RRepoGEO

REPOGEO REPORT · LITE

ArronAI007/Awesome-AGI

Default branch main · commit 5dd83324 · scanned 6/15/2026, 2:28:29 PM

GitHub: 503 stars · 54 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 ArronAI007/Awesome-AGI, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    awesome-list, agi, llm, aigc, large-language-models, generative-ai, machine-learning-resources, deep-learning, ai-resources, prompt-engineering, rlhf
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file (e.g., MIT or Apache-2.0) in the root of the repository.
  • mediumabout#3
    Refine the repository description

    Why:

    CURRENT
    AGI资料汇总学习（主要包括LLM和AIGC），持续更新......
    COPY-PASTE FIX
    A comprehensive, continuously updated curated list of resources for Artificial General Intelligence (AGI), Large Language Models (LLM), and AI-Generated Content (AIGC) learning and development.

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 ArronAI007/Awesome-AGI
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
huggingface/transformers
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. huggingface/transformers · recommended 2×
  2. DeepLearning.AI · recommended 1×
  3. OpenAI API · recommended 1×
  4. Stanford CS224N · recommended 1×
  5. Natural Language Processing with Transformers · recommended 1×
  • CATEGORY QUERY
    Where can I find comprehensive resources to learn about large language model development?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. DeepLearning.AI
    3. OpenAI API
    4. Stanford CS224N
    5. Natural Language Processing with Transformers
    6. Google AI Blog
    7. Weights & Biases (wandb/wandb)

    AI recommended 7 alternatives but never named ArronAI007/Awesome-AGI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for fine-tuning and deploying large language models?
    you: not recommended
    AI recommended (in order):
    1. LoRA (Low-Rank Adaptation)
    2. QLoRA (Quantized LoRA)
    3. Alpaca-GPT4
    4. Proximal Policy Optimization (PPO)
    5. Hugging Face's TRL (Transformer Reinforcement Learning) (huggingface/trl)
    6. Hugging Face Transformers (huggingface/transformers)
    7. vLLM (vllm-project/vllm)
    8. TGI (Text Generation Inference) (huggingface/text-generation-inference)
    9. Kubernetes
    10. NGINX Ingress
    11. Traefik
    12. Google Kubernetes Engine (GKE)
    13. Amazon Elastic Kubernetes Service (EKS)
    14. Azure Kubernetes Service (AKS)
    15. ONNX Runtime (microsoft/onnxruntime)
    16. Core ML
    17. Hugging Face Optimum (huggingface/optimum)
    18. AWS Lambda
    19. Google Cloud Functions
    20. RunPod
    21. Replicate

    AI recommended 21 alternatives but never named ArronAI007/Awesome-AGI. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 ArronAI007/Awesome-AGI?
    pass
    AI did not name ArronAI007/Awesome-AGI — 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 ArronAI007/Awesome-AGI in production, what risks or prerequisites should they evaluate first?
    pass
    AI named ArronAI007/Awesome-AGI 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 ArronAI007/Awesome-AGI solve, and who is the primary audience?
    pass
    AI did not name ArronAI007/Awesome-AGI — 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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  • Brand-free category queries5 vs 2 in Lite
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