This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
SlothSync turns a messy list of errands and obligations into a few small next steps, plus a list of tasks that can wait. It’s for anyone who has a full mind and could use help deciding where to start.
Demo
https://slothsync1.vercel.app/
Code
Repository: https://github.com/rohini-ray/SlothSync.git
How I Built It
SlothSync is a small Python web server with a browser interface. The backend reads the Gemini API key from .env and sends the task list to Gemini for a structured plan. The browser keeps the draft and latest plan in local storage.
Why Does Open Innovation Matter?
The app and its planning prompt are open source, so people can inspect and change how the planner behaves. I chose Gemini for hosted inference to keep setup simple and avoid requiring a local model runtime.
There’s a tradeoff: task lists are sent to Google’s Gemini API, and the app requires an API key and an internet connection. This version does not use an open-weight model or run inference offline, so it doesn’t meet that part of the challenge brief. The API key stays in the backend .env file rather than being exposed to the browser.
The Lazy Payoff
SlothSync is designed to reduce the effort of sorting and prioritizing a task pile. I haven’t measured time saved yet, so I’m not claiming a specific number.
Prize Categories
Best Use of Gemini
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