This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady
🧹 What I Built
I built ClutterPilot, a web-based digital clutter organizer designed to make organizing files easier with the help of AI-assisted suggestions.
Digital clutter builds up over time. Files become difficult to find, folders get messy, and organizing everything becomes another task we keep postponing. ClutterPilot explores how AI can help reduce that effort through a simple web interface and a local AI workflow.
The project uses Qwen 2.5 1.5B through Ollama to generate AI suggestions locally. The local AI suggestion workflow has been tested and verified working.
The goal is simple: spend less time managing digital clutter and more time on what matters. 🦥
🌐 Demo
🔗 Live Website: https://clutterpilot.onrender.com
The hosted website demonstrates the deployed web interface. The Qwen-powered AI suggestions have been verified in the local setup; the hosted backend does not automatically connect to Ollama running on my computer. Therefore, the local AI workflow and hosted preview have different capabilities.
💻 Code
🔗 GitHub Repository: https://github.com/Veeresh23-06/ClutterPilot
The project is built with HTML, CSS, JavaScript, Node.js, and Express, with Qwen 2.5 1.5B and Ollama powering the local AI workflow.
🛠️ How I Built It
I built ClutterPilot using:
- 🌐 HTML, CSS, and JavaScript for the web interface.
- 🟢 Node.js and Express for the backend.
- 🧠 Qwen 2.5 1.5B, an open-weight AI model, for AI-assisted suggestions.
- 🦙 Ollama to run the model locally.
- ☁️ Render to host the web application.
I wanted to explore how an open-weight model could support a practical, everyday task rather than using AI just for the sake of adding an AI feature.
Getting the local AI workflow working was an important part of the project. Deploying the web interface also taught me that a locally running model does not automatically become available to a cloud-hosted application. The hosted version and local setup therefore have different capabilities, and I have kept that distinction explicit.
🌱 Why Does Open Innovation Matter?
Open innovation makes AI experimentation more accessible. With an open-weight model such as Qwen, developers can explore local inference, understand how a model fits into an application, and build practical projects without depending entirely on a closed, paid API.
For ClutterPilot, this made it possible to experiment with local AI suggestions and learn how to integrate an AI model into a web application.
It also reinforced an important lesson: building with AI means understanding the model, the runtime, and the deployment environment—not just connecting an API and calling it done.
I believe open tools give more developers the opportunity to learn, experiment, and create useful solutions at their own pace. 🚀
🤖 My Agent Session
I did not use DevRelay to save an agent session for this submission, so I am leaving this optional section out rather than linking to a session that does not exist.
🦥 The Lazy Payoff
ClutterPilot is designed to reduce the effort involved in organizing digital clutter by providing organization features and AI-assisted suggestions.
I have verified that the local Qwen-based AI suggestion workflow works. I have not measured a specific time saving, so I won't claim a number without evidence.
The idea is to make organizing less tedious—one small step toward a less cluttered digital workspace. 📂✨
🏆 Prize Categories
General Hacktoberfest Open-Source AI Challenge — Week 3: Slow and Steady
I am entering the general challenge category using Qwen 2.5 1.5B. I am not claiming eligibility for the separate Best Use of Gemma category.
🧹 ClutterPilot
Organize smarter. Reduce digital clutter. Keep control.
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