REPOGEO REPORT · LITE

MAC-AutoML/MindPipe

Default branch main · commit 1d1345d2 · scanned 5/7/2026, 4:22:53 PM

GitHub: 914 stars · 24 forks

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

Reposition the README's opening to explicitly state the project's core purpose and hardware focus

Why:

CURRENT

# MindPipe

[English](README.md) | [中文](README_zh.md)

MindPipe is a unified compression and evaluation framework for large language models and vision-language models.

COPY-PASTE FIX

# MindPipe: LLM/LVLM Compression & Evaluation Framework

[English](README.md) | [中文](README_zh.md)

MindPipe is a unified compression and evaluation framework for large language models and vision-language models, specifically designed for efficient deployment on NVIDIA GPUs and Huawei Ascend NPUs.

highlicense#2

Add a LICENSE file or explicitly state the license in the README

Why:

COPY-PASTE FIX

Add a LICENSE file to the repository root with the chosen license (e.g., MIT, Apache-2.0, GPL-3.0), or explicitly state the license(s) in the README's 'About' section.

mediumabout#3
Add a homepage URL to the About section
Why:
COPY-PASTE FIX
```
https://mac-automl.github.io/MindPipe
```

Category GEO backends resolved for this scan: google/gemini-2.0-flash-001, deepseek/deepseek-chat

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.0-flash-001. 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

Of all named tools, what % are you?

Top rival

TensorRT

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

TensorRT · recommended 2×
ONNX Runtime · recommended 2×
Optimum · recommended 1×
Neural Compressor · recommended 1×
OpenVINO · recommended 1×

CATEGORY QUERY
Need a unified framework for LLM compression and evaluation across GPU and NPU hardware.
you: not recommended
AI recommended (in order):
1. Optimum
2. Neural Compressor
3. OpenVINO
4. TensorRT
5. TVM
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
What are robust tools for quantizing and pruning large language and vision models?
you: not recommended
AI recommended (in order):
1. ONNX Runtime
2. TensorRT
3. Intel Neural Compressor (INC)
4. Optimum (Hugging Face)
5. Qualcomm AI Engine (QAI)
6. TensorFlow Model Optimization Toolkit
7. PyTorch Pruning API
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?

Embed your GEO score

Drop this badge into the README of MAC-AutoML/MindPipe. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/MAC-AutoML/MindPipe.svg)](https://repogeo.com/en/r/MAC-AutoML/MindPipe)

HTML

<a href="https://repogeo.com/en/r/MAC-AutoML/MindPipe"><img src="https://repogeo.com/badge/MAC-AutoML/MindPipe.svg" alt="RepoGEO" /></a>

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