What I Built
Meet SlothBrief, a local-first AI note summarizer that turns lengthy notes into concise summaries, key points, and actionable tasks.
We all collect notes, meeting details, study material, and long blocks of text. The difficult part is often not writing them down — it is finding the important information and deciding what to do next.
SlothBrief aims to make that process simpler.
With SlothBrief, you can:
Turn lengthy notes into concise summaries.
Extract important points and actionable tasks.
Mark action items as completed.
Copy or download summaries for later use.
Save and revisit previous briefs through local browser history.
Switch between light and dark themes.
The idea is simple: spend less time processing information and more time acting on it.
Demo
Watch SlothBrief in action: https://youtu.be/5DubSUPCUNc
The demo walks through the summarization workflow, generated results, action items, and brief history.
Code
GitHub repository: https://github.com/ChiragKumarChouhan/SlothBrief
The project is built with a React frontend and an Express backend, with Ollama providing local access to the AI model.
Contributions, feedback, and suggestions for improvement are welcome.
How I Built It
I built SlothBrief using an open-source-oriented stack:
React + Vite for the interactive user interface.
Node.js + Express for the backend API.
Ollama to run AI inference locally.
Gemma (gemma3:1b) as the language model.
The workflow is straightforward:
The user enters notes or provides a supported text file.
The frontend sends the content to the Express backend.
The backend communicates with the locally running Ollama model.
Gemma generates a structured summary and actionable items.
The interface displays the result and allows the user to interact with it.
Successful summaries can be saved in the browser's local history for future reference.
One of my priorities was making the application feel like a practical productivity tool rather than just a prompt box connected to an AI model.
I focused on a clean interface, useful actions, readable results, and a simple workflow.
Why Does Open Innovation Matter?
Open innovation made it possible to build an AI-powered productivity tool around a model that can run locally.
Using Ollama with Gemma gives **SlothBrief **an alternative to relying entirely on a hosted, closed AI API. It also gives developers more control over their inference environment and makes experimentation with models more accessible.
This approach has practical advantages:
More control: AI inference can run on the user's own machine.
Privacy-conscious design: Note content can be processed locally instead of being sent to a third-party AI inference service, provided the application is configured to use only the local endpoint.
Accessibility: Developers can experiment with AI without needing a paid hosted-model API.
Flexibility: The model and prompts can be adjusted as the project evolves.
Local inference does not automatically make every part of an application private. **SlothBrief **still depends on how its backend, browser storage, and any external services are configured. The goal is to keep the AI workflow local and minimize unnecessary data sharing.
For me, open innovation is about making useful technology easier to experiment with, understand, and improve.
The Lazy Payoff
SlothBrief is designed to reduce the effort involved in turning unstructured notes into something actionable.
Instead of manually rewriting long notes, users can generate a summary, review the key points, and work through the action items in one place.
I have focused on building the workflow and its supporting features first. I have not yet measured time savings through a formal user test, so I won't claim a specific number of minutes saved.
The next step is to test it with real notes and compare the time required to process them manually against the SlothBrief workflow.
Prize Categories
Select the partner categories that the project actually qualifies for according to the official challenge rules.
Open-Source AI / local inference, if eligible under the challenge's listed categories.
Final Thoughts
Building SlothBrief has been an opportunity to explore how open AI models can power a focused, useful application without making the experience unnecessarily complicated.
The goal isn't to add AI to everything. It's to use AI where it removes friction from a real task.
SlothBrief — less reading, more doing.
Thanks for checking out my project. Feedback and contributions are welcome.
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