RRepoGEO

REPOGEO REPORT · LITE

MAC-AutoML/MindPipe

Default branch main · commit 1d1345d2 · scanned 5/8/2026, 6:17:47 AM

GitHub: 938 stars · 24 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)

4 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
35 /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
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 MAC-AutoML/MindPipe, 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 tagline to the README's introduction

    Why:

    CURRENT
    # MindPipe
    COPY-PASTE FIX
    # MindPipe

The Unified LLM/LVLM Model Compression & Evaluation Framework
  • highlicense#2
    Add a standard open-source license file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with a standard open-source license (e.g., Apache-2.0, MIT, or GPL-3.0).
  • mediumhomepage#3
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    Add a link to a project page, documentation, or the organization's main page in the repository's About section.

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 MAC-AutoML/MindPipe
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenVINO Toolkit
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenVINO Toolkit · recommended 1×
  2. NVIDIA TensorRT · recommended 1×
  3. Hugging Face Optimum · recommended 1×
  4. Intel Neural Compressor (INC) · recommended 1×
  5. PyTorch Quantization Recipes / Pruning APIs · recommended 1×
  • CATEGORY QUERY
    Seeking a unified framework for quantizing and pruning large vision language models.
    you: not recommended
    AI recommended (in order):
    1. OpenVINO Toolkit
    2. NVIDIA TensorRT
    3. Hugging Face Optimum
    4. Intel Neural Compressor (INC)
    5. PyTorch Quantization Recipes / Pruning APIs
    6. ONNX Runtime

    AI recommended 6 alternatives but never named MAC-AutoML/MindPipe. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Framework for reproducible LLM compression research across GPU and NPU hardware?
    you: not recommended
    AI recommended (in order):
    1. Optimum (huggingface/optimum)
    2. DeepSpeed (microsoft/DeepSpeed)
    3. ONNX Runtime (microsoft/onnxruntime)
    4. OpenVINO (openvinotoolkit/openvino)
    5. TensorRT (NVIDIA/TensorRT)
    6. PyTorch (pytorch/pytorch)
    7. TensorFlow (tensorflow/tensorflow)

    AI recommended 7 alternatives but never named MAC-AutoML/MindPipe. 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 MAC-AutoML/MindPipe?
    pass
    AI named MAC-AutoML/MindPipe explicitly

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

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

    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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MAC-AutoML/MindPipe — 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