RRepoGEO

REPOGEO REPORT · LITE

cr7258/ai-infra-learning

Default branch main · commit d66cd9ce · scanned 7/1/2026, 3:23:37 PM

GitHub: 502 stars · 61 forks

AI VISIBILITY SCORE
17 /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
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 cr7258/ai-infra-learning, 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 clear introductory sentence to the README

    Why:

    CURRENT
    ## AI Infra 学习会议
    COPY-PASTE FIX
    This repository serves as a curated collection of materials, recordings, and schedules for AI infrastructure learning meetings, focusing on topics like LLM inference optimization, PagedAttention, and speculative decoding.

## AI Infra 学习会议
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-infrastructure, llm-inference, pagedattention, speculative-decoding, prefix-caching, machine-learning-engineering, learning-resources, deep-learning, ai-learning
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0) to clarify usage rights.

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 cr7258/ai-infra-learning
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ONNX Runtime
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. ONNX Runtime · recommended 2×
  2. OpenVINO · recommended 2×
  3. bitsandbytes · recommended 2×
  4. Hugging Face Optimum Library · recommended 1×
  5. DeepSpeed · recommended 1×
  • CATEGORY QUERY
    Where can I find resources to learn about optimizing large language model inference?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Optimum Library
    2. ONNX Runtime
    3. DeepSpeed
    4. NVIDIA TensorRT
    5. OpenVINO
    6. bitsandbytes
    7. AWQ
    8. GPTQ
    9. MLPerf Inference Benchmarks

    AI recommended 9 alternatives but never named cr7258/ai-infra-learning. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to efficiently manage memory and accelerate decoding for large language models?
    you: not recommended
    AI recommended (in order):
    1. vLLM
    2. DeepSpeed-MII
    3. TensorRT-LLM
    4. FlashAttention / FlashAttention-2
    5. bitsandbytes
    6. Hugging Face Optimum
    7. ONNX Runtime
    8. OpenVINO

    AI recommended 8 alternatives but never named cr7258/ai-infra-learning. 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 cr7258/ai-infra-learning?
    pass
    AI did not name cr7258/ai-infra-learning — 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 cr7258/ai-infra-learning in production, what risks or prerequisites should they evaluate first?
    pass
    AI named cr7258/ai-infra-learning 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 cr7258/ai-infra-learning solve, and who is the primary audience?
    pass
    AI did not name cr7258/ai-infra-learning — 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?

Embed your GEO score

Drop this badge into the README of cr7258/ai-infra-learning. 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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cr7258/ai-infra-learning — 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