One of the most uncomfortable moments as a founder is realizing that you may have spent weeks or months building something in a way that is not quite right.
Not because the vision is wrong.
But because the path you took to reach it may not be the right one.
That is the kind of realization I recently had while building Mozart.
The first path made sense
Mozart started as a local-first desktop app to run AI agents on files and projects.
The first version naturally spoke to developers:
- Git
- worktrees
- branches
- local repositories
- coding agents
- CLI-based providers
And honestly, it made sense.
Developers are early adopters. They understand the technical concepts. They already feel the value of AI agents. They know why local-first matters. They understand why giving agents access to files and project context can be powerful.
But the more I thought about it, the more I realized something important.
The real opportunity is probably not only about developers.
The bigger problem
Most people who work behind a computer already feel that AI can help them.
But they are still stuck in a copy-paste loop:
- ChatGPT on one side
- files on another
- emails somewhere else
- documents in a folder
- business tools in different tabs
- workflows living only in their head
They do not need raw AI power only.
They need a way to turn that power into actual work.
That is where the product needs to evolve.
AI is the fuel, not the vehicle
I keep coming back to this metaphor:
AI models are becoming the new fuel.
Tokens are the oil.
But fuel alone does not take you anywhere.
You still need a car.
You need a cockpit.
You need an itinerary.
And in software, that itinerary is business logic:
- workflows
- templates
- skills
- tools
- context
- company-specific processes
The value is not only in the model.
The value is in the way you connect models, files, tools, workflows, and context to help someone go from point A to point B.
What I want Mozart to become
That is the direction Iām exploring with Mozart: a local-first AI workspace designed to make agents usable on real files, workflows, and business context.
Not just another tool for developers.
Not just another chat interface.
Not just another wrapper around an AI model.
The goal is to build a cockpit where people can use agents on their own files, with their own workflows, without needing to understand all the complexity underneath.
Maybe the first path was too technical.
But I still believe the direction is right.
I wrote more about this realization here:
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