RRepoGEO

REPOGEO REPORT · LITE

mryab/efficient-dl-systems

Default branch main · commit e632aa89 · scanned 6/30/2026, 10:02:41 PM

GitHub: 1,008 stars · 149 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
15 /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
0 / 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 mryab/efficient-dl-systems, 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 H1 to explicitly state 'Course Materials'

    Why:

    CURRENT
    # Efficient Deep Learning Systems
    COPY-PASTE FIX
    # Efficient Deep Learning Systems Course Materials
  • highhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/mryab/efficient-dl-systems
  • mediumtopics#3
    Add educational topics to reinforce repo's nature

    Why:

    CURRENT
    cuda, deep-learning, distributed-training, efficient-deep-learning, inference-optimization, machine-learning, ml-infrastructure, ml-systems, mlops, performance-optimization, pytorch
    COPY-PASTE FIX
    cuda, deep-learning, distributed-training, efficient-deep-learning, inference-optimization, machine-learning, ml-infrastructure, ml-systems, mlops, performance-optimization, pytorch, course-materials, educational-resource, lecture-notes, syllabus

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 mryab/efficient-dl-systems
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. NVIDIA CUDA Toolkit · recommended 1×
  3. NVIDIA cuDNN · recommended 1×
  4. NVIDIA TensorRT · recommended 1×
  5. PyTorch · recommended 1×
  • CATEGORY QUERY
    Seeking resources to improve deep neural network training and inference efficiency on GPUs.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA Toolkit
    2. NVIDIA cuDNN
    3. NVIDIA TensorRT
    4. PyTorch
    5. TensorFlow
    6. DeepSpeed
    7. OpenVINO Toolkit

    AI recommended 7 alternatives but never named mryab/efficient-dl-systems. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for distributed deep learning and performance profiling?
    you: not recommended
    AI recommended (in order):
    1. PyTorch Distributed
    2. Horovod
    3. TensorFlow Distributed Strategy API
    4. DeepSpeed
    5. Ray Train
    6. NVIDIA Nsight Systems
    7. PyTorch Profiler
    8. TensorFlow Profiler
    9. cProfile
    10. Linux `perf`

    AI recommended 10 alternatives but never named mryab/efficient-dl-systems. 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 mryab/efficient-dl-systems?
    pass
    AI did not name mryab/efficient-dl-systems — 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 mryab/efficient-dl-systems in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name mryab/efficient-dl-systems — 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?

  • In one sentence, what problem does the repo mryab/efficient-dl-systems solve, and who is the primary audience?
    pass
    AI did not name mryab/efficient-dl-systems — 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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mryab/efficient-dl-systems — 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