RRepoGEO

REPOGEO REPORT · LITE

mckaywrigley/paul-graham-gpt

Default branch main · commit 795be559 · scanned 5/19/2026, 8:53:41 AM

GitHub: 2,666 stars · 377 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 specific topics to improve categorization

    Why:

    COPY-PASTE FIX
    rag, llm-application, semantic-search, gpt, openai, supabase, pgvector, paul-graham, ai-assistant, knowledge-base
  • highreadme#2
    Reposition README H1 to clarify its role as an application example

    Why:

    CURRENT
    # Paul Graham GPT

AI-powered search and chat for Paul Graham's essays.
    COPY-PASTE FIX
    # Paul Graham GPT

An open-source, full-stack example of an AI-powered search and chat application built on Paul Graham's essays, demonstrating Retrieval Augmented Generation (RAG) with OpenAI embeddings and Supabase pgvector.
  • mediumreadme#3
    Add a 'Who is this for?' or 'Why use this?' section to the README

    Why:

    COPY-PASTE FIX
    ## Who is this for?

This repository is ideal for developers, researchers, and entrepreneurs looking for a practical, fully open-source blueprint to implement Retrieval Augmented Generation (RAG) systems over custom datasets. It provides a complete, working solution from data preparation and embedding to a functional search and chat interface, serving as a robust starting point for your own AI-powered knowledge base projects.

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
Weaviate
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Weaviate · recommended 2×
  2. Pinecone · recommended 2×
  3. Qdrant · recommended 2×
  4. Chroma · recommended 2×
  5. LangChain · recommended 1×
  • CATEGORY QUERY
    How can I build an AI-powered conversational agent over my own collection of documents?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset/Haystack)
    4. OpenAI API
    5. Weaviate
    6. Pinecone
    7. Qdrant
    8. Chroma
    9. Gradio
    10. Streamlit

    AI recommended 10 alternatives but never named mckaywrigley/paul-graham-gpt. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for implementing semantic search on a custom dataset using embeddings?
    you: not recommended
    AI recommended (in order):
    1. OpenAI Embeddings
    2. Hugging Face Transformers
    3. Sentence Transformers
    4. Cohere Embeddings
    5. Pinecone
    6. Weaviate
    7. Qdrant
    8. Faiss
    9. Chroma
    10. Elasticsearch
    11. OpenSearch

    AI recommended 11 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 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?

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

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mckaywrigley/paul-graham-gpt — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
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mckaywrigley/paul-graham-gpt — RepoGEO report