RRepoGEO

REPOGEO REPORT · LITE

runningcheese/Awesome-AI

Default branch main · commit 50e96559 · scanned 6/30/2026, 11:13:29 AM

GitHub: 2,816 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
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 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
  • highreadme#1
    Refine README's introductory sentence to clarify repo's specific focus

    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
    打造切实有用《人工智能指南》，AKA 你的《人工智能指南》，**专注于解决AI服务访问难题、提供API使用教程，并精选中文AI资源**，奶酪出品，开源免费，持续更新！
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    artificial-intelligence, ai-guide, chatgpt, openai, api-keys, ai-access, chinese-resources, llm, ai-workarounds
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root and choose an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) for your project.

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 API · recommended 2×
  3. NordVPN · recommended 1×
  4. ExpressVPN · recommended 1×
  5. Surfshark · recommended 1×
  • CATEGORY QUERY
    Struggling to access popular AI chatbot services; what are the best workarounds?
    you: not recommended
    AI recommended (in order):
    1. NordVPN
    2. ExpressVPN
    3. Surfshark
    4. Proton VPN
    5. Google Gemini
    6. Microsoft Copilot
    7. Claude by Anthropic
    8. Perplexity AI
    9. Poe by Quora
    10. ChatGPT
    11. OpenAI API
    12. Google AI Studio
    13. Anthropic API
    14. ChatGPT Desktop App
    15. Microsoft Copilot App
    16. OpenAI Status Page
    17. Google Cloud Status Dashboard

    AI recommended 17 alternatives but never named runningcheese/Awesome-AI. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for flexible ways to integrate large language models into custom applications.
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. LangChain
    3. Hugging Face Transformers Library
    4. Google Cloud Vertex AI
    5. Anthropic API
    6. LlamaIndex

    AI recommended 6 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 named runningcheese/Awesome-AI explicitly

    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?

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MARKDOWN (README)
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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