RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/tiny-universe

Default branch main · commit a5ae08d5 · scanned 6/29/2026, 4:53:09 AM

GitHub: 4,932 stars · 470 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
35 /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
3 / 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 datawhalechina/tiny-universe, 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
  • highreadme#1
    Add a concise English positioning statement to the README

    Why:

    COPY-PASTE FIX
    Add the following sentence immediately after the main H1 title in the README: "This repository is a comprehensive, hands-on guide to building large language models (LLMs) and their ecosystems (RAG, Agent, Eval) from first principles, designed for deep learning practitioners."
  • hightopics#2
    Add topics reflecting the project's educational and 'from scratch' nature

    Why:

    CURRENT
    agent, diffusion, evaluation-metrics, llama, qwen, rag, transformers
    COPY-PASTE FIX
    agent, diffusion, evaluation-metrics, llama, qwen, rag, transformers, llm-from-scratch, educational-resource, deep-learning-guide, white-box-llm, llm-architecture
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the text of the MIT License.

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 datawhalechina/tiny-universe
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. PyTorch · recommended 1×
  3. TensorFlow · recommended 1×
  4. NumPy · recommended 1×
  5. Matplotlib · recommended 1×
  • CATEGORY QUERY
    How to learn large language model architecture and components by building them from scratch?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. NumPy
    4. Hugging Face Transformers
    5. Matplotlib
    6. Seaborn
    7. Jupyter Notebooks
    8. VS Code

    AI recommended 8 alternatives but never named datawhalechina/tiny-universe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking resources to implement a RAG framework or AI agent system from first principles.
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. Hugging Face Datasets
    5. Faiss
    6. Elasticsearch
    7. OpenSearch
    8. Sentence-Transformers
    9. NLTK
    10. spaCy

    AI recommended 10 alternatives but never named datawhalechina/tiny-universe. 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 datawhalechina/tiny-universe?
    pass
    AI named datawhalechina/tiny-universe explicitly

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

  • If a team adopts datawhalechina/tiny-universe in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datawhalechina/tiny-universe 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 datawhalechina/tiny-universe solve, and who is the primary audience?
    pass
    AI named datawhalechina/tiny-universe explicitly

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

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datawhalechina/tiny-universe — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite