SlothMode：減少壓力，將任務拆成更小步驟

🦥 SlothMode: Less Overwhelm, Smaller Steps — AI-Powered Planning at a Human Pace

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

What I Built

Ever spend so much time planning your tasks that you have no energy left to actually do them?

I built SlothMode, a gentle, AI-assisted task planner designed to make productivity feel less overwhelming. It is especially useful for students and people who struggle with executive dysfunction or planning anxiety.

Instead of dumping a huge to-do list into a rigid calendar, SlothMode helps turn tasks into manageable steps and creates a schedule that leaves room for breaks and everyday routines.

🦥 What makes it different?

• Personalized onboarding: Set up sleep, meals, college or work hours, and other routine preferences.
• Local AI assistance: Gemma 3 1B helps classify tasks, estimate durations, assess urgency, and suggest smaller subtasks.
• Gentle scheduling: A deterministic scheduler organizes tasks into focus sessions and breaks while respecting deadlines and protected routine blocks.
• Flexible planning: Switch between day, week, and month views, edit tasks, track subtasks, and reschedule overdue work.
• Graceful fallback: If Ollama or the model is unavailable, rule-based estimation helps the app remain usable.

The goal isn't to squeeze more work into every minute. It's to make starting feel easier.

Demo

Click here for video and screenshots

Screenshots and a walkthrough are also available in the repository.

Code

GitHub repository: https://github.com/gargijoshi9/SlothMode

Built with React, TypeScript, Vite, Express, Ollama, and Gemma 3 1B.

Check out the repository for the source code, setup instructions, and implementation details.

How I Built It

SlothMode combines local AI assistance with predictable scheduling logic.

The frontend is built with React and TypeScript, with Vite for development and building. An Express backend exposes endpoints for task estimation, classification, and planning.

For AI-assisted task understanding, I integrated Gemma 3 1B through Ollama. The model can help estimate how long a task might take, identify its category and urgency, and suggest smaller steps.

But I deliberately kept the actual scheduling deterministic.

The model helps interpret the task; the scheduler decides how to fit work into the day. It organizes focus sessions and breaks around deadlines and protected routine blocks rather than asking a language model to invent calendar timestamps.

I also added validation and rule-based fallbacks for unavailable models or invalid responses. This matters because a productivity app should not become unusable just because its AI dependency is temporarily unavailable.

One of the most important parts was connecting personalized onboarding to actual scheduling behavior. Routine preferences should influence the plan, not merely make the onboarding screen look personalized.

Why Does Open Innovation Matter?

Open innovation made it possible to experiment with AI-assisted planning without building the entire experience around a closed, hosted model API.

Using an open-weight model locally through Ollama gave me more control over the integration and let me develop around a model I could run on my own machine.

It also encouraged a useful architectural separation: AI handles flexible language understanding, while deterministic code handles scheduling decisions.

That combination makes the application easier to reason about and gives it a fallback path when the model isn't available.

Open tools also make the project easier for other developers to inspect, learn from, and extend. Contributors can examine the integration, experiment with different models, or improve the scheduler without needing to treat the AI service as a black box.

The Lazy Payoff 🦥

SlothMode is meant to reduce the mental effort between “I have so much to do” and “Here's one small thing I can start.”

I haven't measured time saved yet, so I won't make up a number. For now, the goal is a calmer starting point, more realistic plans, and permission to take things one step at a time.

Prize Categories

• Best Use of Gemma — SlothMode uses Gemma 3 1B locally through Ollama for task classification, time estimates, urgency, and suggested subtasks.

Less overwhelm. Smaller steps. A gentler way to get things done.

Thanks for checking out SlothMode! 🦥

Always open to suggesstion and improvements ! ❤️