RRepoGEO

REPOGEO REPORT · LITE

gpu-mode/resource-stream

Default branch main · commit 95a4f689 · scanned 5/10/2026, 10:37:37 AM

GitHub: 2,128 stars · 126 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 gpu-mode/resource-stream, 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
    Clarify the repo's purpose as a curated collection of learning materials in the README's opening.

    Why:

    CURRENT
    Here you find a collection of CUDA related material (books, papers, blog-post, youtube videos, tweets, implementations etc.). We also collect information to higher level tools for performance optimization and kernel development like Triton and `torch.compile()` ... whatever makes the GPUs go brrrr.
    COPY-PASTE FIX
    This repository is a curated collection of learning materials and links for GPU programming, focusing on CUDA, performance optimization, and kernel development. It includes books, papers, blog posts, YouTube videos, tweets, and implementations, as well as resources for tools like Triton and `torch.compile()`.
  • hightopics#2
    Add specific topics to improve categorization.

    Why:

    COPY-PASTE FIX
    gpu-programming, cuda, gpu-optimization, parallel-computing, deep-learning, machine-learning, learning-resources, tutorials, education, triton, torch-compile
  • mediumabout#3
    Refine the 'About' description for clarity.

    Why:

    CURRENT
    GPU programming related news and material links
    COPY-PASTE FIX
    A curated collection of learning materials, news, and links for GPU programming, focusing on CUDA, performance optimization, and kernel development.

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/resource-stream
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenCL Specification and Reference Pages
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenCL Specification and Reference Pages · recommended 2×
  2. AMD ROCm Documentation · recommended 2×
  3. NVIDIA CUDA Documentation & Samples · recommended 1×
  4. Udacity's "Intro to Parallel Programming" · recommended 1×
  5. HIP (Heterogeneous-compute Interface for Portability) · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive learning materials for general purpose GPU programming?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA Documentation & Samples
    2. Udacity's "Intro to Parallel Programming"
    3. OpenCL Specification and Reference Pages
    4. AMD ROCm Documentation
    5. HIP (Heterogeneous-compute Interface for Portability)
    6. "CUDA by Example: An Introduction to General-Purpose GPU Programming" by Jason Sanders and Edward Kandrot
    7. "Programming Massively Parallel Processors: A Hands-on Approach" by David B. Kirk and Wen-mei W. Hwu
    8. Intel oneAPI Documentation
    9. DPC++ (Data Parallel C++)

    AI recommended 9 alternatives but never named gpu-mode/resource-stream. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for resources on optimizing GPU performance and developing custom kernels.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA Documentation and Samples
    2. OpenCL Specification and Reference Pages
    3. CUDA by Example: An Introduction to General-Purpose GPU Programming
    4. Professional CUDA C Programming
    5. AMD ROCm Documentation
    6. Intel oneAPI DPC++ Documentation
    7. GPU Gems Series

    AI recommended 7 alternatives but never named gpu-mode/resource-stream. 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 gpu-mode/resource-stream?
    pass
    AI named gpu-mode/resource-stream 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/resource-stream in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gpu-mode/resource-stream 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/resource-stream solve, and who is the primary audience?
    pass
    AI named gpu-mode/resource-stream explicitly

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

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gpu-mode/resource-stream — 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