RRepoGEO

REPOGEO REPORT · LITE

Infatoshi/cuda-course

Default branch master · commit 79681bfd · scanned 5/8/2026, 3:48:05 PM

GitHub: 3,583 stars · 630 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 Infatoshi/cuda-course, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the MIT License text, as it is a common and permissive license suitable for educational content.
  • highabout#2
    Add a concise description for the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    A comprehensive, free, and open-source course on CUDA programming, GPU kernel optimization, and high-performance computing for deep learning, covering CUDA, PyTorch, and Triton.
  • hightopics#3
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    cuda, gpu-programming, deep-learning, high-performance-computing, hpc, kernel-optimization, pytorch, triton, freecodecamp, education, course

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 Infatoshi/cuda-course
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Deep Learning Institute (DLI) Courses
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Deep Learning Institute (DLI) Courses · recommended 1×
  2. Udacity's 'Deep Learning Nanodegree' · recommended 1×
  3. Coursera's 'Deep Learning Specialization' by Andrew Ng (DeepLearning.AI) · recommended 1×
  4. fast.ai's 'Practical Deep Learning for Coders' · recommended 1×
  5. Stanford University's CS231n: Convolutional Neural Networks for Visual Recognition (Course Materials) · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive course to learn GPU programming and optimize deep learning models?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Deep Learning Institute (DLI) Courses
    2. Udacity's 'Deep Learning Nanodegree'
    3. Coursera's 'Deep Learning Specialization' by Andrew Ng (DeepLearning.AI)
    4. fast.ai's 'Practical Deep Learning for Coders'
    5. Stanford University's CS231n: Convolutional Neural Networks for Visual Recognition (Course Materials)

    AI recommended 5 alternatives but never named Infatoshi/cuda-course. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I improve computational performance by learning low-level kernel development for accelerators?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA
    2. OpenCL
    3. AMD ROCm
    4. Intel oneAPI
    5. SYCL
    6. Vulkan Compute Shaders
    7. Xilinx Vitis HLS
    8. Intel HLS

    AI recommended 8 alternatives but never named Infatoshi/cuda-course. 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 Infatoshi/cuda-course?
    pass
    AI did not name Infatoshi/cuda-course — 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 Infatoshi/cuda-course in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Infatoshi/cuda-course 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 Infatoshi/cuda-course solve, and who is the primary audience?
    pass
    AI named Infatoshi/cuda-course explicitly

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

Embed your GEO score

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MARKDOWN (README)
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Infatoshi/cuda-course — 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