This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
I built SlothBuddy AI, a simple tool that helps you organize your to-do list without feeling overwhelmed by it.
Sometimes, I have so many things to do that deciding where to start becomes a task of its own. That's the problem I wanted to work on.
With SlothBuddy, you enter your tasks, click a button, and let AI help organize them into three groups: Urgent, Important, and Can Wait. It also suggests a small first step to help you get going.
The idea behind it is pretty simple: you don't always need to work faster. Sometimes, you just need a clearer plan.
Demo
Give SlothBuddy a try!
👉 Live app: https://slothbuddy-gzoagvjdmx8yjsjwyje8av.streamlit.app/
Enter a few tasks and click Organize My Tasks to see it in action.
Code
💻GitHub repository:https://github.com/codelift338-dev/SlothBuddy
Feel free to check out the code!
How I Built It
I built this project using Python, Streamlit, and Hugging Face Inference Providers.
One thing that influenced how I built it was my hardware. I'm working with an older Dell laptop with a Pentium processor and 8 GB of RAM, so running a large AI model locally wasn't a realistic option for me.
Instead, I used a hosted AI model through Hugging Face. This let me build an AI-powered application without needing to download a huge model or upgrade my laptop.
Streamlit helped me turn the Python code into a simple web app, and I used GitHub to store and share the project. I also learned how to deploy the app and configure its dependencies so it could run online.
Getting everything working took a bit of troubleshooting, especially during deployment, but solving those issues was part of the learning experience.
Why Does Open Innovation Matter?
For me, open innovation means having the opportunity to build something useful with the tools and resources already available to you.
I don't have a powerful development machine, but that didn't stop me from experimenting with AI. Open-source tools and access to models through Hugging Face made this project possible for me.
I could focus on building something practical instead of worrying about expensive hardware or setting up a large model locally.
That's what I like about this ecosystem: you can start small, learn as you go, and keep improving your project.
SlothBuddy currently uses hosted inference, so it needs an internet connection. The tasks entered into the app are sent to the AI service for processing.
The Lazy Payoff
SlothBuddy is about making the first step easier.
Instead of spending time manually sorting a long to-do list, you can get an AI-generated plan in a few seconds. You still decide what matters most, but at least you have somewhere to start.
I haven't measured exactly how much time it saves, so I won't make up a number. For me, the useful part is turning a messy list into something that feels more manageable.
Because sometimes, being a little lazy just means finding a smarter way to get started. 🦥
This was a great learning experience for me, from building the first version to getting it deployed online. It's a small project, but I'm happy to have taken it from an idea to a working application.
Thanks for checking it out!
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