RRepoGEO

REPOGEO REPORT · LITE

loveunk/deep-learning-llm-agent-notes

Default branch master · commit 499cc7d4 · scanned 6/20/2026, 4:53:01 PM

GitHub: 2,547 stars · 395 forks

AI VISIBILITY SCORE
28 /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
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 loveunk/deep-learning-llm-agent-notes, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highlicense#1
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license, such as MIT or Apache-2.0, to clearly state usage terms.
  • mediumabout#2
    Enhance the 'About' description for clarity

    Why:

    CURRENT
    机器学习、深度学习的学习路径及知识总结
    COPY-PASTE FIX
    面向中文学习者的 AI 学习路线图：从机器学习、深度学习基础，到大语言模型、RAG、Agent 和多模态工程实践。提供结构化学习路径和知识总结，助你从概念走向可运行的 AI 应用。

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 loveunk/deep-learning-llm-agent-notes
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. Python · recommended 1×
  3. NumPy · recommended 1×
  4. Pandas · recommended 1×
  5. JupyterLab · recommended 1×
  • CATEGORY QUERY
    What is a good learning roadmap for transitioning into large language model engineering?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. NumPy
    3. Pandas
    4. JupyterLab
    5. Google Colaboratory
    6. scikit-learn
    7. NLTK
    8. spaCy
    9. PyTorch
    10. TensorFlow
    11. Hugging Face Transformers (huggingface/transformers)
    12. GPT-3/GPT-4
    13. LLaMA/LLaMA 2
    14. BERT
    15. Mistral
    16. Hugging Face Datasets (huggingface/datasets)
    17. Hugging Face Accelerate (huggingface/accelerate)
    18. Hugging Face PEFT (huggingface/peft)
    19. OpenAI API
    20. Anthropic API
    21. bitsandbytes (TimDettmers/bitsandbytes)
    22. Hugging Face Evaluate (huggingface/evaluate)
    23. LangChain (langchain-ai/langchain)
    24. LlamaIndex (run-llama/llama_index)
    25. Pinecone
    26. Weaviate (weaviate/weaviate)
    27. Chroma (chroma-core/chroma)
    28. FAISS (facebookresearch/faiss)
    29. Gradio (gradio-app/gradio)
    30. Streamlit (streamlit/streamlit)
    31. FastAPI (tiangolo/fastapi)
    32. AWS SageMaker
    33. Google Cloud Vertex AI
    34. Azure Machine Learning
    35. Weights & Biases (wandb/wandb)
    36. MLflow (mlflow/mlflow)
    37. AutoGPT (Significant-Gravitas/AutoGPT)
    38. BabyAGI (yoheinakajima/babyagi)
    39. GPT-4V
    40. DALL-E 3
    41. OpenVINO (openvinotoolkit/openvino)
    42. ONNX Runtime (microsoft/onnxruntime)

    AI recommended 42 alternatives but never named loveunk/deep-learning-llm-agent-notes. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I quickly get started with building AI agents and multimodal applications practically?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. Hugging Face Diffusers
    5. OpenAI API
    6. Google AI Studio
    7. Gemini API
    8. Microsoft Semantic Kernel

    AI recommended 8 alternatives but never named loveunk/deep-learning-llm-agent-notes. 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 loveunk/deep-learning-llm-agent-notes?
    pass
    AI named loveunk/deep-learning-llm-agent-notes explicitly

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

  • If a team adopts loveunk/deep-learning-llm-agent-notes in production, what risks or prerequisites should they evaluate first?
    pass
    AI named loveunk/deep-learning-llm-agent-notes 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 loveunk/deep-learning-llm-agent-notes solve, and who is the primary audience?
    pass
    AI did not name loveunk/deep-learning-llm-agent-notes — 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 loveunk/deep-learning-llm-agent-notes. 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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loveunk/deep-learning-llm-agent-notes — 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