RRepoGEO

REPOGEO REPORT · LITE

underlines/awesome-ml

Default branch master · commit 81c7bdf6 · scanned 5/23/2026, 6:33:10 AM

GitHub: 2,672 stars · 237 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
22 /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
1 / 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 underlines/awesome-ml, 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
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    The README excerpt shows the H1 followed by a link and "Contributions welcome."
    COPY-PASTE FIX
    Add the following sentence immediately after the `<h1>Awesome ML</h1>` or the `awesome.re` link: "This is a curated list of awesome resources, tools, and frameworks for Large Language Models (LLMs), analytics, and data science."
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    awesome-list, llm, large-language-models, machine-learning, datascience, analytics, generative-ai, rag, ml-tools, ml-resources
  • lowhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/underlines/awesome-ml

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 underlines/awesome-ml
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Hugging Face Transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Hugging Face Transformers · recommended 2×
  2. LangChain · recommended 2×
  3. PyTorch · recommended 1×
  4. TensorFlow · recommended 1×
  5. Llama.cpp · recommended 1×
  • CATEGORY QUERY
    Looking for a comprehensive list of open-source large language model development tools and resources.
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. PyTorch
    3. TensorFlow
    4. LangChain
    5. Llama.cpp
    6. Ollama
    7. DeepSpeed
    8. bitsandbytes
    9. LoRA
    10. MLflow

    AI recommended 10 alternatives but never named underlines/awesome-ml. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best frameworks and libraries for building retrieval augmented generation systems?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Hugging Face Transformers
    5. Hugging Face Datasets
    6. Faiss
    7. Weaviate
    8. Pinecone
    9. Qdrant
    10. Chroma
    11. Milvus
    12. Elasticsearch

    AI recommended 12 alternatives but never named underlines/awesome-ml. 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 underlines/awesome-ml?
    pass
    AI did not name underlines/awesome-ml — 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?

  • If a team adopts underlines/awesome-ml in production, what risks or prerequisites should they evaluate first?
    pass
    AI named underlines/awesome-ml 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 underlines/awesome-ml solve, and who is the primary audience?
    pass
    AI did not name underlines/awesome-ml — 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?

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underlines/awesome-ml — 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