I've spent some time building an AI desktop application because I wanted a workflow that didn't depend on a single model or ecosystem.
Like many people, I tried a number of AI tools and IDEs. They all had strengths, but I found myself constantly switching between applications depending on whether I wanted to use a local model, an API model, or another provider.
I wanted one application that stayed out of the way and let me choose the best model for the task.
That became More AI.
The project is built around a model-agnostic architecture that supports local models, API providers, and CLI-connected models without changing the overall workflow.
Some of the features include:
Multi-model chat
Integrated AI IDE
AI orchestration and agent workflows
Voice assistant
Image, music generation, and video editing
Governance, audit trails, and execution history
Local-first with optional cloud providers
One area I spent a lot of time on was governance. As AI starts writing code, making changes, and coordinating workflows, I wanted those actions to be visible and traceable instead of feeling like a black box.
The project is completely open source under the MIT License.
I'm interested in feedback from people using or building AI tooling, especially around the architecture, orchestration approach, and model abstraction layer. What has been your go to? do you have a daily that you are in then one you use every once in a while? Intrested in hearing your perspective.
I've spent some time building an AI desktop application because I wanted a workflow that didn't depend on a single model or ecosystem.
Like many people, I tried a number of AI tools and IDEs. They all had strengths, but I found myself constantly switching between applications depending on whether I wanted to use a local model, an API model, or another provider.
I wanted one application that stayed out of the way and let me choose the best model for the task.
That became More AI.
The project is built around a model-agnostic architecture that supports local models, API providers, and CLI-connected models without changing the overall workflow.
Some of the features include:
Multi-model chat Integrated AI IDE AI orchestration and agent workflows Voice assistant Image, music generation, and video editing Governance, audit trails, and execution history Local-first with optional cloud providers
One area I spent a lot of time on was governance. As AI starts writing code, making changes, and coordinating workflows, I wanted those actions to be visible and traceable instead of feeling like a black box.
The project is completely open source under the MIT License.
I'm interested in feedback from people using or building AI tooling, especially around the architecture, orchestration approach, and model abstraction layer. What has been your go to? do you have a daily that you are in then one you use every once in a while? Intrested in hearing your perspective.