LazyDesk AI: Breaking Big Tasks into Small Steps with Local AI | Hacktoberfest Week 3

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

LazyDesk AI is a Python and Streamlit application designed to make everyday tasks easier using local AI. I worked on adding a “Break It Down” mode that turns a large task into smaller, actionable steps.

The goal is to make overwhelming tasks feel easier to start, one small step at a time.

The feature is currently under development. I have not yet verified a working demo.

Original repository: https://github.com/prarthanas9917/lazydesk-ai

The project uses Python, Streamlit, and Ollama. I extended the task selection interface and added instructions intended to guide the local model to produce 3–7 manageable steps.

Testing and further improvements are still needed.

Open-source AI lets developers learn from existing projects, adapt tools to their needs, and experiment with models that can run locally. Local inference can help keep prompts on a user's own machine instead of sending them to a third-party AI provider.

The idea is simple: spend less time figuring out where to start and more time taking the first small step. The intended benefit is reducing the effort needed to organize a large task.

This is an early contribution. The new mode has been added to the code, but I still need to verify the feature, test the local model integration, and complete the contribution workflow.