RRepoGEO

REPOGEO REPORT · LITE

gpu-mode/resource-stream

Default branch main · commit 5c0efa14 · scanned 6/20/2026, 9:17:56 AM

GitHub: 2,184 stars · 132 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
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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    gpu-programming, cuda, gpu-resources, learning-resources, performance-optimization, kernel-development, triton, torch-compile
  • highabout#2
    Clarify the 'About' description to explicitly state it's a resource collection

    Why:

    CURRENT
    GPU programming related news and material links
    COPY-PASTE FIX
    A curated collection of learning resources, news, and materials for GPU programming, focusing on CUDA, performance optimization, and kernel development.
  • mediumreadme#3
    Reinforce the 'resource collection' aspect in the README's main heading

    Why:

    CURRENT
    # GPU MODE Resource Stream
    COPY-PASTE FIX
    # GPU MODE Resource Stream: Curated Learning Resources for GPU Programming

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
DPC++
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DPC++ · recommended 2×
  2. NVIDIA CUDA · recommended 1×
  3. Udemy · recommended 1×
  4. CUDA C/C++: Mastering GPU Programming · recommended 1×
  5. Coursera · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive learning resources for general purpose GPU programming?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA
    2. Udemy
    3. CUDA C/C++: Mastering GPU Programming
    4. Coursera
    5. Introduction to Parallel Programming
    6. OpenCL
    7. Khronos Group
    8. AMD ROCm
    9. Programming Massively Parallel Processors: A Hands-on Approach
    10. Intel oneAPI
    11. DPC++
    12. SYCL

    AI recommended 12 alternatives but never named gpu-mode/resource-stream. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best resources for optimizing GPU performance and developing custom kernels?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA Toolkit Documentation
    2. NVIDIA Nsight Systems
    3. NVIDIA Nsight Compute
    4. CUDA by Example
    5. OpenCL Specification
    6. Khronos Group Resources
    7. AMD ROCm Documentation
    8. HIP
    9. ROCm-Profiler
    10. rocprof
    11. Intel oneAPI DPC++ Documentation
    12. DPC++
    13. Intel VTune Profiler
    14. GPU Gems Series

    AI recommended 14 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?

Embed your GEO score

Drop this badge into the README of gpu-mode/resource-stream. 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/resource-stream.svg)](https://repogeo.com/en/r/gpu-mode/resource-stream)
HTML
<a href="https://repogeo.com/en/r/gpu-mode/resource-stream"><img src="https://repogeo.com/badge/gpu-mode/resource-stream.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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