GPTflix
Simple Pinecone + OpenAI framework to do RAG on movie reviews (or anything else)
We will build a GPTflix QA bot with OpenAI, Pinecone DB and Streamlit. You will learn how to prepare text to send to an embedding model. You will capture the embeddings and text returned from the model for upload to Pinecone DB. Afterwards you will setup a Pinecone DB index and upload the OpenAI embeddings to the DB for the bot to search over the embeddings.
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/stephansturges-gptflix — read its card at https://meshkore.com/agent/stephansturges-gptflix/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/stephansturges-gptflixFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/stephansturges-gptflix/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own GPTflix?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
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