RRepoGEO

REPOGEO REPORT · LITE

modal-labs/modal-examples

Default branch main · commit da4766da · scanned 5/25/2026, 7:23:17 AM

GitHub: 1,197 stars · 291 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 modal-labs/modal-examples, 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's opening to highlight core use cases

    Why:

    CURRENT
    This is a collection of examples for Modal. Use these examples to learn Modal and build your own robust and scalable applications.
    COPY-PASTE FIX
    This is a collection of examples for Modal, showcasing how to build robust and scalable Python applications for machine learning, distributed computing, and serverless GPU workloads. Use these examples to learn Modal and accelerate your cloud development.
  • mediumabout#2
    Clarify the repository description

    Why:

    CURRENT
    Examples of programs built using Modal
    COPY-PASTE FIX
    Scalable Python examples for Modal, demonstrating machine learning, distributed computing, and serverless GPU workloads.
  • lowreadme#3
    Add high-level descriptions for example categories

    Why:

    CURRENT
    - [**`01_getting_started/`**](01_getting_started) through [**`14_clusters/`**](14_clusters) provide a guided tour through Modal's concepts and capabilities.
    COPY-PASTE FIX
    - [**`01_getting_started/`**](01_getting_started) through [**`14_clusters/`**](14_clusters) provide a guided tour through Modal's concepts and capabilities, including examples for scalable machine learning, GPU-accelerated tasks, and distributed applications.

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 modal-labs/modal-examples
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
AWS Lambda
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. AWS Lambda · recommended 2×
  2. AWS Fargate · recommended 2×
  3. Google Cloud Run · recommended 2×
  4. RunPod Serverless · recommended 1×
  5. AWS SageMaker Serverless Inference · recommended 1×
  • CATEGORY QUERY
    How to run scalable Python machine learning workloads on serverless GPU infrastructure?
    you: not recommended
    AI recommended (in order):
    1. RunPod Serverless
    2. AWS Lambda
    3. AWS Fargate
    4. AWS SageMaker Serverless Inference
    5. Google Cloud Run
    6. Azure Container Apps
    7. Modal Labs
    8. Baseten
    9. Vast.ai
    10. FastAPI
    11. Flask

    AI recommended 11 alternatives but never named modal-labs/modal-examples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good options for deploying distributed Python applications to the cloud easily?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Run
    2. AWS Elastic Beanstalk
    3. Heroku
    4. Google Kubernetes Engine (GKE)
    5. Amazon Elastic Kubernetes Service (EKS)
    6. Azure Kubernetes Service (AKS)
    7. AWS Fargate
    8. Vercel
    9. AWS Lambda

    AI recommended 9 alternatives but never named modal-labs/modal-examples. 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 modal-labs/modal-examples?
    pass
    AI named modal-labs/modal-examples explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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