RRepoGEO

REPOGEO REPORT · LITE

ml-explore/mlx-examples

Default branch main · commit 796f5b53 · scanned 7/1/2026, 1:58:19 AM

GitHub: 8,789 stars · 1,195 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
28 /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
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 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
  • hightopics#1
    Add comprehensive ML-related topics to improve categorization

    Why:

    CURRENT
    mlx
    COPY-PASTE FIX
    mlx, machine-learning, deep-learning, llm, large-language-models, fine-tuning, image-generation, image-classification, speech-recognition, computer-vision, nlp, audio-processing, apple-silicon
  • highreadme#2
    Strengthen README's introductory positioning to assert its role as a primary resource

    Why:

    CURRENT
    # MLX Examples

This repo contains a variety of standalone examples using the MLX framework.
    COPY-PASTE FIX
    # MLX Examples

This repository is the official, comprehensive collection of standalone examples for the MLX framework, designed to help developers and researchers quickly get started with machine learning on Apple silicon. Explore practical implementations across various domains, from large language models and image generation to speech recognition and multimodal AI.
  • mediumreadme#3
    Add a 'Why MLX Examples?' section to explicitly state its unique value proposition

    Why:

    COPY-PASTE FIX
    ## Why MLX Examples?

This repository provides practical, ready-to-run examples specifically tailored for the MLX framework, leveraging its unique design for efficient machine learning on Apple silicon (CPUs and GPUs). Unlike general framework tutorials, these examples are optimized to showcase MLX's capabilities and serve as direct starting points for your MLX projects.

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
Hugging Face Transformers Library
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers Library · recommended 2×
  2. PyTorch · recommended 1×
  3. TensorFlow · recommended 1×
  4. Hugging Face Datasets Library · recommended 1×
  5. Weights & Biases · recommended 1×
  • CATEGORY QUERY
    How to get started with large language model development, including fine-tuning techniques?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. PyTorch
    3. TensorFlow
    4. Hugging Face Datasets Library
    5. Weights & Biases
    6. Google Colaboratory
    7. Kaggle Notebooks
    8. OpenAI API
    9. Anthropic API
    10. Google Gemini API
    11. LoRA
    12. QLoRA
    13. Hugging Face PEFT library

    AI recommended 13 alternatives but never named ml-explore/mlx-examples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical examples for building image classification, generation, or speech recognition models.
    you: not recommended
    AI recommended (in order):
    1. TensorFlow Tutorials
    2. PyTorch Examples (pytorch/examples)
    3. Keras Examples
    4. Hugging Face Transformers Library
    5. fast.ai Course Notebooks
    6. DeepLearning.AI Coursera Courses
    7. Papers With Code

    AI recommended 7 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 did not name ml-explore/mlx-examples — 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 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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ml-explore/mlx-examples — 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