SlowMate AI: Do Less. Move Forward.
This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady. What I Built Meet SlowMate AI , a local-first productivity assistant designed for people who feel overwhelmed by their to-do lists and struggle to decide where to start. Instead of asking users to organize everything themselves, SlowMate lets them dump their thoughts, unfinished tasks, or daily…
This submission is part of the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady. The project is called SlowMate AI, a local-first productivity assistant that helps individuals overwhelmed by their to-do lists. Instead of requiring users to organize everything themselves, SlowMate lets them input their thoughts, unfinished tasks, or daily responsibilities and then generates a small, realistic action plan based on the user's current energy level and available time.
The AI behind SlowMate is an open-weight language model called Qwen2.5 3B, which processes task descriptions locally on the user's computer using Ollama, rather than sending them to a hosted AI API. This approach prioritizes privacy, control, flexibility, and potentially lower costs. The frontend of SlowMate is built using HTML, CSS, and JavaScript, with no frontend framework required.
The project is deployed on GitHub Pages, while AI inference runs locally on the user's device. By removing friction and making the first step easier, SlowMate aims to remove pressure and promote realistic progress in productivity.
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