RRepoGEO

REPOGEO REPORT · LITE

csarron/awesome-emdl

Default branch master · commit 5827c508 · scanned 6/15/2026, 10:17:44 AM

GitHub: 769 stars · 168 forks

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 csarron/awesome-emdl, 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 README's opening to clarify it's a resource list

    Why:

    CURRENT
    Embedded and mobile deep learning research notes.
    COPY-PASTE FIX
    A curated list of research papers, projects, and resources for embedded and mobile deep learning.
  • mediumreadme#2
    Add a clear value proposition to the README's introduction

    Why:

    COPY-PASTE FIX
    This 'awesome list' aims to be the definitive collection for anyone researching or developing efficient deep learning solutions for edge, embedded, and mobile devices.
  • lowhomepage#3
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://github.com/csarron/awesome-emdl

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 csarron/awesome-emdl
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pytorch/pytorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. pytorch/pytorch · recommended 2×
  2. tensorflow/tensorflow · recommended 1×
  3. microsoft/onnxruntime · recommended 1×
  4. tensorflow/model-optimization · recommended 1×
  5. NVIDIA/apex · recommended 1×
  • CATEGORY QUERY
    What are techniques to make deep neural networks efficient for embedded and mobile inference?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Lite (tensorflow/tensorflow)
    2. PyTorch Mobile (pytorch/pytorch)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. TensorFlow Model Optimization Toolkit (tensorflow/model-optimization)
    5. torch.nn.utils.prune (pytorch/pytorch)
    6. NVIDIA APEX (NVIDIA/apex)
    7. Hugging Face Transformers (huggingface/transformers)
    8. Core ML
    9. Android Neural Networks API (NNAPI)
    10. NVIDIA TensorRT
    11. OpenVINO (openvinotoolkit/openvino)

    AI recommended 11 alternatives but never named csarron/awesome-emdl. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find research and resources on optimizing AI for mobile and edge devices?
    you: not recommended
    AI recommended (in order):
    1. Google AI Blog
    2. TensorFlow Lite
    3. Qualcomm AI Research

    AI recommended 3 alternatives but never named csarron/awesome-emdl. 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 csarron/awesome-emdl?
    pass
    AI named csarron/awesome-emdl explicitly

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

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

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

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csarron/awesome-emdl — 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