RRepoGEO

REPOGEO REPORT · LITE

ml-explore/mlx-swift

Default branch main · commit e23ae6b2 · scanned 6/26/2026, 3:31:45 AM

GitHub: 1,937 stars · 247 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
33 /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
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, 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 paragraph to clearly state its core purpose and category

    Why:

    CURRENT
    MLX Swift is a Swift API for MLX.
MLX is an array framework for machine learning on Apple silicon. MLX Swift expands MLX to the Swift language, making research and experimentation easier on Apple silicon.
    COPY-PASTE FIX
    MLX Swift is a powerful Swift array framework for high-performance machine learning, enabling deep learning training and inference directly on Apple silicon. It provides a native Swift API for MLX, making research and experimentation with models like LLMs and VLMs seamless on Apple platforms.
  • hightopics#2
    Add more specific topics to improve categorization for deep learning and LLM use cases

    Why:

    CURRENT
    mlx
    COPY-PASTE FIX
    mlx, swift, machine-learning, deep-learning, apple-silicon, llm, vlm, on-device-ai, array-programming, differentiable-programming
  • mediumreadme#3
    Expand the 'Language Models' section to highlight local execution and training capabilities

    Why:

    CURRENT
    ## Language Models
LLM and VLM implementations are available in mlx-swift-lm.
    COPY-PASTE FIX
    ## Language Models
MLX Swift provides robust support for integrating and running large language models (LLMs) and vision language models (VLMs) directly on Apple silicon. Implementations are available in `mlx-swift-lm`, allowing for efficient on-device inference and fine-tuning within your Swift applications.

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. Core ML Tools · recommended 1×
  3. Swift for TensorFlow (S4TF) · recommended 1×
  4. Create ML · recommended 1×
  5. ML Compute · recommended 1×
  • CATEGORY QUERY
    What's the best Swift library for training and running deep learning on Apple hardware?
    you: not recommended
    AI recommended (in order):
    1. Core ML
    2. Core ML Tools
    3. Swift for TensorFlow (S4TF)
    4. Create ML
    5. ML Compute
    6. TensorFlow
    7. PyTorch

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

    Show full AI answer
  • CATEGORY QUERY
    Need a Swift library to integrate large language models into my macOS or iOS app.
    you: not recommended
    AI recommended (in order):
    1. OpenAI Swift (openai/openai-swift)
    2. Swift AI (Swift-AI/Swift-AI)
    3. GoogleGenerativeAI for Swift (google/generative-ai-swift)
    4. Hugging Face Transformers (huggingface/transformers)
    5. PythonKit (pvieito/PythonKit)
    6. Core ML
    7. coremltools (apple/coremltools)
    8. Alamofire (Alamofire/Alamofire)

    AI recommended 8 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 did not name ml-explore/mlx-swift — 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 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?

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ml-explore/mlx-swift — 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