLTK (nltk/nltk)
    9. SpaCy (explosion/spaCy)
    10. Hugging Face Transformers library (huggingface/transformers)
    11. Hugging Face PEFT library (huggingface/peft)
    12. OpenAI API
    13. Anthropic Claude API
    14. Google Gemini API
    15. Hugging Face Evaluate library (huggingface/evaluate)
    16. LangChain (langchain-ai/langchain)
    17. LlamaIndex (run-llama/llama_index)
    18. Docker (moby/moby)
    19. AWS
    20. Google Cloud
    21. Azure
    22. FastAPI (tiangolo/fastapi)
    23. Flask (pallets/flask)
    24. Prometheus (prometheus/prometheus)
    25. Grafana (grafana/grafana)
    26. Weights & Biases (W&B)
    27. Pinecone
    28. Weaviate (weaviate/weaviate)
    29. ChromaDB (chroma-core/chroma)
    30. Milvus (milvus-io/milvus)

    AI recommended 30 alternatives but never named ashishps1/learn-ai-engineering. 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 ashishps1/learn-ai-engineering?
    pass
    AI named ashishps1/learn-ai-engineering explicitly

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

  • If a team adopts ashishps1/learn-ai-engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ashishps1/learn-ai-engineering 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 ashishps1/learn-ai-engineering solve, and who is the primary audience?
    pass
    AI named ashishps1/learn-ai-engineering 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 ashishps1/learn-ai-engineering. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/ashishps1/learn-ai-engineering.svg)](https://repogeo.com/en/r/ashishps1/learn-ai-engineering)
HTML
<a href="https://repogeo.com/en/r/ashishps1/learn-ai-engineering"><img src="https://repogeo.com/badge/ashishps1/learn-ai-engineering.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

ashishps1/learn-ai-engineering — 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
ashishps1/learn-ai-engineering — RepoGEO report