RRepoGEO

REPOGEO REPORT · LITE

CalvinXKY/InfraTech

Default branch main · commit 83afea71 · scanned 5/21/2026, 7:44:01 AM

GitHub: 2,323 stars · 197 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
30 /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
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 CalvinXKY/InfraTech, 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
    Clarify the README H1 to emphasize 'AI Infrastructure'

    Why:

    CURRENT
    # InfraTech
    COPY-PASTE FIX
    # AI InfraTech: AI Infrastructure Knowledge & Code Practice
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-infrastructure, deep-learning, machine-learning, pytorch, vllm, sglang, large-language-models, llm-inference, performance-optimization, distributed-ai, attention-mechanisms, deep-learning-frameworks, hardware-acceleration
  • mediumlicense#3
    Add a LICENSE file to clarify usage terms

    Why:

    CURRENT
    (no LICENSE file detected)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root. Consider a permissive license like MIT or Apache-2.0 if the content is intended for broad use, or explicitly state the desired terms.

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 CalvinXKY/InfraTech
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers Library
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 2×
  2. NVIDIA Deep Learning Institute (DLI) · recommended 1×
  3. AWS Machine Learning University · recommended 1×
  4. Google Cloud AI Platform · recommended 1×
  5. Microsoft Azure AI · recommended 1×
  • CATEGORY QUERY
    How can I learn about AI infrastructure for large models and performance optimization?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Deep Learning Institute (DLI)
    2. AWS Machine Learning University
    3. Google Cloud AI Platform
    4. Microsoft Azure AI
    5. Hugging Face Transformers Library
    6. PyTorch
    7. TensorFlow

    AI recommended 7 alternatives but never named CalvinXKY/InfraTech. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find practical examples for deep learning attention mechanisms and distributed AI?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Tutorials
    2. PyTorch Examples (pytorch/examples)
    3. Hugging Face Transformers Library
    4. Keras Examples
    5. DeepLearning.AI Coursera Courses
    6. Papers With Code

    AI recommended 6 alternatives but never named CalvinXKY/InfraTech. 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 CalvinXKY/InfraTech?
    pass
    AI named CalvinXKY/InfraTech explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts CalvinXKY/InfraTech in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CalvinXKY/InfraTech 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 CalvinXKY/InfraTech solve, and who is the primary audience?
    pass
    AI named CalvinXKY/InfraTech explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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MARKDOWN (README)
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CalvinXKY/InfraTech — 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