RRepoGEO

REPOGEO REPORT · LITE

nkasmanoff/pi-card

Default branch main · commit 9899b41f · scanned 6/15/2026, 4:18:22 PM

GitHub: 812 stars · 42 forks

AI VISIBILITY SCORE
30 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 nkasmanoff/pi-card, 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 the README's main heading to explicitly state the project's purpose

    Why:

    CURRENT
    # Pi-C.A.R.D
    COPY-PASTE FIX
    # Pi-C.A.R.D: An AI Voice Assistant for Raspberry Pi
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    raspberry-pi, ai-assistant, voice-assistant, llm, embedded-ai, local-llm, python
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT or Apache-2.0) to the repository root.

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 nkasmanoff/pi-card
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
home-assistant/core
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. home-assistant/core · recommended 2×
  2. mycroftai/mycroft-core · recommended 1×
  3. MycroftAI/precise-lite · recommended 1×
  4. Picovoice/porcupine · recommended 1×
  5. mozilla/DeepSpeech · recommended 1×
  • CATEGORY QUERY
    How to build a personal AI voice assistant running entirely on a Raspberry Pi?
    you: not recommended
    AI recommended (in order):
    1. Mycroft AI (Picroft) (mycroftai/mycroft-core)
    2. precise-lite (MycroftAI/precise-lite)
    3. Picovoice Porcupine (Picovoice/porcupine)
    4. DeepSpeech (mozilla/DeepSpeech)
    5. Adapt (MycroftAI/adapt)
    6. Padac (MycroftAI/padac)
    7. Mimic 3 (MycroftAI/mimic3)
    8. Rhasspy (rhasspy/rhasspy)
    9. Snowboy (KITT-AI/snowboy)
    10. Raven
    11. Vosk (alphacep/vosk-api)
    12. Kaldi (kaldi-asr/kaldi)
    13. Home Assistant's Intent Recognizer (home-assistant/core)
    14. OpenFST (openfst/openfst)
    15. FuzzyWuzzy (seatgeek/fuzzywuzzy)
    16. NanoTTS
    17. MaryTTS (marytts/marytts)
    18. Home Assistant (home-assistant/core)
    19. OpenWakeWord (rhasspy/openwakeword)
    20. Whisper (openai/whisper)
    21. Piper (rhasspy/piper)
    22. Picovoice
    23. Cheetah (Picovoice/cheetah)
    24. Rhino (Picovoice/rhino)
    25. Orca (Picovoice/orca)
    26. RASA NLU (RasaHQ/rasa)

    AI recommended 26 alternatives but never named nkasmanoff/pi-card. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are options for deploying a local large language model on embedded hardware?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. LlamaEdge
    3. ONNX Runtime
    4. TensorFlow Lite
    5. PyTorch Mobile
    6. LibTorch
    7. OpenVINO
    8. Qualcomm Neural Processing SDK (SNPE)
    9. NVIDIA TensorRT

    AI recommended 9 alternatives but never named nkasmanoff/pi-card. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 nkasmanoff/pi-card?
    pass
    AI named nkasmanoff/pi-card explicitly

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

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

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

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite