RRepoGEO

REPOGEO REPORT · LITE

gpu-mode/awesomeMLSys

Default branch main · commit 49031c21 · scanned 5/13/2026, 3:42:44 AM

GitHub: 1,062 stars · 41 forks

AI VISIBILITY SCORE
23 /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
2 / 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, ml-engineering, mlops, reading-list, curated-list, attention-mechanism, performance-optimization, deep-learning, papers, research
  • highabout#2
    Refine the repository's 'About' description

    Why:

    CURRENT
    An ML Systems Onboarding list
    COPY-PASTE FIX
    A curated reading list of essential papers, videos, and repositories for onboarding into ML Systems, covering topics like attention mechanisms and performance optimizations.
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, for example, with the MIT License text.

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
Stanford CS 329S: Machine Learning Systems Design
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Stanford CS 329S: Machine Learning Systems Design · recommended 1×
  2. Designing Machine Learning Systems · recommended 1×
  3. Google's Rules of Machine Learning: Best Practices for ML Engineering · recommended 1×
  4. visenger/awesome-mlops · recommended 1×
  5. Machine Learning Engineering · recommended 1×
  • CATEGORY QUERY
    Where can I find a curated reading list to learn about ML systems?
    you: not recommended
    AI recommended (in order):
    1. Stanford CS 329S: Machine Learning Systems Design
    2. Designing Machine Learning Systems
    3. Google's Rules of Machine Learning: Best Practices for ML Engineering
    4. Awesome MLOps (visenger/awesome-mlops)
    5. Machine Learning Engineering
    6. MLOps Community
    7. Practical MLOps

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

    Show full AI answer
  • CATEGORY QUERY
    What resources are available for understanding attention mechanisms and performance optimizations in ML?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face (huggingface/transformers)
    2. NVIDIA
    3. CUDA
    4. cuDNN
    5. TensorRT
    6. PyTorch (pytorch/pytorch)
    7. TensorFlow (tensorflow/tensorflow)

    AI recommended 7 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 did not name gpu-mode/awesomeMLSys — 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 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?

Embed your GEO score

Drop this badge into the README of gpu-mode/awesomeMLSys. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/gpu-mode/awesomeMLSys.svg)](https://repogeo.com/en/r/gpu-mode/awesomeMLSys)
HTML
<a href="https://repogeo.com/en/r/gpu-mode/awesomeMLSys"><img src="https://repogeo.com/badge/gpu-mode/awesomeMLSys.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

gpu-mode/awesomeMLSys — 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