RRepoGEO

REPOGEO REPORT · LITE

wafer-ai/gpu-perf-engineering-resources

Default branch main · commit 42f09089 · scanned 6/8/2026, 8:08:12 PM

GitHub: 807 stars · 95 forks

AI VISIBILITY SCORE
22 /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
1 / 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 wafer-ai/gpu-perf-engineering-resources, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Add a concise tagline under the H1 emphasizing 'structured curriculum'

    Why:

    COPY-PASTE FIX
    Add a line directly under the H1, such as: 'A structured curriculum and comprehensive guide for mastering GPU performance engineering in AI infrastructure.'
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT or Apache-2.0) to the repository root, or explicitly state the licensing terms in the README.

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 wafer-ai/gpu-perf-engineering-resources
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
PyTorch
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. PyTorch · recommended 2×
  2. NVIDIA Developer Blog · recommended 1×
  3. NVIDIA DLI (Deep Learning Institute) Courses · recommended 1×
  4. NVIDIA Documentation · recommended 1×
  5. CUDA · recommended 1×
  • CATEGORY QUERY
    Where can I find resources to learn GPU performance optimization for AI infrastructure?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Developer Blog
    2. NVIDIA DLI (Deep Learning Institute) Courses
    3. NVIDIA Documentation
    4. CUDA
    5. cuDNN
    6. TensorRT
    7. CUDA C++ Programming Guide
    8. Professional CUDA C Programming
    9. PyTorch
    10. TensorFlow
    11. NVIDIA Nsight Systems
    12. NVIDIA Nsight Compute
    13. OpenAI Triton
    14. GPU Gems

    AI recommended 14 alternatives but never named wafer-ai/gpu-perf-engineering-resources. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I improve the performance of my AI models running on GPU hardware?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. PyTorch
    3. ONNX Runtime
    4. DeepSpeed
    5. Nsight Systems
    6. Nsight Compute
    7. XLA (Accelerated Linear Algebra)
    8. Triton Inference Server

    AI recommended 8 alternatives but never named wafer-ai/gpu-perf-engineering-resources. 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 wafer-ai/gpu-perf-engineering-resources?
    pass
    AI did not name wafer-ai/gpu-perf-engineering-resources — 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 wafer-ai/gpu-perf-engineering-resources in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wafer-ai/gpu-perf-engineering-resources 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 wafer-ai/gpu-perf-engineering-resources solve, and who is the primary audience?
    pass
    AI did not name wafer-ai/gpu-perf-engineering-resources — 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?

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wafer-ai/gpu-perf-engineering-resources — 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