RRepoGEO

REPOGEO REPORT · LITE

MLNLP-World/LLMs-from-scratch-CN

Default branch main · commit f8cef3d2 · scanned 5/11/2026, 4:37:59 AM

GitHub: 2,607 stars · 432 forks

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 MLNLP-World/LLMs-from-scratch-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

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

OVERALL DIRECTION
  • hightopics#1
    Add specific topics for better categorization

    Why:

    COPY-PASTE FIX
    llm, large-language-models, from-scratch, tutorial, chinese, deep-learning, machine-learning, nlp, jupyter-notebook, education
  • highreadme#2
    Add a concise introductory paragraph to the README

    Why:

    CURRENT
    (The first content after badges/nav is the '项目动机' heading and its paragraph)
    COPY-PASTE FIX
    本项目是《LLMs-from-scratch》的中文翻译版本，提供从零开始构建大型语言模型的详细教程、Markdown笔记和带中文注释的Jupyter代码，专为中文学习者设计。
  • mediumlicense#3
    Clarify the existing license in the README

    Why:

    CURRENT
    (No explicit license statement in the README excerpt)
    COPY-PASTE FIX
    本项目遵循原项目《LLMs-from-scratch》的许可协议。请查阅 [LICENSE](LICENSE) 文件获取详细信息。

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 MLNLP-World/LLMs-from-scratch-CN
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Bilibili
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Bilibili · recommended 2×
  2. Coursera · recommended 1×
  3. YouTube · recommended 1×
  4. Hugging Face · recommended 1×
  5. Transformers · recommended 1×
  • CATEGORY QUERY
    I need resources to understand large language models from scratch, preferably in Chinese.
    you: not recommended
    AI recommended (in order):
    1. Coursera
    2. YouTube
    3. Bilibili
    4. Hugging Face
    5. Transformers
    6. PyTorch
    7. TensorFlow
    8. MXNet
    9. Bilibili
    10. Zhihu

    AI recommended 10 alternatives but never named MLNLP-World/LLMs-from-scratch-CN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for practical tutorials and commented code to build custom large language models.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. PyTorch (pytorch/pytorch)
    3. PyTorch Lightning (Lightning-AI/lightning)
    4. TensorFlow (tensorflow/tensorflow)
    5. Keras (keras-team/keras)
    6. OpenAI API
    7. Fast.ai
    8. Stanford CS224N

    AI recommended 8 alternatives but never named MLNLP-World/LLMs-from-scratch-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 MLNLP-World/LLMs-from-scratch-CN?
    pass
    AI did not name MLNLP-World/LLMs-from-scratch-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 MLNLP-World/LLMs-from-scratch-CN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named MLNLP-World/LLMs-from-scratch-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 MLNLP-World/LLMs-from-scratch-CN solve, and who is the primary audience?
    pass
    AI did not name MLNLP-World/LLMs-from-scratch-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?

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