RRepoGEO

REPOGEO REPORT · LITE

johnmai-dev/ChatMLX

Default branch main · commit 2107e656 · scanned 6/7/2026, 12:06:39 AM

GitHub: 830 stars · 62 forks

AI VISIBILITY SCORE
35 /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
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 johnmai-dev/ChatMLX, 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 more specific application-oriented topics

    Why:

    CURRENT
    chat, llama, mlx, mlx-swift, qwen, swift, swift-transformers, swiftui, transformer, transformers
    COPY-PASTE FIX
    chat, llama, mlx, mlx-swift, qwen, swift, swift-transformers, swiftui, transformer, transformers, macos-app, desktop-app, llm-client, local-llm
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/maiqingqiang/ChatMLX
  • lowreadme#3
    Add a 'Comparison with Alternatives' section to the README

    Why:

    COPY-PASTE FIX
    ## Comparison with Alternatives

Unlike general-purpose LLM runners like LM Studio or Ollama, ChatMLX is specifically designed as a native macOS application built with Swift and Apple's MLX framework. This focus allows for highly optimized performance on Apple Silicon, providing a seamless user experience tailored for macOS users who want a dedicated, high-performance local LLM chat client, rather than a server or a cross-platform solution.

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 johnmai-dev/ChatMLX
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LM Studio
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 1×
  2. Ollama · recommended 1×
  3. Jan · recommended 1×
  4. LocalAI · recommended 1×
  5. llama.cpp · recommended 1×
  • CATEGORY QUERY
    How can I run a local LLM chat application on my Apple silicon Mac?
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. Ollama
    3. Jan
    4. LocalAI
    5. llama.cpp
    6. llama-cpp-python
    7. ctransformers
    8. Hugging Face transformers
    9. bitsandbytes

    AI recommended 9 alternatives but never named johnmai-dev/ChatMLX. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source Swift-based chat clients support local large language models for macOS?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp (ggerganov/llama.cpp)
    2. MLX (apple/mlx)
    3. Hugging Face Transformers (huggingface/transformers)
    4. ChatSecure (ChatSecure/ChatSecure)
    5. Element-iOS/macOS (element-hq/element-ios)
    6. Signal-iOS/macOS (signalapp/Signal-iOS)

    AI recommended 6 alternatives but never named johnmai-dev/ChatMLX. 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 johnmai-dev/ChatMLX?
    pass
    AI named johnmai-dev/ChatMLX explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts johnmai-dev/ChatMLX in production, what risks or prerequisites should they evaluate first?
    pass
    AI named johnmai-dev/ChatMLX 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 johnmai-dev/ChatMLX solve, and who is the primary audience?
    pass
    AI named johnmai-dev/ChatMLX explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of johnmai-dev/ChatMLX. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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johnmai-dev/ChatMLX — 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