RRepoGEO

REPOGEO REPORT · LITE

qxresearch/qxresearch-event-1

Default branch master · commit 950ea213 · scanned 5/25/2026, 6:52:59 AM

GitHub: 2,672 stars · 820 forks

AI VISIBILITY SCORE
27 /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
1 / 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 qxresearch/qxresearch-event-1, 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
  • highabout#1
    Reposition the 'About' description to emphasize practical Python projects

    Why:

    CURRENT
    Python hands on tutorial with 50+ Python Application (10 lines of code) By @xiaowuc2
    COPY-PASTE FIX
    50+ concise Python applications (10 lines of code each) for hands-on learning in ML, Deep Learning, GUI, CV, and API development. Perfect for quick tutorials and practical project examples.
  • mediumreadme#2
    Add a clear, concise mission statement at the top of the README

    Why:

    CURRENT
    Welcome to our GitHub repository featuring 50+ Python applications with only 10 lines of code! In this repository, you'll find a wide range of topics such as Machine Learning, Deep Learning, GUI, Computer Vision, and API development. Each application is designed to be simple and concise, making it easy to understand and modify. Whether you're a beginner or an experienced developer, these applications are perfect for learning and experimenting with Python. So dive in, explore, and have fun!
    COPY-PASTE FIX
    This repository offers 50+ practical Python applications, each under 10 lines of code, designed for quick learning and hands-on experience in Machine Learning, Deep Learning, GUI, Computer Vision, and API development. Ideal for beginners and experienced developers seeking concise project examples.
  • lowtopics#3
    Expand topics with more specific AI/chatbot project keywords

    Why:

    CURRENT
    ai-project, artificial-intelligence-projects, audio-visualizer, chatbot-python, chatgpt, chatgpt-python, hactoberfest, machine-learning, machine-learning-project, machine-learning-projects, ml-project, python, python-application, python-chatbot, python-mini-projects, python-project, python-projects, python-tutorial, trending-repositories, voice-recorder
    COPY-PASTE FIX
    ai-project, artificial-intelligence-projects, audio-visualizer, chatbot-python, chatgpt, chatgpt-python, hactoberfest, machine-learning, machine-learning-project, machine-learning-projects, ml-project, python, python-application, python-chatbot, python-mini-projects, python-project, python-projects, python-tutorial, trending-repositories, voice-recorder, python-ai-examples, python-chatbot-tutorial, simple-ai-projects, python-ml-examples

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 qxresearch/qxresearch-event-1
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Python Crash Course by Eric Matthes
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Python Crash Course by Eric Matthes · recommended 1×
  2. Automate the Boring Stuff with Python by Al Sweigart · recommended 1×
  3. Exercism.org · recommended 1×
  4. Real Python · recommended 1×
  5. Codecademy · recommended 1×
  • CATEGORY QUERY
    Where can I find small, practical Python project examples for quick learning?
    you: not recommended
    AI recommended (in order):
    1. Python Crash Course by Eric Matthes
    2. Automate the Boring Stuff with Python by Al Sweigart
    3. Exercism.org
    4. Real Python
    5. Codecademy
    6. GitHub

    AI recommended 6 alternatives but never named qxresearch/qxresearch-event-1. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for simple Python code examples to implement basic AI or chatbot features.
    you: not recommended
    AI recommended (in order):
    1. NLTK (Natural Language Toolkit)
    2. spaCy
    3. TextBlob
    4. Transformers (Hugging Face)
    5. Rasa

    AI recommended 5 alternatives but never named qxresearch/qxresearch-event-1. 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 qxresearch/qxresearch-event-1?
    pass
    AI did not name qxresearch/qxresearch-event-1 — likely talking about a different project

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

  • If a team adopts qxresearch/qxresearch-event-1 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named qxresearch/qxresearch-event-1 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 qxresearch/qxresearch-event-1 solve, and who is the primary audience?
    pass
    AI did not name qxresearch/qxresearch-event-1 — likely talking about a different project

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

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