RRepoGEO

REPOGEO REPORT · LITE

Hoper-J/AI-Guide-and-Demos-zh_CN

Default branch master · commit 3e0b7d0d · scanned 5/20/2026, 3:58:23 PM

GitHub: 4,081 stars · 436 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 Hoper-J/AI-Guide-and-Demos-zh_CN, 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
  • highreadme#1
    Reposition the README H1 to specify category and unique value

    Why:

    CURRENT
    # 这是一个中文的 AI/LLM 大模型入门项目
    COPY-PASTE FIX
    # Hoper-J/AI-Guide-and-Demos-zh_CN: 李宏毅2024生成式AI导论中文镜像与LLM入门实战指南 (支持无GPU学习)
  • mediumreadme#2
    Add a concise introductory paragraph to the README

    Why:

    COPY-PASTE FIX
    本项目提供一份从API调用到本地大模型部署与微调的逐步指南，特别为没有强大显卡的学习者设计，通过Kaggle/Colab在线环境即可实践。同时，它也是李宏毅 (HUNG-YI LEE）2024生成式人工智能导论课程的完整中文镜像作业。

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 Hoper-J/AI-Guide-and-Demos-zh_CN
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. Google Colaboratory (Colab) Pro/Pro+ · recommended 1×
  3. pytorch/pytorch · recommended 1×
  4. tensorflow/tensorflow · recommended 1×
  5. Hugging Face Spaces · recommended 1×
  • CATEGORY QUERY
    How to learn large language model deployment and fine-tuning without a powerful GPU?
    you: not recommended
    AI recommended (in order):
    1. Google Colaboratory (Colab) Pro/Pro+
    2. Hugging Face Transformers (huggingface/transformers)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow (tensorflow/tensorflow)
    5. Hugging Face Spaces
    6. Gradio (gradio-app/gradio)
    7. Google Cloud Platform (GCP)
    8. Vertex AI Workbench
    9. Compute Engine
    10. Amazon Web Services (AWS)
    11. SageMaker Studio Lab
    12. EC2 Instances
    13. Microsoft Azure
    14. Azure Machine Learning
    15. Azure Virtual Machines
    16. RunPod.io
    17. Vast.ai
    18. bitsandbytes (TimDettmers/bitsandbytes)
    19. PEFT (huggingface/peft)

    AI recommended 19 alternatives but never named Hoper-J/AI-Guide-and-Demos-zh_CN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a beginner-friendly guide for LLM development, from API calls to local model fine-tuning.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face's Transformers Course
    2. Transformers library (huggingface/transformers)
    3. OpenAI API Documentation and Tutorials
    4. OpenAI API
    5. OpenAI Python library (openai/openai-python)
    6. LangChain Documentation and "Getting Started" Guides
    7. LangChain (langchain-ai/langchain)
    8. Google's Generative AI Learning Path
    9. Fast.ai's "Practical Deep Learning for Coders"

    AI recommended 9 alternatives but never named Hoper-J/AI-Guide-and-Demos-zh_CN. 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 Hoper-J/AI-Guide-and-Demos-zh_CN?
    pass
    AI did not name Hoper-J/AI-Guide-and-Demos-zh_CN — 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 Hoper-J/AI-Guide-and-Demos-zh_CN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Hoper-J/AI-Guide-and-Demos-zh_CN 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 Hoper-J/AI-Guide-and-Demos-zh_CN solve, and who is the primary audience?
    pass
    AI did not name Hoper-J/AI-Guide-and-Demos-zh_CN — 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

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Hoper-J/AI-Guide-and-Demos-zh_CN — 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