RRepoGEO

REPOGEO REPORT · LITE

MLNLP-World/LLMs-from-scratch-CN

Default branch main · commit f8cef3d2 · scanned 6/21/2026, 7:37:37 AM

GitHub: 2,689 stars · 444 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 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

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

OVERALL DIRECTION
  • highreadme#1
    Clarify the existing license in the README

    Why:

    COPY-PASTE FIX
    Add a section or line in the README, e.g., '本项目遵循 [Original Project License Name/Type] 许可协议。具体细节请参阅 LICENSE 文件。' (This project follows the [Original Project License Name/Type] license. Please refer to the LICENSE file for details.)
  • mediumhomepage#2
    Add the original project's URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://github.com/rasbt/LLMs-from-scratch.git

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
PyTorch
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 1×
  2. Hugging Face Transformers · recommended 1×
  3. NumPy · recommended 1×
  4. scikit-learn · recommended 1×
  5. Weights & Biases (W&B) · recommended 1×
  • CATEGORY QUERY
    How to build large language models from the ground up with practical code examples?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. Hugging Face Transformers
    3. NumPy
    4. scikit-learn
    5. Weights & Biases (W&B)
    6. TensorBoard
    7. CUDA
    8. ROCm
    9. Docker

    AI recommended 9 alternatives but never named MLNLP-World/LLMs-from-scratch-CN. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive guide to understanding LLM architecture in Chinese?
    you: not recommended
    AI recommended (in order):
    1. 动手学深度学习 (Dive into Deep Learning)
    2. The Illustrated Transformer (Jay Alammar's blog posts)
    3. Hugging Face Transformers (huggingface/transformers)
    4. Zhihu (知乎)
    5. CSDN (中国软件开发网)
    6. Attention Is All You Need

    AI recommended 6 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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MLNLP-World/LLMs-from-scratch-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