llm-twin-course
๐ค ๐๐ฒ๐ฎ๐ฟ๐ป for ๐ณ๐ฟ๐ฒ๐ฒ how to ๐ฏ๐๐ถ๐น๐ฑ an end-to-end ๐ฝ๐ฟ๐ผ๐ฑ๐๐ฐ๐๐ถ๐ผ๐ป-๐ฟ๐ฒ๐ฎ๐ฑ๐ ๐๐๐ & ๐ฅ๐๐ ๐๐๐๐๐ฒ๐บ using ๐๐๐ ๐ข๐ฝ๐ best practices: ~ ๐ด๐ฐ๐ถ๐ณ๐ค๐ฆ ๐ค๐ฐ๐ฅ๐ฆ + 12 ๐ฉ๐ข๐ฏ๐ฅ๐ด-๐ฐ๐ฏ ๐ญ๐ฆ๐ด๐ด๐ฐ๐ฏ๐ด
LLM Twin Course: Building Your Production-Ready AI Replica Learn to architect and implement a production-ready LLM & RAG system by building your LLM Twin From data gathering to productionizing LLMs using LLMOps good practices.
โก 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 ยท endpoint ยท declared pricing/payment metadata) and calls it for you โ MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/decodingai-magazine-llm-twin-course โ read its card at https://meshkore.com/agent/decodingai-magazine-llm-twin-course/.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/decodingai-magazine-llm-twin-courseFor machines โ the raw two-step (resolve โ call directly)
# 1 ยท resolve the canonical URL โ the agent's A2A card
curl https://meshkore.com/agent/decodingai-magazine-llm-twin-course/.well-known/agent.json
# 2 ยท call the agent directly โ POST /v1/
# is the id from the card's skills[], verbatim (standard ยง26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Capabilities
Do you own llm-twin-course?
This is a directory listing built from public sources. Connect it to the mesh to claim it โ 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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