RRepoGEO

REPOGEO REPORT · LITE

jinbooooom/ai-infra-hpc

Default branch main · commit c068e3a8 · scanned 6/11/2026, 12:38:15 AM

GitHub: 546 stars · 58 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
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 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 jinbooooom/ai-infra-hpc, 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
    Reposition the README H1 and subtitle to explicitly state its tutorial nature

    Why:

    CURRENT
    # AI-Infra HPC 学习与总结
### 本仓库用于记录 AI-Infra 与 HPC 技术：
    COPY-PASTE FIX
    # AI-Infra HPC 学习与总结：AI 系统底层技术与高性能计算教程
### 本仓库提供全面的 AI-Infra 与 HPC 技术学习路径和实践教程，涵盖：
  • mediumabout#2
    Enhance the repository description to emphasize its comprehensive educational content

    Why:

    CURRENT
    hpc 教程，包含集合通信(mpi、nccl)、cuda 编程、向量化 SIMD、RDMA 通信等
    COPY-PASTE FIX
    全面的 AI-Infra 与 HPC 教程，深入讲解集合通信 (MPI, NCCL)、CUDA 编程、向量化 SIMD、RDMA 通信等核心技术，助你掌握 AI 系统底层优化。
  • lowtopics#3
    Add specific educational topics to reinforce the repo's format

    Why:

    CURRENT
    ai, ai-infra, deep-learning, hpc, llm
    COPY-PASTE FIX
    ai, ai-infra, deep-learning, hpc, llm, tutorial, learning-path, guide

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 jinbooooom/ai-infra-hpc
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA NCCL
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA NCCL · recommended 2×
  2. NVIDIA CUDA Toolkit · recommended 1×
  3. cuDNN · recommended 1×
  4. cuBLAS · recommended 1×
  5. NVIDIA Nsight Systems · recommended 1×
  • CATEGORY QUERY
    How can I optimize deep learning model performance using CUDA programming and HPC techniques?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA CUDA Toolkit
    2. cuDNN
    3. cuBLAS
    4. NVIDIA Nsight Systems
    5. Nsight Compute
    6. PyTorch
    7. TensorFlow
    8. torch.compile
    9. XLA
    10. torch.cuda.amp
    11. tf.keras.mixed_precision
    12. NVIDIA NCCL
    13. RAPIDS cuDF
    14. cuML
    15. OpenACC
    16. OpenMP Offload
    17. Custom CUDA Kernels

    AI recommended 17 alternatives but never named jinbooooom/ai-infra-hpc. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking tutorials on multi-node multi-GPU communication, including RDMA, MPI, and NCCL protocols.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA NCCL
    2. NVIDIA GPUDirect RDMA
    3. NVIDIA DLI
    4. Open MPI
    5. MPI Forum
    6. LLNL MPI Tutorial
    7. Intel MPI Library
    8. High Performance Computing: Modern Systems and Practices
    9. Hugging Face Accelerate (huggingface/accelerate)
    10. PyTorch (pytorch/pytorch)

    AI recommended 10 alternatives but never named jinbooooom/ai-infra-hpc. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • 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 jinbooooom/ai-infra-hpc?
    pass
    AI did not name jinbooooom/ai-infra-hpc — 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 jinbooooom/ai-infra-hpc in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jinbooooom/ai-infra-hpc 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 jinbooooom/ai-infra-hpc solve, and who is the primary audience?
    pass
    AI named jinbooooom/ai-infra-hpc explicitly

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

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jinbooooom/ai-infra-hpc — 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