RRepoGEO

REPOGEO REPORT · LITE

Blaizzy/mlx-audio-swift

Default branch main · commit 856e04af · scanned 6/3/2026, 9:37:01 AM

GitHub: 644 stars · 107 forks

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 Blaizzy/mlx-audio-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's opening to highlight MLX/Apple Silicon differentiator

    Why:

    CURRENT
    A modular Swift SDK for audio processing with MLX on Apple Silicon
    COPY-PASTE FIX
    MLX Audio Swift is a modular Swift SDK providing high-performance, Metal-accelerated audio processing with MLX on Apple Silicon. It offers native bindings for integrating advanced machine learning models directly into Swift applications for tasks like Text-to-Speech, Speech-to-Text, and Voice Activity Detection.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://[your-project-website-here]
  • lowreadme#3
    Add a 'Why MLX Audio Swift?' or 'Key Features' section to the README

    Why:

    COPY-PASTE FIX
    ## Why MLX Audio Swift?

MLX Audio Swift stands out by offering Swift-native bindings for Apple's MLX Audio framework, enabling Metal-accelerated machine learning audio processing directly within Swift applications. This provides a highly integrated and performant solution for ML-driven audio tasks on Apple Silicon, differentiating it from generic cloud APIs or system frameworks.

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 Blaizzy/mlx-audio-swift
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AVSpeechSynthesizer
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AVSpeechSynthesizer · recommended 2×
  2. SFSpeechRecognizer · recommended 2×
  3. Google Cloud Speech-to-Text API · recommended 2×
  4. Google Cloud Text-to-Speech API · recommended 1×
  5. Amazon Polly · recommended 1×
  • CATEGORY QUERY
    How to implement text-to-speech and speech-to-text features in a Swift app on Apple Silicon?
    you: not recommended
    AI recommended (in order):
    1. AVSpeechSynthesizer
    2. SFSpeechRecognizer
    3. Google Cloud Speech-to-Text API
    4. Google Cloud Text-to-Speech API
    5. Amazon Polly
    6. Amazon Transcribe
    7. Microsoft Azure Cognitive Services Speech
    8. Azure Text to Speech
    9. Azure Speech to Text

    AI recommended 9 alternatives but never named Blaizzy/mlx-audio-swift. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What Swift libraries offer voice activity detection and real-time speech-to-speech capabilities?
    you: not recommended
    AI recommended (in order):
    1. Apple's Speech Framework
    2. SFSpeechRecognizer
    3. AVAudioEngine
    4. AVAudioRecorder
    5. AVSpeechSynthesizer
    6. Vosk Swift
    7. Google Cloud Speech-to-Text API
    8. Amazon Transcribe Streaming API
    9. Picovoice Rhino Speech-to-Intent
    10. Porcupine Wake Word

    AI recommended 10 alternatives but never named Blaizzy/mlx-audio-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
    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 Blaizzy/mlx-audio-swift?
    pass
    AI did not name Blaizzy/mlx-audio-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 Blaizzy/mlx-audio-swift in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Blaizzy/mlx-audio-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 Blaizzy/mlx-audio-swift solve, and who is the primary audience?
    pass
    AI named Blaizzy/mlx-audio-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

Drop this badge into the README of Blaizzy/mlx-audio-swift. 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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Blaizzy/mlx-audio-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