This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
What I Built
I built SlothFlow — Do Less, Live More, a privacy-focused AI productivity assistant designed to make everyday work simpler and less overwhelming.
The idea behind SlothFlow is straightforward: productivity should not mean constantly feeling busy. Sometimes, the hardest part of getting things done is figuring out where to start.
SlothFlow helps users turn unstructured thoughts and to-do lists into manageable action plans, summarize notes, and prioritize tasks. It also includes a task management dashboard where users can organize their work and track their progress.
The project includes three main AI tools:
- Task Planning: Break overwhelming tasks into smaller, actionable steps.
- Smart Summarization: Turn lengthy notes into concise, useful summaries.
- Task Prioritization: Identify important tasks and decide what deserves attention first.
SlothFlow also supports task tracking, browser-based persistence, and a demo mode so users can explore its features without setting up an AI model.
My goal was to build something that reduces the mental effort of organizing work and helps people focus on one meaningful step at a time.
Demo
Try SlothFlow here:
The application features a calming, nature-inspired interface designed to make productivity feel less stressful.
Code
Explore the complete source code on GitHub:
The project is built with HTML, CSS, and JavaScript, with optional local AI integration through Ollama.
How I Built It
I built SlothFlow using HTML, CSS, and vanilla JavaScript to keep the application lightweight, accessible, and easy to run.
The project combines a responsive productivity dashboard, browser-based task storage, interactive task management, and AI-powered productivity workflows.
For AI integration, SlothFlow supports Ollama, which allows users to run open-weight language models locally instead of relying exclusively on a hosted AI API.
For example, users can download a model such as Llama 3.2 through Ollama and configure SlothFlow to connect to their local model endpoint. The application can then send prompts to the locally running model for task planning, summarization, and prioritization.
The project also includes a demo mode, allowing users to explore the interface without installing a model.
My approach was to keep the core application simple while making local AI an optional capability. This makes SlothFlow easier to experiment with and gives users more control over how they use AI.
Why Does Open Innovation Matter?
I believe AI should be accessible, adaptable, and respectful of user privacy.
Many AI productivity applications depend on centralized services, which can require users to send personal notes, plans, and tasks to external servers. That is not always ideal, especially when the information is private.
Open-weight models and local inference provide another option.
By supporting Ollama, SlothFlow gives users the opportunity to run AI on their own machines, choose compatible models, and experiment without requiring a hosted AI API key for the local workflow.
Open innovation also makes the project easier to learn from and extend. Developers can explore the source code, adapt the interface, experiment with different models, and contribute new features.
For me, the most important benefit is user control. AI should help people get things done without forcing them to give up control over their data or depend entirely on a single provider.
SlothFlow is my small step toward making useful AI tools more approachable, private, and accessible.
My Agent Session
I built SlothFlow with a focus on keeping the application lightweight and understandable while integrating optional local AI capabilities.
I have not included a DevRelay agent session link in this submission. If I publish a session later, I can add it here.
The Lazy Payoff
SlothFlow is designed to reduce the time and mental effort spent deciding what to do, organizing notes, and breaking large tasks into smaller steps.
Instead of staring at a long to-do list and wondering where to begin, users can use the planning and prioritization tools to create a clearer starting point.
The goal is not to automate every part of productivity. It is to remove some of the friction that makes getting started difficult.
I am continuing to improve the project, with the aim of making everyday planning feel simpler and more manageable.
Prize Categories
My primary focus is the Slow and Steady open-source AI challenge: building a useful application around open-weight AI and local inference.
I have not selected any additional partner prize categories. I will update this section if I enter a specific eligible category.
Project: SlothFlow
Live Demo: https://shshnkdixit.github.io/SlothFlow/#ai-tools
Source Code: https://github.com/shshnkdixit/SlothFlow
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