RRepoGEO

REPOGEO REPORT · LITE

moonshine-ai/moonshine

Default branch main · commit 90d824c6 · scanned 5/15/2026, 6:59:40 AM

GitHub: 8,069 stars · 418 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 moonshine-ai/moonshine, 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 statement to clarify core purpose

    Why:

    CURRENT
    # Moonshine Voice

**Voice Interfaces for Everyone[Quickstart](#quickstart)
...
Moonshine Voice is an open source AI toolkit for developers building real-time voice applications.
    COPY-PASTE FIX
    # Moonshine Voice: The open-source, on-device toolkit for real-time voice interfaces and agents.

Build ultra-low latency speech-to-text, intent recognition, and text-to-speech applications that run entirely offline, directly on-device.
  • mediumreadme#2
    Clarify the project's license directly in the README

    Why:

    COPY-PASTE FIX
    Add a clear statement in the README's 'License' section or near the top: 'This project uses a custom license. Please refer to the `LICENSE` file for full details on its terms and conditions.'
  • mediumcomparison#3
    Expand README comparison to include direct competitors

    Why:

    COPY-PASTE FIX
    Expand the 'When should you choose Moonshine over Whisper?' section, or add a new 'Comparison with Alternatives' section, to explicitly detail how Moonshine Voice differentiates itself from competitors like Picovoice, Rhino Speech-to-Intent, Porcupine Wake Word, and Cheetah Speech-to-Text.

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 moonshine-ai/moonshine
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Edge Impulse
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Edge Impulse · recommended 2×
  2. Picovoice · recommended 1×
  3. Rhino Speech-to-Intent · recommended 1×
  4. Porcupine Wake Word · recommended 1×
  5. Cheetah Speech-to-Text · recommended 1×
  • CATEGORY QUERY
    What's a good toolkit for real-time, on-device voice interfaces with low latency?
    you: not recommended
    AI recommended (in order):
    1. Picovoice
    2. Rhino Speech-to-Intent
    3. Porcupine Wake Word
    4. Cheetah Speech-to-Text
    5. Mozilla DeepSpeech
    6. TensorFlow Lite
    7. Edge Impulse
    8. OpenVINO

    AI recommended 8 alternatives but never named moonshine-ai/moonshine. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Need an offline speech-to-text and intent recognition library for embedded devices.
    you: not recommended
    AI recommended (in order):
    1. Picovoice Porcupine
    2. Rhino
    3. Mozilla DeepSpeech (mozilla/DeepSpeech)
    4. RASA NLU (RasaHQ/rasa)
    5. Vosk (alphacep/vosk-api)
    6. scikit-learn (scikit-learn/scikit-learn)
    7. TensorFlow Lite (tensorflow/tensorflow)
    8. Snips NLU (snipsco/snips-nlu)
    9. CMU Sphinx (cmusphinx/pocketsphinx)
    10. Edge Impulse

    AI recommended 10 alternatives but never named moonshine-ai/moonshine. 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 moonshine-ai/moonshine?
    pass
    AI named moonshine-ai/moonshine explicitly

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

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

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

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MARKDOWN (README)
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moonshine-ai/moonshine — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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