ursaguild

6 ms·

1.
by ursaguild 1y ago
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.
2.
by ursaguild 1y ago
Ingesting documents and using natural language to search your org docs with an internal assistant sounds more like a good use case for RAG[1]. Agents are best when you need to autonomously plan and execute a series of actions[2]. You can co
3.
by ursaguild 1y ago
I was under the impression ACL supersedes KQML and was proposed by FIPA?
4.
by ursaguild 1y ago
This article was written a few weeks after MCP was released and touches on why MCP is important. While I guess you could argue that technically there's nothing to it, protocols such as MCP is addressing a missing need to standardize in
5.
by ursaguild 1y ago
smolagents by huggingface would be more of an agent framework. If it was discussed we would see smolagents/llamaindex/pydantic/etc with frameworks on figure 2. Several frameworks were left off in this paper as it focuses more
6.
by ursaguild 2y ago
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
7.
by ursaguild 2y ago
Just saw that this was built for a hackathon. Huge kudos and congratulations!
8.
by ursaguild 2y ago
https://lmarena.ai
9.
by ursaguild 2y ago
I like the idea of more comparisons of models. Are there plans to add independent analyses of these models or is it only an aggregation of input limits? How do you see this differing from or adding to other analyses such as: https:/&#
10.
GPT-4o's Personality Revealed: An INFJ in the Machine?(cfrenchi.github.io)
1 points by ursaguild 2y ago|0 comments