RRepoGEO

REPOGEO REPORT · LITE

huggingface/ml-intern

Default branch main · commit d7637ba4 · scanned 5/8/2026, 5:12:48 AM

GitHub: 8,925 stars · 907 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 huggingface/ml-intern, 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 clarify it's an autonomous ML agent

    Why:

    CURRENT
    An ML intern that autonomously researches, writes, and ships good quality ML related code using the Hugging Face ecosystem — with deep access to docs, papers, datasets, and cloud compute.
    COPY-PASTE FIX
    ML Intern is an autonomous AI agent designed to function as an open-source ML engineer. It researches papers, trains models, and ships high-quality ML code using the Hugging Face ecosystem, with deep access to docs, datasets, and cloud compute.
  • hightopics#2
    Add specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    ai-agent, machine-learning, llm, autonomous-ai, huggingface, mlops, code-generation, research-assistant
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://smolagents-ml-intern.hf.space/

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 huggingface/ml-intern
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Databricks Lakehouse Platform
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Databricks Lakehouse Platform · recommended 1×
  2. Google Cloud Vertex AI · recommended 1×
  3. Amazon SageMaker · recommended 1×
  4. Azure Machine Learning · recommended 1×
  5. mlflow/mlflow · recommended 1×
  • CATEGORY QUERY
    How can I automate the entire machine learning model development and deployment workflow?
    you: not recommended
    AI recommended (in order):
    1. Databricks Lakehouse Platform
    2. Google Cloud Vertex AI
    3. Amazon SageMaker
    4. Azure Machine Learning
    5. MLflow (mlflow/mlflow)
    6. Kubeflow (kubeflow/kubeflow)
    7. Hugging Face Ecosystem
    8. 🤗 Transformers (huggingface/transformers)
    9. 🤗 Accelerate (huggingface/accelerate)

    AI recommended 9 alternatives but never named huggingface/ml-intern. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an AI assistant to research papers and generate code for machine learning projects.
    you: not recommended
    AI recommended (in order):
    1. ChatGPT
    2. GitHub Copilot
    3. Google Gemini
    4. Perplexity AI
    5. Anthropic Claude
    6. Wolfram Alpha

    AI recommended 6 alternatives but never named huggingface/ml-intern. 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 huggingface/ml-intern?
    pass
    AI named huggingface/ml-intern explicitly

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

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