What I Built
I built SlothDesk AI, a productivity assistant designed for people who want to get things done without feeling overwhelmed.
We all have tasks we keep postponing, long notes we don't want to read, and big goals that feel difficult to start. SlothDesk AI makes these things easier by breaking tasks into smaller steps and simplifying everyday work.
✨ Features include:
- Lazy Plan Generator: Breaks big tasks into small, manageable steps.
- Note Simplifier: Turns long notes into short summaries.
- Focus Mode: Helps users concentrate on one task at a time.
- Task Manager: Add, complete, and delete tasks.
- Persistent Storage: Saves tasks in the browser using local storage.
- Local AI Integration: Supports Ollama and an open-weight language model for AI-powered assistance.
The idea is simple: spend less energy organizing your work and more time doing what matters.
Demo
🔗 Live Demo: [ file:///C:/Users/Rishi/OneDrive/Documents/Slow%20and%20Steady/index.html]
The demo lets users organize their tasks, generate simple plans, summarize notes, and use Focus Mode.
Code
💻 GitHub Repository: [ [https://github.com/rishiyadavcreator]]
Built with HTML, CSS, and vanilla JavaScript, with optional integration for Ollama.
How I Built It
I built SlothDesk AI using HTML, CSS, and JavaScript.
HTML provides the structure, CSS creates a clean, calming, responsive interface, and JavaScript powers the task manager, Focus Mode, browser storage, and productivity tools.
For the AI component, I designed an optional integration with Ollama, which can run an open-weight language model locally. The app can use a model such as Qwen2.5 3B to generate task plans and summarize notes.
When the model is unavailable, basic fallback functionality keeps the productivity tools usable.
My goal was to create something lightweight and beginner-friendly without depending entirely on paid cloud APIs.
Why Does Open Innovation Matter?
Open innovation matters because AI should be accessible, customizable, and under the user's control.
With a locally running open-weight model, SlothDesk AI can offer several advantages:
- Privacy: Prompts can be processed on the user's own computer rather than sent to an external AI provider.
- Cost control: Local inference avoids per-request API charges, although hardware and electricity still have costs.
- Flexibility: Users can experiment with different compatible models instead of being locked into one provider.
- Offline potential: Once a model is downloaded and configured, inference can work without an internet connection.
- Accessibility: Basic productivity features remain available even when the AI service is unavailable.
A closed API can be convenient, but open-weight models give developers more freedom to experiment and users more control over how their tools work.
For me, open-source AI makes it possible to build a productivity assistant that can grow with its users instead of depending entirely on a paid service.
My Agent Session
I developed SlothDesk AI with the goal of making everyday productivity simpler and less stressful.
🔗 Agent Session: Add your DevRelay session link here if you create and save one.
The Lazy Payoff
SlothDesk AI is built around one question:
What if getting started took less effort?
Instead of staring at a large task, users can break it down into smaller actions. Instead of manually organizing every task, they can keep everything in one place. Focus Mode helps them work on one thing at a time.
The intended payoff is less planning overhead, fewer distractions, and an easier way to begin.
I haven't measured actual time savings yet, so the next step is to ask users to try it and collect feedback on how much effort it saves them.
Prize Categories
Primary theme: Slow and Steady — open-source AI for easier everyday life.
Technology focus: Open-weight models and local inference.
The project is intended to demonstrate how open-source AI can make everyday productivity tools more accessible and customizable.
Built with 🌿, JavaScript, and a little sloth energy.
SlothDesk AI — Do Less, Gently. 🦥
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