u/Full_Win_8680

Most work around Local LLMs focuses on helping users find information:

• RAG over documents
• Personal knowledge bases
• Semantic search
• Agent workflows
• Tool use

But I've been wondering about a different problem.

Imagine two people are:

• Reading the same documentation
• Exploring the same GitHub repository
• Researching the same model
• Working through the same technical problem

A local model could potentially understand the context of what a user is doing and identify others with overlapping interests or goals.

Instead of only answering questions, the model could help users discover relevant people.

Something like:

"Five other people have been researching this project recently."

or

"Three users are exploring the same inference optimization techniques."

Obviously there are major privacy and consent challenges, but I'm curious whether anyone here sees value in this concept.

Questions:

• Would human discovery be a useful capability for Local LLMs?
• Could embeddings be used to match interests or context while preserving privacy?
• Are there existing projects exploring this idea?
• Does this sound genuinely useful or just like another layer of noise?

Interested to hear thoughts from people building local-first AI systems and agents.

I've been thinking about a problem that feels increasingly relevant as AI agents become more capable.

Today, when humans browse the web, they're mostly isolated. Thousands of people might be reading the same documentation, research paper, GitHub repository, or article, but they don't know each other exist.

At the same time, AI agents are becoming better at understanding context, interests, and intent.

This made me wonder:

In the future, should AI agents help connect people who are working on similar problems or exploring the same topics?

For example:

• Someone reading Kubernetes documentation.
• Someone researching RAG architectures.
• Someone exploring a specific open-source project.
• Someone investigating the same AI paper.

An agent could identify shared context and facilitate introductions, discussions, or topic-based groups.

The challenge is balancing usefulness with privacy and avoiding spam.

I'm curious what people here think:

• Would agent-assisted discovery of relevant people be valuable?
• Should agents proactively suggest connections?
• What privacy boundaries would need to exist?
• Could this become a meaningful use case for AI agents beyond productivity automation?

Interested in hearing different perspectives from builders and researchers working in the AI agent space.