RRepoGEO

REPOGEO REPORT · LITE

cfregly/ai-performance-engineering

Default branch main · commit 2f7e30f9 · scanned 5/10/2026, 5:02:54 AM

GitHub: 1,418 stars · 197 forks

AI VISIBILITY SCORE
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 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

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

OVERALL DIRECTION
  • highabout#1
    Add a concise 'About' description

    Why:

    COPY-PASTE FIX
    Code, tooling, and resources for AI Systems Performance Engineering, covering GPU optimization, distributed training, inference scaling, and full-stack tuning for modern AI workloads, accompanying an O'Reilly book.
  • mediumhomepage#2
    Add a homepage URL

    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
DeepSpeed
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. DeepSpeed · recommended 2×
  2. torch.cuda.amp · recommended 1×
  3. tf.keras.mixed_precision · recommended 1×
  4. torch.utils.checkpoint · recommended 1×
  5. tf.recompute_grad · recommended 1×
  • CATEGORY QUERY
    How to optimize GPU usage and memory for deep learning model training?
    you: not recommended
    AI recommended (in order):
    1. torch.cuda.amp
    2. tf.keras.mixed_precision
    3. torch.utils.checkpoint
    4. tf.recompute_grad
    5. DeepSpeed
    6. FairScale
    7. torch.utils.data.DataLoader
    8. tf.data.Dataset

    AI recommended 8 alternatives but never named cfregly/ai-performance-engineering. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tools and techniques for profiling and tuning AI inference performance at scale.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Nsight Systems
    2. Nsight Compute
    3. TensorFlow Profiler
    4. PyTorch Profiler
    5. Intel VTune Amplifier
    6. DeepSpeed
    7. NVIDIA Triton Inference Server
    8. perf
    9. DTrace
    10. Grafana
    11. Prometheus

    AI recommended 11 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
    fail

    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 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?

  • 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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MARKDOWN (README)
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cfregly/ai-performance-engineering — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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