Student Machines
We are a design and research lab building natural interfaces that make complex AI workflows simple and affordable for everyone. Our flagship project is a friendly offline AI assistant named Modu.
Building the OS for local AI
We’re building the interface layer for an on‑device future, where people interact with local AI through clear, intuitive design. Today, most local AI interfaces are built for developers first. We design and test new UX systems that make deploying local AI frictionless for everyday users, bundled with the right tools and models out of the box.
While local models are becoming leaner and more sophisticated, public awareness of them remains low. The truth is, their real accessibility hinges on how seamless the onboarding experience feels. Today, there’s still a significant amount of friction in environment setup: you have to choose between thousands of constantly shifting model variants and parameter sizes, wire up the right MCP servers or API keys, know how to pip-install and run headless models, and even then, most local AI apps still collapse everything into a bare chatbox or CLI.
On their own, local models still fall short of what people now expect from everyday AI tools. It is when you layer on web search, multimodality, effectively unlimited file context, and rich MCP connections that they become viable for real, day‑to‑day workflows.
With the right setup, the advantages of local AI are immediately clear: ownership and control, privacy by default on-device, offline availability, and effectively zero marginal cost. Cloud models aren’t going anywhere, but everyone benefits from having a private, offline assistant as a dependable fallback: on the road with spotty Wi‑Fi, while working with sensitive files you don’t want leaving your device, or anytime you want capable AI that stays affordable over time. Because the truth is, for 80~90% of general AI tasks like summarization, analysis, drafting, and research, local models are more than capable.
Our building blocks
The thesis is that this is largely a design challenge: local AI will only feel compelling when it’s pre-packaged as a complete, everyday product, built around real workflows and instant usability, without complex configuration or manual setup. A truly accessible local AI application must have the following characteristics:
Student Machine Lab is run by Joonseo.