RRepoGEO

REPOGEO REPORT · LITE

ml-explore/mlx-swift-examples

Default branch main · commit 378f2449 · scanned 6/25/2026, 7:02:02 AM

GitHub: 2,611 stars · 408 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)

3 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-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 README H1 and opening paragraph to specify category

    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

Practical examples for running and training machine learning models, including Large Language Models (LLMs) and Stable Diffusion, directly on Apple Silicon devices (iOS and macOS) using MLX Swift.
  • hightopics#2
    Add comprehensive topics to improve categorization

    Why:

    CURRENT
    mlx
    COPY-PASTE FIX
    mlx, swift, machine-learning, deep-learning, llm, large-language-models, on-device-ml, apple-silicon, ios, macos, stable-diffusion, training, inference, examples
  • mediumhomepage#3
    Add a homepage URL

    Why:

    COPY-PASTE FIX
    https://github.com/ml-explore/mlx-swift

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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Core ML · recommended 2×
  2. coremltools · recommended 1×
  3. MLX · recommended 1×
  4. llama.cpp · recommended 1×
  5. Swift-Llama · recommended 1×
  • CATEGORY QUERY
    How can I run large language models directly on iOS or macOS devices using Swift?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. coremltools
    3. MLX
    4. llama.cpp
    5. Swift-Llama
    6. ONNX Runtime
    7. TensorFlow Lite

    AI recommended 7 alternatives but never named ml-explore/mlx-swift-examples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for Swift examples to train machine learning models locally on Apple silicon devices.
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Create ML
    3. Core ML Tools
    4. Swift for TensorFlow
    5. Metal Performance Shaders Graph

    AI recommended 5 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 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?

  • 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