RRepoGEO

REPOGEO REPORT · LITE

he-y/Awesome-Pruning

Default branch master · commit 45ac58b4 · scanned 6/30/2026, 5:37:44 PM

GitHub: 2,494 stars · 331 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 he-y/Awesome-Pruning, 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
    Expand repository topics to emphasize research and resource list nature

    Why:

    CURRENT
    awesome-list, model-acceleration, model-compression, pruning
    COPY-PASTE FIX
    awesome-list, model-acceleration, model-compression, pruning, deep-learning-research, research-papers, survey, machine-learning-resources
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root with your chosen open-source license (e.g., MIT, Apache-2.0, GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the repository settings

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Set the homepage URL in the repository settings to a relevant link, such as the author's academic profile or a project page if one exists.

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 he-y/Awesome-Pruning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. TensorFlow Lite · recommended 1×
  3. PyTorch Mobile · recommended 1×
  4. TensorFlow Model Optimization Toolkit · recommended 1×
  5. PyTorch Pruning · recommended 1×
  • CATEGORY QUERY
    How can I reduce the size and improve inference speed of my deep learning models?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite
    2. PyTorch Mobile
    3. ONNX Runtime
    4. TensorFlow Model Optimization Toolkit
    5. PyTorch Pruning
    6. NVIDIA's Automatic Mixed Precision (AMP)
    7. cuDNN
    8. Hugging Face Transformers
    9. DistilBERT
    10. TinyBERT
    11. TensorFlow/Keras
    12. PyTorch
    13. MobileNet
    14. EfficientNet
    15. ShuffleNet
    16. NVIDIA TensorRT
    17. OpenVINO Toolkit
    18. Core ML
    19. ONNX Runtime
    20. JAX

    AI recommended 20 alternatives but never named he-y/Awesome-Pruning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find comprehensive resources on neural network pruning techniques for model optimization?
    you: not recommended
    AI recommended (in order):
    1. Papers With Code
    2. Distiller (IntelLabs/distiller)
    3. PyTorch (pytorch/pytorch)
    4. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    5. Deep Learning Specialization

    AI recommended 5 alternatives but never named he-y/Awesome-Pruning. 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 he-y/Awesome-Pruning?
    pass
    AI named he-y/Awesome-Pruning explicitly

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

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

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

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he-y/Awesome-Pruning — 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