RRepoGEO

REPOGEO REPORT · LITE

skindhu/Build-A-Large-Language-Model-CN

Default branch main · commit 592cc35a · scanned 6/30/2026, 2:43:28 AM

GitHub: 3,785 stars · 635 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 skindhu/Build-A-Large-Language-Model-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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    large-language-models, llm, gpt, deep-learning, machine-learning, nlp, chinese, tutorial, e-book, from-scratch
  • highreadme#2
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    # Build a Large Language Model (From Scratch) 中文版

随着大语言模型（LLM）技术的飞速发展，越来越多的应用开始渗透到我们的工作和日常生活中。
    COPY-PASTE FIX
    # Build a Large Language Model (From Scratch) 中文版

本项目是《Build a Large Language Model (From Scratch)》的中文翻译版，一本深入探讨大语言模型原理与实现的开源电子书。随着大语言模型（LLM）技术的飞速发展，越来越多的应用开始渗透到我们的工作和日常生活中。
  • mediumreadme#3
    Clarify the repository's license in the README

    Why:

    COPY-PASTE FIX
    ## 许可证

本项目采用自定义许可证。请查阅 `LICENSE` 文件以获取完整的许可条款和条件。

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 skindhu/Build-A-Large-Language-Model-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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 1×
  2. Stanford CS324 · recommended 1×
  3. DeepLearning.AI's Generative AI with Transformers Specialization · recommended 1×
  4. pytorch/pytorch · recommended 1×
  5. tensorflow/tensorflow · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive guide to building large language models from scratch?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library (huggingface/transformers)
    2. Stanford CS324
    3. DeepLearning.AI's Generative AI with Transformers Specialization
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)

    AI recommended 5 alternatives but never named skindhu/Build-A-Large-Language-Model-CN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical tutorials to implement large language models and understand GPT architecture in Chinese.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. TensorFlow
    4. "动手学深度学习" (Dive into Deep Learning)
    5. Bilibili (哔哩哔哩)
    6. Zhihu (知乎)
    7. CSDN (中国软件开发网)
    8. OpenAI's GPT-3/GPT-4 API Documentation

    AI recommended 8 alternatives but never named skindhu/Build-A-Large-Language-Model-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 skindhu/Build-A-Large-Language-Model-CN?
    pass
    AI did not name skindhu/Build-A-Large-Language-Model-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 skindhu/Build-A-Large-Language-Model-CN in production, what risks or prerequisites should they evaluate first?
    pass
    AI named skindhu/Build-A-Large-Language-Model-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 skindhu/Build-A-Large-Language-Model-CN solve, and who is the primary audience?
    pass
    AI did not name skindhu/Build-A-Large-Language-Model-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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skindhu/Build-A-Large-Language-Model-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