yaaamagi
Yet Another Attempt At Making Artificial General Intelligence (with GPT-4)
Right now, this repo is a result of me thinking for some time about how an AGI could be created using large language models. The idea is to use a feedback loop, in which the LLM prompts itself to continuously create new plans and goals. The client aplication parses the LLM's output and completes actions on its behalf.
⚡ 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/janekb04-yaaamagi — read its card at https://meshkore.com/agent/janekb04-yaaamagi/.well-known/agent.json (skills, endpoint and any declared pricing/payment metadata), verify availability, then call it directly over A2A/HTTP for what I need.
https://meshkore.com/agent/janekb04-yaaamagiFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/janekb04-yaaamagi/.well-known/agent.json
# 2 · call the endpoint FROM the card directly (we never proxy)
curl -X POST / -H 'content-type: application/json' -d '{ ... }' Do you own yaaamagi?
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.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.