RRepoGEO

REPOGEO REPORT · LITE

K-Dense-AI/karpathy

Default branch main · commit 501567e2 · scanned 5/20/2026, 3:13:12 PM

GitHub: 1,432 stars · 164 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 K-Dense-AI/karpathy, 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 the repo's identity and differentiate it from Andrej Karpathy's educational projects

    Why:

    CURRENT
    An agentic Machine Learning Engineer that trains state-of-the-art ML models using the Claude Agent SDK and Google ADK. This is a simple implementation demonstrating the power of Scientific Agent Skills for machine learning.
    COPY-PASTE FIX
    This `K-Dense-AI/karpathy` repository is an agentic Machine Learning Engineer, distinct from Andrej Karpathy's educational nanoGPT or micrograd projects. It trains state-of-the-art ML models using the Claude Agent SDK and Google ADK, demonstrating the power of Scientific Agent Skills for machine learning.
  • mediumreadme#2
    Add a sentence to the README's opening emphasizing end-to-end ML workflow automation

    Why:

    COPY-PASTE FIX
    It's specifically designed to automate complex, end-to-end machine learning engineering tasks, providing a robust framework for leveraging AI agents across your entire ML pipeline.
  • lowtopics#3
    Expand topics to include more specific ML engineering and workflow automation terms

    Why:

    CURRENT
    agentic-ai, automl, machine-learning
    COPY-PASTE FIX
    agentic-ai, automl, machine-learning, ml-engineering, workflow-automation, ai-agents, mlops-tools

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 K-Dense-AI/karpathy
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kubeflow Pipelines
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Kubeflow Pipelines · recommended 1×
  2. MLflow · recommended 1×
  3. Apache Airflow · recommended 1×
  4. Metaflow · recommended 1×
  5. Azure Machine Learning · recommended 1×
  • CATEGORY QUERY
    What are the best tools for automating end-to-end machine learning workflows?
    you: not recommended
    AI recommended (in order):
    1. Kubeflow Pipelines
    2. MLflow
    3. Apache Airflow
    4. Metaflow
    5. Azure Machine Learning
    6. Google Cloud Vertex AI
    7. AWS Step Functions
    8. Amazon SageMaker

    AI recommended 8 alternatives but never named K-Dense-AI/karpathy. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to leverage AI agents for automating complex machine learning engineering tasks?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. AutoGPT
    4. CrewAI
    5. Microsoft AutoGen
    6. Haystack
    7. OpenAI Assistants API

    AI recommended 7 alternatives but never named K-Dense-AI/karpathy. 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 K-Dense-AI/karpathy?
    pass
    AI named K-Dense-AI/karpathy explicitly

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

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

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

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K-Dense-AI/karpathy — RepoGEO report