RRepoGEO

REPOGEO REPORT · LITE

PaddleJitLab/CUDATutorial

Default branch develop · commit 640ff87f · scanned 6/30/2026, 4:02:58 PM

GitHub: 1,028 stars · 103 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
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 PaddleJitLab/CUDATutorial, 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
    Add a concise English introduction to the README

    Why:

    COPY-PASTE FIX
    This repository offers a comprehensive, hands-on tutorial for CUDA high-performance programming, with a strong focus on practical kernel optimization techniques and efficient GPU memory access. Learn to build, profile, and optimize CUDA applications from beginner to advanced levels through detailed, runnable examples.
  • mediumhomepage#2
    Set the repository homepage URL

    Why:

    COPY-PASTE FIX
    https://cuda.keter.top/
  • mediumtopics#3
    Update repository topics for better specificity

    Why:

    CURRENT
    cuda-programming, deep-learning
    COPY-PASTE FIX
    cuda-programming, cuda-optimization, gpu-programming, high-performance-computing

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 PaddleJitLab/CUDATutorial
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
CUDA C++ Programming Guide
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. CUDA C++ Programming Guide · recommended 2×
  2. Professional CUDA C Programming · recommended 2×
  3. CUDA by Example: An Introduction to General-Purpose GPU Programming · recommended 2×
  4. CUDA Samples · recommended 1×
  5. CUDA Training Series · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive tutorial to learn CUDA high-performance programming?
    you: not recommended
    AI recommended (in order):
    1. CUDA C++ Programming Guide
    2. CUDA Samples
    3. CUDA Training Series
    4. CUDA C/C++: Mastering GPU Programming
    5. Introduction to Parallel Programming
    6. Professional CUDA C Programming
    7. CUDA by Example: An Introduction to General-Purpose GPU Programming
    8. OpenACC
    9. NVIDIA HPC SDK

    AI recommended 9 alternatives but never named PaddleJitLab/CUDATutorial. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best resources for optimizing CUDA kernel performance and GPU memory access?
    you: not recommended
    AI recommended (in order):
    1. CUDA C++ Programming Guide
    2. CUDA C++ Best Practices Guide
    3. NVIDIA Nsight Systems
    4. NVIDIA Nsight Compute
    5. Professional CUDA C Programming
    6. CUDA by Example: An Introduction to General-Purpose GPU Programming
    7. Udacity's Parallel Programming with CUDA Course
    8. NVIDIA Developer Blog
    9. GPU Computing Gems
    10. Stack Overflow

    AI recommended 10 alternatives but never named PaddleJitLab/CUDATutorial. 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 PaddleJitLab/CUDATutorial?
    pass
    AI did not name PaddleJitLab/CUDATutorial — 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 PaddleJitLab/CUDATutorial in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PaddleJitLab/CUDATutorial 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 PaddleJitLab/CUDATutorial solve, and who is the primary audience?
    pass
    AI named PaddleJitLab/CUDATutorial 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 PaddleJitLab/CUDATutorial. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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HTML
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PaddleJitLab/CUDATutorial — 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