RRepoGEO

REPOGEO REPORT · LITE

ilsilfverskiold/Awesome-LLM-Resources-List

Default branch main · commit 99c2ec8d · scanned 5/31/2026, 8:58:41 PM

GitHub: 526 stars · 90 forks

AI VISIBILITY SCORE
28 /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
2 / 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 ilsilfverskiold/Awesome-LLM-Resources-List, 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 the README's opening to clarify its purpose as a resource list

    Why:

    CURRENT
    A Curated Collection of LLM resources. 💡✨
    COPY-PASTE FIX
    This repository is a curated collection of resources specifically for applied AI engineering with Large Language Models (LLMs). It aims to provide a comprehensive list of tools, platforms, papers, and best practices across the LLM ecosystem.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/ilsilfverskiold/Awesome-LLM-Resources-List
  • mediumlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file to the repository root, e.g., with the MIT License text.

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 ilsilfverskiold/Awesome-LLM-Resources-List
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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. AWS Lambda · recommended 1×
  2. AWS Inferentia · recommended 1×
  3. AWS Trainium · recommended 1×
  4. NVIDIA GPUs · recommended 1×
  5. EC2 instances · recommended 1×
  • CATEGORY QUERY
    What are the best serverless platforms for deploying private large language models efficiently?
    you: not recommended
    AI recommended (in order):
    1. AWS Lambda
    2. AWS Inferentia
    3. AWS Trainium
    4. NVIDIA GPUs
    5. EC2 instances
    6. Google Cloud Run
    7. Azure Container Apps
    8. Modal Labs
    9. Vercel
    10. RunPod Serverless
    11. Banana.dev
    12. Replicate

    AI recommended 12 alternatives but never named ilsilfverskiold/Awesome-LLM-Resources-List. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find a comprehensive list of resources for applied AI engineering with LLMs?
    you: not recommended
    AI recommended (in order):
    1. Awesome-LLM Engineering
    2. Hugging Face Transformers
    3. LangChain
    4. OpenAI API
    5. DeepLearning.AI
    6. Weights & Biases
    7. Microsoft Azure AI

    AI recommended 7 alternatives but never named ilsilfverskiold/Awesome-LLM-Resources-List. 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 ilsilfverskiold/Awesome-LLM-Resources-List?
    pass
    AI named ilsilfverskiold/Awesome-LLM-Resources-List explicitly

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

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

Embed your GEO score

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