RRepoGEO

REPOGEO REPORT · LITE

fikrikarim/parlor

Default branch main · commit 1d37c674 · scanned 5/11/2026, 8:57:52 PM

GitHub: 1,752 stars · 213 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 fikrikarim/parlor, 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 H1 and opening sentence to clarify it's an AI assistant application

    Why:

    CURRENT
    # Parlor
On-device, real-time multimodal AI. Have natural voice and vision conversations with an AI that runs entirely on your machine.
    COPY-PASTE FIX
    # Parlor: Your On-Device, Real-Time Multimodal AI Assistant
Parlor is a fully functional, real-time multimodal AI assistant that runs entirely on your machine, enabling natural voice and vision conversations.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a URL to a dedicated project page, demo, or a more detailed landing page for Parlor.
  • mediumtopics#3
    Add more explicit 'AI assistant' and 'conversational AI' topics

    Why:

    CURRENT
    apple-silicon, gemma, kokoro, litert-lm, local-llm, mlx, multimodal, on-device-ai, python, real-time, speech-recognition, text-to-speech, voice-assistant
    COPY-PASTE FIX
    apple-silicon, gemma, kokoro, litert-lm, local-llm, mlx, multimodal, on-device-ai, python, real-time, speech-recognition, text-to-speech, voice-assistant, ai-assistant, conversational-ai

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 fikrikarim/parlor
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
NVIDIA Jetson Platform
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. NVIDIA Jetson Platform · recommended 1×
  2. TensorFlow Lite · recommended 1×
  3. PyTorch Mobile · recommended 1×
  4. OpenCV · recommended 1×
  5. Picovoice · recommended 1×
  • CATEGORY QUERY
    How to build a real-time, on-device voice and vision AI assistant?
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Jetson Platform
    2. TensorFlow Lite
    3. PyTorch Mobile
    4. OpenCV
    5. Picovoice
    6. Porcupine
    7. Rhino
    8. Mozilla DeepSpeech
    9. NVIDIA Riva
    10. eSpeak NG
    11. MaryTTS
    12. Python

    AI recommended 12 alternatives but never named fikrikarim/parlor. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are options for developing a local multimodal conversational AI on Apple Silicon?
    you: not recommended
    AI recommended (in order):
    1. MLX (apple/mlx)
    2. PyTorch (pytorch/pytorch)
    3. Hugging Face Transformers (huggingface/transformers)
    4. Hugging Face Diffusers (huggingface/diffusers)
    5. llama.cpp (ggerganov/llama.cpp)
    6. OpenCV (opencv/opencv)
    7. TensorFlow (tensorflow/tensorflow)
    8. tensorflow-metal (apple/tensorflow-metal)
    9. Core ML

    AI recommended 9 alternatives but never named fikrikarim/parlor. 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 fikrikarim/parlor?
    pass
    AI named fikrikarim/parlor explicitly

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

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

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

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fikrikarim/parlor — 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