RRepoGEO

REPOGEO REPORT · LITE

mckaywrigley/paul-graham-gpt

Default branch main · commit 795be559 · scanned 6/30/2026, 4:58:20 PM

GitHub: 2,663 stars · 375 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 mckaywrigley/paul-graham-gpt, 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 the repository

    Why:

    COPY-PASTE FIX
    rag, semantic-search, llm, gpt, openai, vector-database, pgvector, supabase, python, example, template, paul-graham
  • highreadme#2
    Reposition the README H1 and opening paragraph to highlight its RAG example utility

    Why:

    CURRENT
    # Paul Graham GPT

AI-powered search and chat for Paul Graham's essays.
    COPY-PASTE FIX
    # Paul Graham GPT: A RAG Example for Custom Documents

This repository demonstrates how to build an AI-powered search and chat system (Retrieval Augmented Generation, or RAG) on custom documents, using Paul Graham's essays as a practical, open-source example.
  • mediumreadme#3
    Add a 'Use Cases & Examples' section to the README

    Why:

    COPY-PASTE FIX
    ## Use Cases & Examples

This repository serves as a practical, open-source example for:

- Building Retrieval Augmented Generation (RAG) systems.
- Implementing semantic search and Q&A on custom data.
- Integrating OpenAI embeddings with a vector database like pgvector on Supabase.
- Developing AI-powered applications with a focus on specific knowledge domains.

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 mckaywrigley/paul-graham-gpt
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. OpenAI API · recommended 2×
  3. Pinecone · recommended 2×
  4. Chroma · recommended 2×
  5. Weaviate · recommended 2×
  • CATEGORY QUERY
    How can I build a conversational AI system for my own documents?
    you: not recommended
    AI recommended (in order):
    1. ChatGPT Plus
    2. OpenAI
    3. LangChain
    4. OpenAI API
    5. GPT-4
    6. GPT-3.5 Turbo
    7. Anthropic Claude
    8. Google Gemini
    9. Pinecone
    10. Chroma
    11. Weaviate
    12. Milvus
    13. Qdrant
    14. text-embedding-ada-002
    15. all-MiniLM-L6-v2
    16. LlamaIndex
    17. Hugging Face Transformers
    18. FAISS
    19. Llama 2
    20. Mistral
    21. Falcon
    22. Azure AI Search
    23. Azure Cognitive Search
    24. Azure OpenAI Service
    25. Google Cloud Vertex AI Search and Conversation
    26. Vertex AI Search
    27. Vertex AI Conversation
    28. Dialogflow CX/ES

    AI recommended 28 alternatives but never named mckaywrigley/paul-graham-gpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective ways to implement semantic search and Q&A on custom data?
    you: not recommended
    AI recommended (in order):
    1. OpenAI API
    2. Hugging Face Transformers
    3. FAISS
    4. Pinecone
    5. Weaviate
    6. LangChain
    7. LlamaIndex
    8. Chroma
    9. Qdrant
    10. Elasticsearch

    AI recommended 10 alternatives but never named mckaywrigley/paul-graham-gpt. 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 mckaywrigley/paul-graham-gpt?
    pass
    AI did not name mckaywrigley/paul-graham-gpt — 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 mckaywrigley/paul-graham-gpt in production, what risks or prerequisites should they evaluate first?
    pass
    AI did not name mckaywrigley/paul-graham-gpt — 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?

  • In one sentence, what problem does the repo mckaywrigley/paul-graham-gpt solve, and who is the primary audience?
    pass
    AI named mckaywrigley/paul-graham-gpt explicitly

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

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mckaywrigley/paul-graham-gpt — 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