RRepoGEO

REPOGEO REPORT · LITE

jingyaogong/minimind-o

Default branch master · commit f7b0a325 · scanned 5/15/2026, 5:39:06 AM

GitHub: 1,240 stars · 143 forks

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 jingyaogong/minimind-o, 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 core English description to the top of the README

    Why:

    CURRENT
    The current README starts with badges and a Chinese introduction, with the English version linked further down. The core description is not immediately visible in English.
    COPY-PASTE FIX
    A 0.1B Omni model trained from scratch, capable of listening, speaking, and seeing, designed for personal GPU/CPU inference.
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    artificial-intelligence, chatgpt, omni
    COPY-PASTE FIX
    artificial-intelligence, omni-model, multimodal-ai, small-language-model, from-scratch-training, speech-ai, vision-ai, consumer-hardware
  • lowreadme#3
    Add a dedicated 'Key Differentiators' section to the README

    Why:

    COPY-PASTE FIX
    ## ✨ Key Differentiators
*   **Smallest Full Omni Implementation:** At ~0.1B parameters, MiniMind-O is among the smallest complete Omni models, making it accessible for personal training and fast CPU inference.
*   **End-to-End From Scratch:** Provides a full, from-scratch implementation of an Omni model, including training data and code, without reliance on high-level third-party frameworks.
*   **True Multimodality:** Supports text, audio, and visual inputs with text and streaming speech outputs from a single weight.
*   **Consumer Hardware Friendly:** Designed to be trainable on a single consumer GPU (e.g., RTX 3090) and runnable on CPU.

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 jingyaogong/minimind-o
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 1×
  2. timm · recommended 1×
  3. PyTorch Lightning · recommended 1×
  4. Keras · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    How to train a small-scale multimodal AI model from scratch for personal use?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. timm
    3. PyTorch Lightning
    4. Keras
    5. TensorFlow
    6. JAX
    7. Flax

    AI recommended 7 alternatives but never named jingyaogong/minimind-o. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient omni-modal AI for real-time speech and visual interaction on consumer hardware.
    you: not recommended
    AI recommended (in order):
    1. Google MediaPipe
    2. TensorFlow Lite
    3. OpenVINO
    4. NVIDIA Jetson Platform
    5. NVIDIA Riva
    6. DeepStream SDK
    7. PyTorch Mobile
    8. TorchVision
    9. TorchAudio
    10. ONNX Runtime
    11. Hugging Face Transformers

    AI recommended 11 alternatives but never named jingyaogong/minimind-o. 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 jingyaogong/minimind-o?
    pass
    AI did not name jingyaogong/minimind-o — 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 jingyaogong/minimind-o in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jingyaogong/minimind-o 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 jingyaogong/minimind-o solve, and who is the primary audience?
    pass
    AI named jingyaogong/minimind-o 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 jingyaogong/minimind-o. 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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MARKDOWN (README)
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jingyaogong/minimind-o — 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