RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/tiny-universe

Default branch main · commit a5ae08d5 · scanned 5/17/2026, 11:23:07 PM

GitHub: 4,847 stars · 463 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
    Reposition the README's opening sentence to emphasize 'from-scratch educational guide'

    Why:

    CURRENT
    本项目是一个从原理出发、以“白盒”为导向、围绕大模型全链路的“手搓”大模型指南，旨在帮助有传统深度学习基础的读者从底层原理出发，“纯手搓”搭建一个清晰、可用的大模型系统...
    COPY-PASTE FIX
    本项目是一个**从原理出发、以“白盒”为导向、围绕大模型全链路的“手搓”大模型教育指南**，旨在帮助有传统深度学习基础的读者从底层原理出发，“纯手搓”搭建一个清晰、可用的大模型系统...
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. (e.g., choose a common open-source license like MIT, Apache-2.0, or GPL-3.0 and paste its text into the file.)
  • mediumtopics#3
    Add more specific topics to highlight 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-tutorial, machine-learning-education

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
ray-project/ray
Recommended in 4 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 4×
  2. pytorch/pytorch · recommended 3×
  3. tensorflow/tensorflow · recommended 2×
  4. dask/dask · recommended 2×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    How can I build large language model components like RAG and agents from scratch for deep understanding?
    you: not recommended
    AI recommended (in order):
    1. PyTorch
    2. TensorFlow
    3. Hugging Face Transformers
    4. Faiss
    5. NLTK
    6. spaCy
    7. Scikit-learn

    AI recommended 7 alternatives but never named datawhalechina/tiny-universe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive guide to implement a full LLM system, including diffusion and evaluation metrics.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Diffusers (huggingface/diffusers)
    3. PEFT (huggingface/peft)
    4. Hugging Face evaluate (huggingface/evaluate)
    5. datasets (huggingface/datasets)
    6. PyTorch (pytorch/pytorch)
    7. TensorFlow (tensorflow/tensorflow)
    8. Accelerate (huggingface/accelerate)
    9. PyTorch Lightning (Lightning-AI/lightning)
    10. Keras (keras-team/keras)
    11. scikit-learn (scikit-learn/scikit-learn)
    12. PyTorch DataLoader (pytorch/pytorch)
    13. tf.data.Dataset (tensorflow/tensorflow)
    14. DeepSpeed (microsoft/DeepSpeed)
    15. FSDP (pytorch/pytorch)
    16. Weights & Biases (wandb/wandb)
    17. MLflow (mlflow/mlflow)
    18. Ray (ray-project/ray)
    19. Dask (dask/dask)
    20. Ray Tune (ray-project/ray)
    21. Ray Data (ray-project/ray)
    22. Dask DataFrames (dask/dask)
    23. Ray Serve (ray-project/ray)
    24. ONNX Runtime (microsoft/onnxruntime)
    25. TensorRT (NVIDIA/TensorRT)
    26. Kubernetes (kubernetes/kubernetes)
    27. Docker (docker/cli)

    AI recommended 27 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.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite