RRepoGEO

REPOGEO REPORT · LITE

cfregly/ai-performance-engineering

Default branch main · commit 86c979d9 · scanned 6/20/2026, 3:27:37 AM

GitHub: 1,598 stars · 226 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
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 cfregly/ai-performance-engineering, 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 comprehensive topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-performance-engineering, gpu-optimization, distributed-training, inference-scaling, full-stack-tuning, ai-systems, machine-learning-performance, deep-learning-optimization, pytorch-profiler, nsight, vllm, tensorrt-llm, nvidia-dynamo, o'reilly-book, performance-tuning, mlops
  • highreadme#2
    Reposition README H1 to clarify book companion status

    Why:

    CURRENT
    # AI Performance Engineering

_**Update:** Are you interested in a hands-on course for this material?_
    COPY-PASTE FIX
    # AI Performance Engineering

**The official companion repository for the O'Reilly book on AI Systems Performance Engineering, offering practical code, labs, and resources for optimizing AI workloads.**

_**Update:** Are you interested in a hands-on course for this material?_
  • mediumhomepage#3
    Add the O'Reilly book's Amazon link as the repository homepage

    Why:

    COPY-PASTE FIX
    https://www.amazon.com/Systems-Performance-Engineering-Optimizing-Algorithms/dp/B0F47689K8/

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 cfregly/ai-performance-engineering
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA CUDA Toolkit
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA CUDA Toolkit · recommended 2×
  2. PyTorch Distributed · recommended 1×
  3. TensorFlow Distributed · recommended 1×
  4. Horovod · recommended 1×
  5. NVIDIA DALI · recommended 1×
  • CATEGORY QUERY
    How to optimize GPU performance and scale distributed training for AI workloads?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA Toolkit
    2. PyTorch Distributed
    3. TensorFlow Distributed
    4. Horovod
    5. NVIDIA DALI
    6. Kubeflow
    7. Ray

    AI recommended 7 alternatives but never named cfregly/ai-performance-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What resources are available for full-stack performance tuning of AI inference systems?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA TensorRT
    2. OpenVINO Toolkit
    3. ONNX Runtime
    4. DeepSpeed
    5. Accelerate (Hugging Face)
    6. PyTorch JIT (TorchScript)
    7. TensorFlow Lite
    8. TensorFlow Serving
    9. Intel oneAPI Base Toolkit
    10. NVIDIA CUDA Toolkit
    11. cuDNN
    12. NVIDIA Nsight Systems
    13. Intel VTune Profiler
    14. PyTorch Profiler
    15. TensorFlow Profiler

    AI recommended 15 alternatives but never named cfregly/ai-performance-engineering. 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 cfregly/ai-performance-engineering?
    pass
    AI did not name cfregly/ai-performance-engineering — 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 cfregly/ai-performance-engineering in production, what risks or prerequisites should they evaluate first?
    pass
    AI named cfregly/ai-performance-engineering 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 cfregly/ai-performance-engineering solve, and who is the primary audience?
    pass
    AI did not name cfregly/ai-performance-engineering — 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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cfregly/ai-performance-engineering — 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