RRepoGEO

REPOGEO REPORT · LITE

ml-explore/mlx-examples

Default branch main · commit 796f5b53 · scanned 5/19/2026, 4:27:48 PM

GitHub: 8,623 stars · 1,163 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
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 ml-explore/mlx-examples, 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
  • highabout#1
    Expand the repository's 'About' description

    Why:

    CURRENT
    Examples in the MLX framework
    COPY-PASTE FIX
    Practical, standalone code examples demonstrating various machine learning models and tasks using the MLX array framework.
  • hightopics#2
    Add more specific topics to improve discoverability

    Why:

    CURRENT
    mlx
    COPY-PASTE FIX
    mlx, machine-learning-examples, deep-learning-examples, llm-examples, image-generation, audio-models, multimodal-models, apple-mlx, mlx-framework
  • mediumhomepage#3
    Add a homepage link to the MLX framework documentation

    Why:

    COPY-PASTE FIX
    https://ml-explore.github.io/mlx/build/html/index.html

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 ml-explore/mlx-examples
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. TensorFlow · recommended 1×
  3. PyTorch · recommended 1×
  4. Keras · recommended 1×
  5. fastai/fastbook · recommended 1×
  • CATEGORY QUERY
    Where can I find practical code examples for various deep learning models and tasks?
    you: not recommended
    AI recommended (in order):
    1. TensorFlow
    2. PyTorch
    3. Keras
    4. Hugging Face Transformers (huggingface/transformers)
    5. fast.ai (fastai/fastbook)
    6. Kaggle

    AI recommended 6 alternatives but never named ml-explore/mlx-examples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to get started with example implementations for large language models or image generation?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Hugging Face Diffusers (huggingface/diffusers)
    3. Hugging Face Hub
    4. PyTorch (pytorch/pytorch)
    5. TensorFlow (tensorflow/tensorflow)
    6. OpenAI API
    7. Keras (keras-team/keras)
    8. FastAI (fastai/fastai)
    9. Google Colaboratory

    AI recommended 9 alternatives but never named ml-explore/mlx-examples. 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 ml-explore/mlx-examples?
    pass
    AI named ml-explore/mlx-examples explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite