RRepoGEO

REPOGEO REPORT · LITE

ml-explore/mlx-swift

Default branch main · commit 89cece7d · scanned 5/15/2026, 11:37:14 AM

GitHub: 1,833 stars · 222 forks

AI VISIBILITY SCORE
40 /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
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-swift, 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 to clarify its role as an ML application framework

    Why:

    CURRENT
    MLX Swift is a Swift API for MLX.
    COPY-PASTE FIX
    MLX Swift is a framework for building and deploying machine learning applications directly in Swift, leveraging MLX's high-performance optimizations for Apple silicon.
  • mediumtopics#2
    Add specific topics for LLMs, VLMs, and Stable Diffusion

    Why:

    CURRENT
    mlx
    COPY-PASTE FIX
    mlx, swift, machine-learning, deep-learning, apple-silicon, llm, vlm, stable-diffusion, coreml-alternative, swift-ml
  • lowreadme#3
    Add direct link to documentation in README intro

    Why:

    CURRENT
    [**Installation**](#installation) | **Documentation** | [**Examples**](#examples)
    COPY-PASTE FIX
    [**Installation**](#installation) | [**Documentation**](https://ml-explore.github.io/mlx-swift/) | [**Examples**](#examples)

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
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. ml-explore/mlx · recommended 2×
  3. pytorch/pytorch · recommended 2×
  4. Create ML · recommended 1×
  5. TensorFlow Lite · recommended 1×
  • CATEGORY QUERY
    What frameworks allow building machine learning applications using Swift for Apple devices?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Create ML
    3. TensorFlow Lite
    4. PyTorch Mobile
    5. MLX (ml-explore/mlx)

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

    Show full AI answer
  • CATEGORY QUERY
    How can I integrate language models or stable diffusion into my Swift iOS/macOS apps?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Hugging Face Transformers.swift (huggingface/swift-transformers)
    3. MLX (ml-explore/mlx)
    4. ONNX Runtime (microsoft/onnxruntime)
    5. PyTorch Mobile (pytorch/pytorch)
    6. LibTorch (pytorch/pytorch)
    7. Replicate API
    8. OpenAI API
    9. Hugging Face Inference API

    AI recommended 9 alternatives but never named ml-explore/mlx-swift. 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 ml-explore/mlx-swift?
    pass
    AI named ml-explore/mlx-swift 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 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ml-explore/mlx-swift 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 solve, and who is the primary audience?
    pass
    AI named ml-explore/mlx-swift explicitly

    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 — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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