This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend
What I Built
I built MoveMate, a moving coordination platform, for Arnav Kulshrestha, who **The idea for MoveMate came from a friend who lives in Pune and was shifting from one flat to another. His moving day coincided with a critical deployment at work. He needed to attend meetings and stay available to his team while also coordinating the vehicle, workers, packing and moving his belongings.
He found himself juggling two responsibilities that both needed his attention. Taking time off was difficult, but managing the move alongside work was stressful.
This is a common challenge for working professionals in metro cities. I built MoveMate to help him coordinate the move through a dedicated coordinator, review a moving plan and follow progress in one place, so he could stay informed while focusing on his work.**.
Moving involves more than booking a truck. Someone needs to arrange workers, explain packing requirements, coordinate access and follow up throughout the day. MoveMate brings those tasks into one place, helping my friend plan a move around their work schedule.
The application has three portals:
- Customer: Create a booking, enter inventory and unavailable hours, nominate access delegates, review an AI-generated moving plan, accept quotes and follow progress.
- Coordinator: Apply to join, receive assigned jobs after approval, manage checklists, upload evidence and record moving progress.
- Admin: Review coordinator applications, manage vehicles and workers, assign resources, prepare itemised quotes and handle incidents.
Additional costs require a separate customer decision. The customer confirms delivery before the booking is completed.
Demo
Live application: https://frontend-silk-three-78.vercel.app
The demo follows a complete booking journey: customer registration → moving details and inventory → plan review → submission → admin assignment and quote → customer acceptance → coordinator progress → delivery confirmation.
Feedback from my friend: “After trying MoveMate, I liked having the moving plan, assigned team and progress updates in one place. Being able to enter my unavailable hours and nominate someone to provide access was especially useful because I can’t always step away from work.
The quote review and separate approval for extra costs also made the process clearer. I’d want to try it during an actual move, but the booking experience felt organised and addressed the difficulties I faced while shifting in Pune.”
Code
GitHub repository: https://github.com/Anushkasharma101/movemate.git
The project includes the React frontend, Express backend, API documentation and automated tests.
How I Built It
I used React with Vite for the frontend, Node.js and Express for the backend, and MongoDB Atlas for application data.
The open-weight AI model at the centre of the planning feature is Qwen3-30B-A3B-Instruct-2507, accessed through Backboard.io.
During booking, the backend sends relevant inventory and moving constraints to Qwen. It validates the response and displays an editable draft that the customer reviews before submission. This turns the AI output into a practical part of the moving workflow.
AI does not assign providers, confirm availability, set prices or approve extra charges. Those actions remain controlled by people and backend permissions.
The backend is deployed on Render, and the frontend is deployed on Vercel. Backboard credentials stay on the backend. Server-side access checks keep customer, coordinator and admin operations protected.
Why Does Open Innovation Matter?
Using an open-weight model gives me more control over how MoveMate’s planning feature can develop. Beyond changing prompts, I can explore self-hosting or task-specific fine-tuning in a future version—options that a closed model’s weights would not provide.
The backend model setting is configurable, making it easier to experiment with different models without rebuilding the customer interface.
For this weekend’s prototype, hosted Qwen through Backboard let me focus on the moving workflow without setting up local inference. The current application requires internet access and uses a hosted AI service; it does not claim to run offline or cost nothing.
Customers can also review and save a manual plan when AI is unavailable. The goal is useful assistance that people can inspect and edit.
Prize Categories
- Best Use of MongoDB Atlas — Atlas provides the database behind MoveMate’s application and open-weight AI planning workflow.
MoveMate also integrates Backboard.io for hosted Qwen access and uses Render for backend hosting.
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