This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
Meet RepairSloth, a local-first AI appliance troubleshooting assistant built for people who would rather fix a washing machine than spend hours searching the internet for answers.
When a washing machine stops draining, starts leaking, or refuses to spin, most people have the same questions: What's wrong with it? Is it something simple? Do I need to call a technician?
RepairSloth aims to make that first step easier.
Users can upload a photo of their washing machine, describe the symptoms, and get structured troubleshooting guidance with supporting evidence. Instead of digging through scattered forum posts and troubleshooting articles, users have one place to start investigating the problem.
The goal isn't to replace professional technicians or pretend AI can identify every fault from a photo. It's to help people understand possible causes, identify safe initial checks, and make more informed repair decisions before spending money.
Who gets to be a little lazier? Anyone who owns a washing machine, especially people who aren't familiar with appliance repair and don't know where to begin.
Local demo: RepairSloth is currently an early-stage project that runs locally.
To try it, clone the repository, install the dependencies, start Ollama, download the model, and run the development server.
npm install
ollama pull gemma3:4b
npm run dev
Then open http://localhost:3000.
Note: Ollama must be installed and running. See the README for the complete setup instructions.
Demo video: [https://github.com/user-attachments/assets/235ab0eb-5efc-443b-a765-70d3522df7a4]
GitHub repository: [https://github.com/coder-ralph/RepairSloth]
RepairSloth is built with an open-source-oriented stack, including Next.js, TypeScript, and Ollama for local AI inference.
The project is intended to make the troubleshooting workflow inspectable and customizable, rather than hiding the entire process behind a proprietary API.
I built RepairSloth with a simple principle in mind: AI should reduce the effort required to solve everyday problems, not introduce another complicated workflow.
The current implementation uses:
The application is organized around separate API routes for image identification and diagnosis, with dedicated components for photo uploads, symptom selection, diagnosis results, evidence, and safety notices.
I also designed the workflow to emphasize safety. An AI-generated suggestion should be treated as a possible explanation, not proof that a particular component is broken. Electrical work, internal repairs, and other hazardous procedures should be left to qualified technicians.
Open innovation matters because practical AI shouldn't be limited to companies that can afford expensive API usage or teams with access to proprietary infrastructure.
Using an open-weight model through Ollama gives RepairSloth a path toward local inference. Users and developers can experiment with the model, inspect the surrounding application code, customize the troubleshooting workflow, and potentially adapt the project for different appliances.
It also changes the economics of experimentation. Instead of making every development test depend on a paid, remote inference API, I can run the model on compatible local hardware and iterate on the application.
Just as importantly, open-source development allows other contributors to improve the project. Someone could add better troubleshooting references, expand the supported appliance types, improve the safety checks, or contribute translations for people who need repair guidance in their preferred language.
A small project like RepairSloth won't solve every repair problem. But open innovation makes it possible for more people to participate in improving tools that solve ordinary, real-world problems.