ursaguild

This is really cool to see. Being able to play the token generation was awesome. Amazing job with breaking down how to think about these models. This made the idea of Jev/decision models really easy to grasp for me. The idea of calibrating the model was helpful. I thought this was a great overview.

Especially with a client, consider the word choices around "learning". When using llms, agents, or rag, the system isn't learning (yet) but making a decision based on the context you provide. Most models are a fixed snapshot. If you provide up to date information, it will be able to give you an output based on that.

"Learning" happens when initially training the llm or arguably when fine-tuning. Neither of which are needed for your use case as presented.

I was under the impression ACL supersedes KQML and was proposed by FIPA?

The real benefit I see from mcp is that we are now writing programs for users and ai assistants/agents.

By writing mcp servers for our services/apps we are allowing a standardized way for ai assistants to integrate with tools and services across apps.

Just saw that this was built for a hackathon. Huge kudos and congratulations!