RRepoGEO

REPOGEO REPORT · LITE

RunanywhereAI/RCLI

Default branch main · commit b73a9682 · scanned 5/11/2026, 8:42:12 AM

GitHub: 1,506 stars · 80 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 RunanywhereAI/RCLI, 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
    Clarify RCLI's core identity in the README's opening

    Why:

    CURRENT
    RCLI is an on-device voice AI for macOS. A complete STT + LLM + TTS + VLM pipeline running natively on Apple Silicon — 40 macOS actions via voice, local RAG over your documents, on-device vision (camera & screen analysis), sub-200ms end-to-end latency. No cloud, no API keys.
    COPY-PASTE FIX
    **RCLI** is a self-contained, on-device voice AI *assistant* for macOS, *not* a cloud or deployment management CLI. It provides a complete STT + LLM + TTS + VLM pipeline running natively on Apple Silicon — enabling 40 macOS actions via voice, local RAG over your documents, on-device vision (camera & screen analysis), and sub-200ms end-to-end latency. No cloud, no API keys.
  • hightopics#2
    Add more specific topics emphasizing macOS control and offline capability

    Why:

    CURRENT
    ai-assistant, apple-silicon, kitten-tts, kokoro-tts, lfm2, llama-cpp, llm, local-ai, metal, on-device-ai, parakeet, qwen3, rag, speech-to-text, text-to-speech, tool-calling, voice-assistant
    COPY-PASTE FIX
    ai-assistant, apple-silicon, kitten-tts, kokoro-tts, lfm2, llama-cpp, llm, local-ai, macos-automation, metal, offline-ai, on-device-ai, parakeet, qwen3, rag, speech-to-text, text-to-speech, tool-calling, voice-assistant, voice-control
  • mediumcomparison#3
    Add a 'Why RCLI?' comparison section to the README

    Why:

    COPY-PASTE FIX
    ## Why RCLI? (vs. LM Studio, Whisper.cpp, Apple Speech Framework, etc.)

RCLI stands apart by offering a *complete, integrated voice AI assistant* for macOS, running entirely on-device. Unlike model runners like LM Studio, RCLI provides a full STT+LLM+TTS+VLM pipeline with macOS automation. Unlike libraries such as Whisper.cpp or Apple Speech Framework, RCLI is an end-to-end application, not just a component, designed for direct user interaction and control of your Mac without requiring cloud services or complex integrations.

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 RunanywhereAI/RCLI
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 2×
  2. ggerganov/whisper.cpp · recommended 1×
  3. Apple Speech Framework · recommended 1×
  4. Apple Speech Synthesis Framework · recommended 1×
  5. nateshmbhat/pyttsx3 · recommended 1×
  • CATEGORY QUERY
    How can I run a voice assistant and RAG entirely on my Mac locally?
    you: not recommended
    AI recommended (in order):
    1. Whisper.cpp (ggerganov/whisper.cpp)
    2. Apple Speech Framework
    3. Apple Speech Synthesis Framework
    4. pyttsx3 (nateshmbhat/pyttsx3)
    5. Piper (rhasspy/piper)
    6. Ollama (ollama/ollama)
    7. LM Studio
    8. LocalAI (mudler/LocalAI)
    9. ChromaDB (chroma-core/chroma)
    10. FAISS (facebookresearch/faiss)
    11. LanceDB (lancedb/lancedb)
    12. LangChain (langchain-ai/langchain)
    13. LlamaIndex (run-llama/llama_index)
    14. sentence-transformers (UKPLab/sentence-transformers)

    AI recommended 14 alternatives but never named RunanywhereAI/RCLI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a local AI to control my Mac with voice and analyze documents offline.
    you: not recommended
    AI recommended (in order):
    1. Whisper.cpp
    2. AppleScript
    3. Python
    4. subprocess
    5. AppKit
    6. PyObjC
    7. Ollama
    8. Llama 3
    9. Mistral
    10. PyPDF2
    11. pdfminer.six
    12. Apple's built-in dictation
    13. LM Studio
    14. Jan
    15. Faraday
    16. Voice Control
    17. Shortcuts
    18. Automator
    19. Homebrew
    20. whisper
    21. llama.cpp

    AI recommended 21 alternatives but never named RunanywhereAI/RCLI. 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 RunanywhereAI/RCLI?
    pass
    AI named RunanywhereAI/RCLI explicitly

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

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

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

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RunanywhereAI/RCLI — 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