RRepoGEO

REPOGEO REPORT · LITE

gpu-mode/awesomeMLSys

Default branch main · commit 49031c21 · scanned 6/23/2026, 12:27:53 PM

GitHub: 1,089 stars · 43 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
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 gpu-mode/awesomeMLSys, 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
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    ml-systems, machine-learning-systems, mlsys, reading-list, onboarding, attention-mechanisms, performance-optimization, awesome-list, deep-learning, machine-learning
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the MIT License text. (Or choose another appropriate open-source license if preferred.)
  • mediumreadme#3
    Refine the README's opening paragraph to emphasize its curated, onboarding nature

    Why:

    CURRENT
    This is a reading list of papers/videos/repos I've personally found useful as I was ramping up on ML Systems and that I wish more people would just sit and study carefully during their work hours. If you're looking for more recommendations, go through the citations of the below papers and enjoy!
    COPY-PASTE FIX
    This is a highly curated, opinionated reading list of essential papers, videos, and repositories for anyone onboarding or deepening their understanding of Machine Learning Systems. It focuses on foundational concepts and practical optimizations, designed to guide you through the most impactful resources I've personally found useful.

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 gpu-mode/awesomeMLSys
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Awesome MLOps
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Awesome MLOps · recommended 1×
  2. ML System Design Interview · recommended 1×
  3. Google's Rules of Machine Learning · recommended 1×
  4. Designing Machine Learning Systems · recommended 1×
  5. Machine Learning Engineering · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated list of essential readings for machine learning systems engineering?
    you: not recommended
    AI recommended (in order):
    1. Awesome MLOps
    2. ML System Design Interview
    3. Google's Rules of Machine Learning
    4. Designing Machine Learning Systems
    5. Machine Learning Engineering
    6. Production Machine Learning
    7. The MLOps Community

    AI recommended 7 alternatives but never named gpu-mode/awesomeMLSys. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the foundational papers and resources for understanding and optimizing deep learning attention mechanisms?
    you: not recommended
    AI recommended (in order):
    1. Vision Transformer (ViT)
    2. Linformer
    3. Performer
    4. Reformer
    5. BigBird
    6. FlashAttention

    AI recommended 6 alternatives but never named gpu-mode/awesomeMLSys. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 gpu-mode/awesomeMLSys?
    pass
    AI named gpu-mode/awesomeMLSys explicitly

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

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

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

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