REPOGEO REPORT · LITE

mezbaul-h/june

Default branch master · commit cecf1867 · scanned 6/3/2026, 1:42:19 AM

GitHub: 784 stars · 53 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 mezbaul-h/june, 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 to clearly state project's core identity
Why:
CURRENT
```
# june
```
COPY-PASTE FIX
```
# june: Local Voice Chatbot powered by Ollama, Hugging Face, Coqui TTS
```
mediumhomepage#2
Add a homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
[Your project's dedicated website or documentation URL, e.g., https://june-chatbot.dev]
```

mediumreadme#3

Restructure the 'OVERVIEW' section with explicit key features

Why:

CURRENT

**june** is a local voice chatbot that combines the power of Ollama (for language model capabilities), Hugging Face Transformers (for speech recognition), and the Coqui TTS Toolkit (for text-to-speech synthesis). It provides a flexible, privacy-focused solution for voice-assisted interactions on your local machine, ensuring that no data is sent to external servers.

COPY-PASTE FIX

**june** is a local voice chatbot that combines the power of Ollama (for language model capabilities), Hugging Face Transformers (for speech recognition), and the Coqui TTS Toolkit (for text-to-speech synthesis).

### Key Features:
- **100% Local & Privacy-Focused:** All interactions happen on your machine; no data leaves your system.
- **Voice & Text Interaction:** Supports various modes including voice input/audio output.
- **Open-Source Stack:** Built with Ollama, Hugging Face Transformers, and Coqui TTS.
- **Command-Line Interface:** Easily integrate into your workflow.
- **Flexible & Customizable:** Adaptable for different use cases.

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 mezbaul-h/june

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

Vosk

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

Vosk · recommended 2×
Coqui TTS · recommended 2×
MaryTTS · recommended 2×
Piper · recommended 2×
Mycroft AI · recommended 1×

CATEGORY QUERY
How to build a privacy-focused voice assistant that runs entirely on my local machine?
you: not recommended
AI recommended (in order):
1. Mycroft AI
2. Mycroft Core
3. Open Voice OS
4. PocketSphinx
5. Precise
6. Coqui STT
7. Vosk
8. Mimic 3
9. Coqui TTS
10. Rhasspy
11. Kaldi
12. MaryTTS
13. Home Assistant
14. RASA NLU
15. Assist
16. Piper
17. Whisper.cpp
18. Mozilla DeepSpeech
19. pvporcupine
20. spacy
21. Picovoice
22. Porcupine
23. Rhino
24. Leopard
AI recommended 24 alternatives but never named mezbaul-h/june. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are options for a command-line voice assistant using open-source speech and language models?
you: not recommended
AI recommended (in order):
1. Whisper
2. LLaMA
3. Alpaca
4. Vicuna
5. Guanaco
6. WizardLM
7. Coqui TTS
8. PyAudio
9. Vosk
10. GPT4All
11. Piper
12. Rhino Speech-to-Intent
13. eSpeak NG
14. DeepSpeech
15. OpenAssistant
16. MaryTTS
AI recommended 16 alternatives but never named mezbaul-h/june. 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 mezbaul-h/june?
pass
AI named mezbaul-h/june explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts mezbaul-h/june in production, what risks or prerequisites should they evaluate first?
pass
AI named mezbaul-h/june 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 mezbaul-h/june solve, and who is the primary audience?
pass
AI named mezbaul-h/june 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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mezbaul-h/june — 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