RRepoGEO

REPOGEO REPORT · LITE

polterguy/magic

Default branch master · commit c7655c83 · scanned 5/25/2026, 11:27:22 AM

GitHub: 1,143 stars · 166 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 polterguy/magic, 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 opening and GitHub 'About' description for clarity

    Why:

    CURRENT
    GitHub 'About' description: "Fully Autonomous AI-based Software Development Assistant"
    README H1 + first sentence:
    ```
    # Magic Cloud - Fully Autonomous AI-based Software Development Assistant
    
    > Magic is an open-source, self-hostable AI software development platform that generates and runs full-stack business applications and AI agents from natural language.
    ```
    COPY-PASTE FIX
    GitHub 'About' description: "AI platform that autonomously generates and runs full-stack business applications and AI agents from natural language prompts."
    README H1 + first sentence:
    ```
    # Magic Cloud: AI Platform for Autonomous Full-Stack App Generation from Natural Language
    
    > Magic is an open-source, self-hostable AI platform that autonomously generates and runs complete full-stack business applications and AI agents directly from natural language prompts, without traditional coding.
    ```
  • mediumtopics#2
    Add more specific topics to improve categorization

    Why:

    CURRENT
    ai, automation-framework, low-code, no-code, openai, vibe-coding
    COPY-PASTE FIX
    ai, automation-framework, low-code, no-code, openai, vibe-coding, ai-code-generation, full-stack-ai, natural-language-programming, ai-agents, autonomous-development
  • lowcomparison#3
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, e.g., `## How Magic Compares` or `## Why Magic is Different`, that explicitly contrasts it with traditional web frameworks (like Django, FastAPI, Ruby on Rails) and low-code/no-code UI builders (like Gradio, Streamlit, Appsmith), emphasizing its autonomous AI generation capabilities. For example: "Unlike traditional web frameworks (e.g., Django, FastAPI, Ruby on Rails) or UI-focused low-code tools (e.g., Gradio, Streamlit), Magic is an AI platform designed to autonomously generate and deploy entire full-stack applications from natural language, rather than requiring manual coding or component assembly."

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 polterguy/magic
Avg rank
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Python
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Python · recommended 1×
  2. FastAPI · recommended 1×
  3. Django · recommended 1×
  4. Flask · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How to build full-stack business applications using AI and natural language on my own server?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. FastAPI
    3. Django
    4. Flask
    5. Hugging Face Transformers
    6. PyTorch
    7. TensorFlow
    8. LangChain
    9. LlamaIndex
    10. PostgreSQL
    11. MongoDB
    12. React
    13. Vue.js
    14. Node.js
    15. Express.js
    16. NestJS
    17. Transformers.js
    18. Java
    19. Spring Boot
    20. Deeplearning4j
    21. ONNX Runtime
    22. Angular
    23. C#
    24. .NET
    25. ASP.NET Core
    26. ML.NET
    27. SQL Server
    28. Blazor

    AI recommended 28 alternatives but never named polterguy/magic. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What open-source platforms generate full-stack applications from natural language prompts without much coding?
    you: not recommended
    AI recommended (in order):
    1. Gradio
    2. Streamlit
    3. Appsmith
    4. Budibase
    5. ToolJet
    6. OpenAI's GPT-series
    7. Auto-GPT
    8. BabyAGI
    9. Lobe

    AI recommended 9 alternatives but never named polterguy/magic. 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 polterguy/magic?
    pass
    AI named polterguy/magic explicitly

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

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