RRepoGEO

REPOGEO REPORT · LITE

runningcheese/Awesome-AI

Default branch main · commit 50e96559 · scanned 5/19/2026, 3:47:45 AM

GitHub: 2,810 stars · 217 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 runningcheese/Awesome-AI, 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    ["AI guide", "large language models", "ChatGPT", "OpenAI", "AI resources", "Chinese AI resources", "LLM guide", "AI learning", "AI tutorial"]
  • highlicense#2
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Add a LICENSE file (e.g., MIT or Apache-2.0) to the repository root.
  • highreadme#3
    Reposition the README's opening to clarify its role as a guide

    Why:

    CURRENT
    打造切实有用《人工智能指南》，AKA 你的《人工智能指南》，奶酪出品，开源免费，持续更新！
    COPY-PASTE FIX
    这是一个全面且实用的《人工智能指南》，由奶酪出品，开源免费，持续更新。它旨在帮助你理解、探索并有效利用各种AI工具和资源，特别是大型语言模型如ChatGPT，并提供详细的访问和使用策略。

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 runningcheese/Awesome-AI
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
OpenAI API
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. OpenAI API · recommended 2×
  2. Anthropic Claude API · recommended 1×
  3. Google Cloud Vertex AI · recommended 1×
  4. Hugging Face Transformers Library · recommended 1×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    What are the best ways to programmatically integrate large language models into applications?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Anthropic Claude API
    3. Google Cloud Vertex AI
    4. Hugging Face Transformers Library
    5. LangChain
    6. LlamaIndex
    7. Microsoft Azure OpenAI Service

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

    Show full AI answer
  • CATEGORY QUERY
    How can I find cost-effective methods or free trials for popular conversational AI services?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Google Cloud AI
    3. Microsoft Azure AI
    4. Hugging Face
    5. Anthropic
    6. Cohere
    7. IBM Watson Assistant

    AI recommended 7 alternatives but never named runningcheese/Awesome-AI. 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 runningcheese/Awesome-AI?
    pass
    AI did not name runningcheese/Awesome-AI — 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 runningcheese/Awesome-AI in production, what risks or prerequisites should they evaluate first?
    pass
    AI named runningcheese/Awesome-AI 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 runningcheese/Awesome-AI solve, and who is the primary audience?
    pass
    AI did not name runningcheese/Awesome-AI — 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

Drop this badge into the README of runningcheese/Awesome-AI. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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runningcheese/Awesome-AI — 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