This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
Sometimes the hardest part of a task list is deciding where to start.
I built SlothPlan AI, a personal planner that turns a messy brain dump into small, manageable steps—with breaks already included.
You can type something like:
Finish the project proposal today, 50 minutes; reply to emails, 15 minutes; book a dentist appointment, 10 minutes.
With hosted Gemma configured, SlothPlan extracts task names, durations, priorities, effort levels, and deadlines. You review and edit those details before anything is saved.
The planner then builds a daily schedule around your available hours and work budget.
Its defining feature is Sloth Mode. Switch it on for:
A smaller daily work budget.
Focus sessions capped at 25 minutes.
Breaks of at least 15 minutes.
Easier, shorter tasks after urgent deadlines.
Tasks that cannot fit remain visible. If an urgent task exceeds your capacity, the app explains the problem instead of quietly hiding it.
It is for anyone who wants to spend less energy organizing their work—especially when their energy is already low.
And yes, there is a sleepy robotic sloth supervising the dashboard.
Demo
Live demo: https://slothplan-ai.vercel.app/
Code
GitHub repository: https://github.com/charan2r/slothplan-ai.git
How I Built It
The stack is:
React + TypeScript + Tailwind CSS for the interface.
Python + FastAPI for the backend.
SQLite for saved tasks, preferences, and schedules.
Hosted Gemma 3 for natural-language task extraction.
Gemma handles the interpretation: turning informal language into structured task data.
Python handles the scheduling constraints: deadlines, priorities, available hours, focus blocks, breaks, and daily capacity. Keeping those rules explicit makes the resulting schedule easier to explain and test.
The provider connection is configurable through environment variables. Credentials stay on the backend.
Model output passes through server-side validation, and users review the extracted tasks before saving. If credentials are missing or the provider fails, the app can offer an explicitly labeled local-rule fallback. Manual task entry also remains available.
Verification included 12 backend tests, 9 frontend tests, and 4 browser tests, alongside a production build and automated accessibility checks. Hosted-provider success and failure cases were tested with mocked HTTP responses.
Why Does Open Innovation Matter?
The useful freedom here is control over the model behind the planner.
Gemma’s open weights make it possible to move from hosted inference to infrastructure I control, experiment with another model, or explore fine-tuning for better task extraction. The application’s UI and scheduling logic can remain the same.
That flexibility matters because people describe their work differently. A student’s study plan, a freelancer’s client tasks, and someone’s household chores may need different interpretations.
There is also an important privacy distinction: saved tasks live in SQLite on the backend machine, but hosted extraction sends the brain dump to the configured provider. This version does not claim fully offline AI inference. Open weights provide a path toward self-hosting when that becomes a requirement.
My favorite part of the project is its small ambition: take the planning overhead off someone’s plate, give them a realistic next step, and leave room for a break.
Sloth speed feels appropriate.
Prize Categories
Best Use of Gemma — Gemma provides the natural-language task extraction at the center of SlothPlan AI.
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