LazyDesk: Building a Local AI Task Organizer with Ollama

Have you ever had a long list of tasks but struggled to decide what to do first?

I built LazyDesk, a simple local AI-powered task organizer, to explore how an open-weight AI model can turn raw tasks into a more structured and actionable plan.

LazyDesk is a web application that accepts a list of tasks and uses a locally running AI model to organize them. It also includes an interactive checklist so users can tick off completed tasks and track their progress.

I wanted to experiment with running an AI model locally instead of depending on an external AI API. This made the project a useful way to learn about API integration, backend development and local inference.

The AI processing runs through Ollama on the user's machine. The generated priorities and time estimates are suggestions, so users should review them rather than treat them as exact.

Building LazyDesk helped me practise connecting a frontend to a backend, sending API requests, handling errors, integrating a local AI model and updating the user interface dynamically.

I also learned that adding an AI model is only one part of an application. The surrounding interface and workflow are what make the model useful to people.

Currently, checklist progress resets when the page is refreshed. In the future, I would like to add persistent task storage, editable tasks, due dates and better progress tracking.

GitHub repository: https://github.com/Abhishek-gitty/LazyDesk

To run it locally, install Node.js and Ollama, download the qwen2.5:3b model, run npm install, and start the application with node server.js.

This is an ongoing learning project, and I look forward to improving it further.