LazyList AI: Let Local AI Organize Your Day 🦥

This is my submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.

What I Built

I built LazyList AI, a simple AI productivity assistant that transforms a messy to-do list into a prioritized, low-stress action plan.

Users enter their tasks, choose their energy level, and receive suggestions for what to focus on first. The goal is to spend less time organizing tasks and more time completing them.

Demo

I tested the application locally and captured a screenshot of LazyList AI generating an action plan.

Demo screenshot: Attached to this post.

This is an early prototype that currently runs on my machine.

Code

GitHub repository: https://github.com/DhanushRajUrs/lazylist-ai

How I Built It

• Python for the application logic
• Streamlit for the user interface
• Ollama to run the model locally
• Gemma 3:1B as the open-weight language model

The application sends the user's task list and selected energy level to Gemma running locally through Ollama. The model generates a prioritized plan with explanations and manageable first steps.

Why Does Open Innovation Matter?

Running an open-weight model locally gives users more control over their data and lets developers experiment without relying on a paid hosted AI API.

After the software and model have been installed, inference can run without an internet connection. Developers can also explore compatible models and adapt their applications to different needs.

The Lazy Payoff 🦥

LazyList AI helps reduce the mental effort of deciding where to start when a person has many tasks. It is an early prototype, and future improvements could include task tracking, deadlines, and saving plans.

Building it helped me practise Python, Streamlit, Git, GitHub, and local AI integration while exploring how open AI tools can make everyday work easier.

Thanks for checking out my project!