RRepoGEO

REPOGEO REPORT · LITE

daytonaio/ai-enablement-stack

Default branch main · commit d3db9fa3 · scanned 6/11/2026, 9:08:22 AM

GitHub: 631 stars · 117 forks

AI VISIBILITY SCORE
28 /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
2 / 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 daytonaio/ai-enablement-stack, 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 README's opening to emphasize "mapping/guide"

    Why:

    CURRENT
    <h3 align="center">
  The comprehensive guide to tools and technologies powering modern AI development
</h3>
    COPY-PASTE FIX
    <h3 align="center">
  A Community-Driven Mapping & Comprehensive Guide to Tools and Technologies Powering Modern AI Development
</h3>
  • hightopics#2
    Add relevant topics to the repository

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    ai-development, mlops, ai-tools, ai-ecosystem, technology-mapping, developer-tools, ai-guide, curated-list
  • mediumhomepage#3
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://daytona.io

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 daytonaio/ai-enablement-stack
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. Julia · recommended 1×
  3. R · recommended 1×
  4. PyTorch · recommended 1×
  5. TensorFlow · recommended 1×
  • CATEGORY QUERY
    What are the essential tools and technologies for building modern AI applications?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. Julia
    3. R
    4. PyTorch
    5. TensorFlow
    6. scikit-learn
    7. JAX
    8. NumPy
    9. Pandas
    10. Matplotlib
    11. Seaborn
    12. Amazon Web Services (AWS)
    13. Amazon SageMaker
    14. AWS Lambda
    15. EC2 instances
    16. Google Cloud Platform (GCP)
    17. Google Cloud AI Platform
    18. TensorFlow Extended (TFX)
    19. TPUs
    20. Microsoft Azure
    21. Azure Machine Learning
    22. Azure Databricks
    23. Cognitive Services
    24. MLflow
    25. Hugging Face Transformers
    26. OpenCV
    27. Streamlit
    28. Docker

    AI recommended 28 alternatives but never named daytonaio/ai-enablement-stack. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a structured overview of AI development tools by category?
    you: not recommended
    AI recommended (in order):
    1. Gartner Hype Cycle for Artificial Intelligence
    2. AI Landscape by Sequoia Capital
    3. Papers With Code
    4. GitHub
    5. Kaggle
    6. Towards Data Science
    7. TechCrunch
    8. The Information

    AI recommended 8 alternatives but never named daytonaio/ai-enablement-stack. 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 daytonaio/ai-enablement-stack?
    pass
    AI named daytonaio/ai-enablement-stack explicitly

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

  • If a team adopts daytonaio/ai-enablement-stack in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name daytonaio/ai-enablement-stack — 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?

  • In one sentence, what problem does the repo daytonaio/ai-enablement-stack solve, and who is the primary audience?
    pass
    AI named daytonaio/ai-enablement-stack explicitly

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

Embed your GEO score

Drop this badge into the README of daytonaio/ai-enablement-stack. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/daytonaio/ai-enablement-stack.svg)](https://repogeo.com/en/r/daytonaio/ai-enablement-stack)
HTML
<a href="https://repogeo.com/en/r/daytonaio/ai-enablement-stack"><img src="https://repogeo.com/badge/daytonaio/ai-enablement-stack.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

daytonaio/ai-enablement-stack — 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