RRepoGEO

REPOGEO REPORT · LITE

ml-explore/mlx-swift-examples

Default branch main · commit 357c97fb · scanned 5/14/2026, 5:27:04 PM

GitHub: 2,555 stars · 392 forks

AI VISIBILITY SCORE
22 /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
1 / 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-swift-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
  • highreadme#1
    Reposition the README's opening statement to clarify purpose and audience

    Why:

    CURRENT
    # MLX Swift Examples

Example MLX Swift programs. The language model examples use models implemented in MLX Swift LM.
    COPY-PASTE FIX
    # MLX Swift Examples

This repository offers a comprehensive collection of practical, runnable examples for MLX Swift, showcasing how to build and deploy machine learning models directly on Apple devices (iOS and macOS). It highlights Swift-native training, inference, and fine-tuning capabilities, including LLMs, LoRA, and Stable Diffusion, all optimized for Apple silicon.
  • hightopics#2
    Add comprehensive topics to improve categorization

    Why:

    CURRENT
    mlx
    COPY-PASTE FIX
    mlx, swift, machine-learning, apple-silicon, ios, macos, llm, lora, stable-diffusion, deep-learning, examples, core-ml-alternative
  • mediumabout#3
    Expand the 'About' section description for clarity

    Why:

    CURRENT
    Examples using MLX Swift
    COPY-PASTE FIX
    Practical, Swift-native examples for MLX, demonstrating machine learning training, inference, and fine-tuning on Apple devices (iOS/macOS) with Apple silicon.

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-swift-examples
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Core ML
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 1×
  2. TensorFlow Lite · recommended 1×
  3. PyTorch Mobile · recommended 1×
  4. MLX · recommended 1×
  5. ONNX Runtime · recommended 1×
  • CATEGORY QUERY
    How can I run machine learning models directly on Apple devices?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. TensorFlow Lite
    3. PyTorch Mobile
    4. MLX
    5. ONNX Runtime

    AI recommended 5 alternatives but never named ml-explore/mlx-swift-examples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking examples for fine-tuning large language models on macOS.
    you: not recommended
    AI recommended (in order):
    1. peft (huggingface/peft)
    2. transformers
    3. llama.cpp
    4. mlx (ml-explore/mlx)
    5. mlx-lm
    6. autotrain-advanced

    AI recommended 6 alternatives but never named ml-explore/mlx-swift-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-swift-examples?
    pass
    AI did not name ml-explore/mlx-swift-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-swift-examples in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ml-explore/mlx-swift-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-swift-examples solve, and who is the primary audience?
    pass
    AI did not name ml-explore/mlx-swift-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?

Embed your GEO score

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ml-explore/mlx-swift-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