RRepoGEO

REPOGEO REPORT · LITE

FluidInference/FluidAudio

Default branch main · commit 024bd8e4 · scanned 5/8/2026, 1:12:17 PM

GitHub: 1,988 stars · 271 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 FluidInference/FluidAudio, 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 emphasize CoreML, Apple Neural Engine, and Swift

    Why:

    CURRENT
    FluidAudio is a Swift SDK for fully local, low-latency audio AI on Apple devices, with inference offloaded to the Apple Neural Engine (ANE), resulting in less memory and generally faster inference.
    COPY-PASTE FIX
    FluidAudio is a Swift SDK for fully local, low-latency audio AI on Apple devices, powered by CoreML and the Apple Neural Engine (ANE). It provides state-of-the-art text-to-speech, speech-to-text, voice activity detection, and speaker diarization capabilities.
  • hightopics#2
    Remove the misleading 'nvidia' topic

    Why:

    CURRENT
    nvidia
    COPY-PASTE FIX
    Remove 'nvidia' from the topics list.
  • mediumreadme#3
    Add a 'Why FluidAudio?' or 'Key Features' section to highlight differentiators

    Why:

    COPY-PASTE FIX
    ## Why FluidAudio?
*   **On-Device AI:** Fully local inference on Apple devices, ensuring privacy and low latency.
*   **Apple Neural Engine (ANE) Optimized:** Leverages the ANE for minimal CPU usage, avoiding GPU/MPS, ideal for background and always-on workloads.
*   **State-of-the-Art Models:** Includes optimized CoreML models for speaker diarization, transcription, and voice activity detection.
*   **Swift SDK:** Easy integration into iOS and macOS apps with just a few lines of Swift code.

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 FluidInference/FluidAudio
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
coremltools
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. coremltools · recommended 2×
  2. Apple's Speech Framework · recommended 1×
  3. Core ML · recommended 1×
  4. Google Cloud Speech-to-Text API · recommended 1×
  5. AssemblyAI API · recommended 1×
  • CATEGORY QUERY
    How to implement real-time speech-to-text and speaker diarization on iOS devices?
    you: not recommended
    AI recommended (in order):
    1. Apple's Speech Framework
    2. Core ML
    3. Google Cloud Speech-to-Text API
    4. AssemblyAI API
    5. Whisper (OpenAI)
    6. pyannote.audio
    7. Vosk API

    AI recommended 7 alternatives but never named FluidInference/FluidAudio. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an efficient CoreML solution for on-device audio AI with Apple Neural Engine.
    you: not recommended
    AI recommended (in order):
    1. Create ML
    2. Turi Create
    3. coremltools
    4. coremltools
    5. Metal

    AI recommended 5 alternatives but never named FluidInference/FluidAudio. 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 FluidInference/FluidAudio?
    pass
    AI named FluidInference/FluidAudio explicitly

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

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

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

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FluidInference/FluidAudio — 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