RRepoGEO

REPOGEO REPORT · LITE

CerebriumAI/examples

Default branch master · commit 1a336fa8 · scanned 6/7/2026, 11:52:47 PM

GitHub: 522 stars · 77 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 CerebriumAI/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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README opening to explicitly link examples to Cerebrium's platform capabilities

    Why:

    CURRENT
    Welcome to Cerebrium's official examples repository! This collection of examples helps you get started with building Machine Learning / AI applications on the platform. Whether you're looking to deploy LLMs, process voice data, or handle image and video tasks, you'll find practical, ready-to-use examples here.
    COPY-PASTE FIX
    Welcome to Cerebrium's official examples repository, your practical guide to deploying AI models and LLMs on Cerebrium's scalable serverless GPU infrastructure. This collection provides ready-to-use examples for building Machine Learning / AI applications directly on our platform, covering everything from LLM deployment to image and video tasks.
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0) that aligns with Cerebrium's distribution policies.

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 CerebriumAI/examples
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
RunPod Serverless
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. RunPod Serverless · recommended 2×
  2. AWS SageMaker Serverless Inference · recommended 1×
  3. Google Cloud Vertex AI Prediction (Serverless Endpoints) · recommended 1×
  4. modal-labs/modal-client · recommended 1×
  5. basetenlabs/baseten · recommended 1×
  • CATEGORY QUERY
    How to deploy AI models and LLMs on scalable serverless GPU infrastructure?
    you: not recommended
    AI recommended (in order):
    1. RunPod Serverless
    2. AWS SageMaker Serverless Inference
    3. Google Cloud Vertex AI Prediction (Serverless Endpoints)
    4. Modal Labs (modal-labs/modal-client)
    5. Baseten (basetenlabs/baseten)
    6. Replicate (replicate/replicate)
    7. Lambda Labs (Lambda Cloud Serverless)

    AI recommended 7 alternatives but never named CerebriumAI/examples. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking practical examples for building machine learning applications with serverless functions on GPUs.
    you: not recommended
    AI recommended (in order):
    1. AWS Lambda
    2. AWS SageMaker Endpoints
    3. EC2 instances
    4. Google Cloud Functions
    5. Google Cloud AI Platform Prediction
    6. GKE
    7. Compute Engine
    8. Azure Functions
    9. Azure Machine Learning Endpoints
    10. Azure Container Instances
    11. Azure VMs
    12. Knative (knative/serving)
    13. NVIDIA Triton Inference Server (triton-inference-server/server)
    14. Kubernetes (kubernetes/kubernetes)
    15. EKS
    16. AKS
    17. Modal Labs (modal-labs/modal)
    18. RunPod Serverless
    19. Baseten

    AI recommended 19 alternatives but never named CerebriumAI/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
    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 CerebriumAI/examples?
    pass
    AI named CerebriumAI/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 CerebriumAI/examples in production, what risks or prerequisites should they evaluate first?
    pass
    AI named CerebriumAI/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 CerebriumAI/examples solve, and who is the primary audience?
    pass
    AI named CerebriumAI/examples explicitly

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

Embed your GEO score

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MARKDOWN (README)
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CerebriumAI/examples — RepoGEO report