StudySprint AI
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady What I Built I built Slow Signal , a local-first brain dump sorter for the things I keep putting off. I paste in a messy list, and an open-weight AI model sorts it into what I could do today, what can wait,
Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady Submission 馃Ε
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
I built Slow Signal, a local-first brain dump sorter for the things I keep putting off. I paste in a messy list, and an open-weight AI model sorts it into what I could do today, what can wait, and what I can let go. It also suggests one tiny first step.
The idea is to make task sorting itself less of a task. No account, streak, or cloud AI service required.
https://pranjalkumar1307.github.io/StudySprint-AI/
To run the app locally, install Ollama and run ollama run gemma4:e2b, then start the Java server with mvn compile exec:java and open http://127.0.0.1:8787.
https://github.com/PRANJALKUMAR1307/StudySprint-AI.git
The project is set up to use Gemma 4 through Ollama, and can also use other installed local models. The server filters out cloud models so brain dumps are sent to a local model.
These notes can include personal details. Running an open-weight model locally means they don鈥檛 need to be sent to a hosted AI service. After the model is downloaded, the app can generate reflections on my computer without an internet connection.
Open models also let me switch models and change how the assistant sorts tasks. That makes the AI part something I can inspect and adapt to the way I want to use the app.
One brain dump replaces manually sorting every task into categories. The app is designed to return a short list and a single tiny first step, so I can spend less energy organizing and more energy deciding what I actually want to do.
I haven鈥檛 measured time saved yet, so I鈥檓 treating that as the goal rather than a benchmark.