What I Built
I built BuddyOS for a friend who somehow manages to have five deadlines, three projects, a hackathon, two meetings, and still asks:
"Bhai, deadline kab hai?"
I wanted to build something small, but genuinely useful for one person.
BuddyOS turns the messy information my friend deals with every day into a simple, actionable plan.
They can give it:
- Assignment PDFs
- Hackathon problem statements
- Screenshots containing deadlines
- Meeting notes
- Random text reminders
- Project requirements
- Personal to-do lists
BuddyOS extracts the important information, identifies deadlines and priorities, breaks larger tasks into smaller actions, and builds a realistic plan for what they should work on next.
Instead of another chatbot saying "How can I help?", BuddyOS tries to answer a much more useful question:
"What should you actually do next?"
The project was built around one real person, one real problem, and one goal:
Make their chaotic workload a little easier to manage.
And yes, I actually gave it to my friend to try.
Their first reaction was basically:
"Okay... this is actually useful."
That was the moment this stopped feeling like just another hackathon project.
Demo
How I Built It
The main idea behind BuddyOS was simple:
The AI should be part of the product, not just a chatbot sitting next to it.
The application uses an open-weight model running through local inference to understand the user's documents and notes.
The pipeline looks roughly like this:
Why Does Open Innovation Matter?
This project is personal, which is exactly why privacy matters.
My friend's assignments, project documents, schedules and personal notes are not things I wanted to blindly send to a third-party AI service just to generate a to-do list.
With a local open-weight model,**BuddyOS **can process that information on the user's own machine.
That changes what I can build.
I can:
- Run the model without an internet connection
- Keep personal documents local
- Experiment with different models
- Change the agent's behaviour
- Inspect the workflow
- Customize the system for one specific person's needs
- Build without every interaction becoming another API call
That's what open innovation made possible here.
Prize Categories
- Best Use of Gemma: Gemma is the open-weight model powering the core understanding and planning workflow.
- Best Use of GitHub Copilot: Used during development to accelerate implementation, debugging and iteration.
- **Best Use of Sentry Agent Tracing: **Used to inspect agent behaviour, latency and failures during development.
Submitted by: @jatin_vishwakarma
