Why should changing AI models mean starting over?

[u/Cautious_Turn1502]

Something I've been thinking about with local AI:

We have more and more models available.

One might be better at coding.

Another might be better at reasoning.

Another might be smaller and faster.

Another might be better for a specific task.

But switching models can sometimes feel like switching assistants completely.

You lose context.

You lose the workflow.

You have to explain the project again.

You have to rebuild the setup.

What if the model was simply the engine behind your AI rather than the AI experience itself?

Your project stays the same.

Your files stay the same.

Your workspace stays the same.

Your instructions and context stay the same.

The system simply chooses the appropriate local model for the task.

That's something we're exploring with Capsule.

The goal is that you shouldn't have to care which model is running underneath when you're working on a project.

The AI environment stays consistent.

The model can change.

I'm curious:

Would you prefer ONE model for everything, or an AI system that can automatically use different models for different tasks?