This is my submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
What I Built
Ever finish a busy day with a pile of notes, unfinished tasks, deadlines, and no idea where to start?
I built SlothBrief, a lightweight, local-first AI assistant that turns messy notes into something actionable.
Instead of manually reading through everything, organizing priorities, and figuring out the next step, you paste your notes into SlothBrief and let an AI model do the first pass.
SlothBrief generates three things:
- A concise summary — understand the important points without rereading everything.
- Prioritized tasks — turn scattered thoughts into actionable next steps.
- A gentle tip — get a small, practical suggestion to make your workload feel more manageable.
For example, you might paste notes about a college project deadline, groceries, a presentation, and a software bug. SlothBrief helps turn that mental clutter into a clearer plan.
The goal isn't to automate thinking away. It's to remove the repetitive organizing work so people can spend more energy actually doing the things that matter.
Who gets to be lazier? Students, developers, busy builders, and anyone whose to-do list is more chaotic than their calendar.
Demo
Project: SlothBrief
The app currently runs locally on my Mac. I'm sharing a screen shot demo rather than claiming a publicly hosted version that I haven't deployed.
The demo will show the workflow: enter messy notes, generate the AI response, and review the summary, prioritized tasks, and gentle tip.
Code
GitHub repository: https://github.com/Kishan-shah12/slothbrief
The repository contains the Flask application, web interface, setup instructions, and project documentation.
You're welcome to explore the implementation, run it locally, and adapt it to your own workflow.
How I Built It
SlothBrief uses a small web application and a locally running open-weight language model.
Tech stack
- Python + Flask: the application backend.
- HTML + CSS: a simple, responsive interface.
- Ollama: runs the language model locally and exposes a local inference API.
- Qwen2.5 3B: the model used to transform notes into structured output.
- JSON-based output: the model is prompted to return a summary, tasks, and a gentle tip in a predictable structure.
The basic workflow is straightforward:
- The user enters unstructured notes.
- Flask sends the notes to the locally running Ollama service.
- Qwen2.5 3B generates a structured response.
- The application parses the response and displays the results.
I deliberately chose local inference instead of making a hosted, closed-model API the core dependency.
That choice makes the project easier to experiment with, keeps the inference workflow on the user's machine, and gives developers a straightforward starting point for modifying the model or application.
To run it, install the Python dependencies, install the model through Ollama, and start the Flask app. The README contains the setup instructions.
Why Does Open Innovation Matter?
For a small productivity tool, using AI shouldn't automatically mean depending on a paid API, sending every note to a third-party service, or building around a single provider.
Open-weight models and local inference give developers another option.
1. More control over personal data
SlothBrief sends prompts to a local Ollama endpoint. With the standard local setup, the inference request stays on the user's machine instead of being sent to a hosted model provider. Users should still review their own environment and any future integrations before entering sensitive information.
2. Fewer provider dependencies
The project is built around a replaceable model-serving layer. Developers can experiment with other Ollama-supported models and compare their quality, speed, and hardware requirements without redesigning the entire interface.
3. A lower barrier to experimentation
A developer can inspect the code, change the prompt, improve task prioritization, or add features without needing access to a proprietary model's internal implementation.
4. Learning by building
Running a model locally makes the process more tangible. You can see how the application, model, prompt, and output parsing fit together—and debug the workflow yourself.
Open innovation doesn't mean every model has identical capabilities or that running AI is completely cost-free. Local inference still requires suitable hardware, electricity, and storage. The benefit is choice: developers can understand, adapt, and run the technology on their own terms.
For SlothBrief, that control is part of the product—not just a technical detail.
My Agent Session
I used AI-assisted development while building and troubleshooting SlothBrief.
I haven't attached a DevRelay session yet. If I publish a session later, I'll add it here so readers can inspect the development process.
The Lazy Payoff
The payoff is fewer manual steps between having a pile of notes and knowing what to do next.
Without SlothBrief, I would need to reread my notes, summarize them, extract tasks, and decide where to begin. With SlothBrief, I can get an AI-generated first draft of that plan in one workflow.
I have tested the application locally and confirmed that it generates the expected types of output. I haven't conducted a formal time-saving study, so I won't claim a specific number of minutes saved.
My next goal is to test it against more realistic notes and improve the reliability of its task prioritization.
The principle is simple: automate the boring first pass, keep the human in control, and save your energy for the work that needs you.
Prize Categories
I'm entering for the overall challenge. I'm not claiming a partner prize category because I haven't integrated a qualifying partner technology into this version of SlothBrief.
Built for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.
If you try SlothBrief, I'd love to hear what you'd automate next.
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