This is a submission for the Hacktoberfest Open-Source AI Challenge — Week 2: Side Hustle.
What I Built
Moving into a rental home is exciting, but moving out can be stressful. Tenants may struggle to prove whether a wall mark, damaged fixture, or other issue was already present when they moved in.
That's why I built DepositProof, an AI-powered rental documentation application that helps tenants record property conditions, organize photos, track maintenance issues, and prepare evidence for future reference.
The idea is simple: Document it before. Prove it later.
With DepositProof, users can:
- Create property records and organize rooms.
- Upload photos during move-in, maintenance, and move-out inspections.
- Record existing damage and add notes to condition checklist items.
- Maintain a log of maintenance issues.
- Generate PDF evidence reports.
- Use AI to describe property photos and suggest possible damage categories.
Live Demo
Try DepositProof here: https://rental-evidence-app-4t0s.bolt.host
Explore the dashboard, create a property record, document a room, and see how rental evidence can be organized in one place.
Why I Built It
A rental deposit can represent a significant amount of money. However, documenting a property's condition is easy to forget until the day a tenant moves out.
I wanted to build something practical that encourages tenants to record existing conditions early, keep their evidence organized, and make it easier to review their records later.
DepositProof is my attempt to turn a stressful, disorganized process into a simple documentation workflow.
How Open-Source AI Powers DepositProof
DepositProof connects to Ollama and uses LLaVA 7B (llava:7b), an open-source vision-language model, to analyze property photos.
The workflow is:
- A user uploads a photo of a room or property feature.
- The application stores the photo using its configured storage integration.
- When the user requests AI analysis, the image is sent to their configured Ollama instance.
- The model suggests a description and possible damage categories based on visible details.
- The user reviews the suggestions and documents the property's condition.
Running inference locally gives developers greater control over where AI processing happens and reduces dependence on paid external AI APIs.
AI observations are suggestions, not definitive findings. The model cannot reliably establish when damage occurred, who caused it, or who is legally responsible.
Tech Stack
- React and TypeScript — frontend application
- Vite — development and build tooling
- Tailwind CSS — styling
- Supabase — configured storage and data integration
- Ollama — local AI model runtime
- LLaVA 7B — vision-language model for property photo analysis
What Makes DepositProof Useful?
DepositProof brings several parts of rental documentation together:
- Move-in evidence: Capture property conditions at the beginning of a tenancy.
- Maintenance tracking: Keep a record of issues that need attention.
- Move-out documentation: Organize photos and compare recorded conditions.
- PDF reports: Prepare a printable summary of inspection evidence.
- AI-assisted observations: Get a starting point for describing visible property conditions.
The goal is not to replace careful inspection or professional judgment. It is to make documentation easier to maintain and review.
What I Learned
Building DepositProof gave me practical experience connecting a web application to a locally running vision-language model.
It also taught me that building an AI application involves more than adding a model. The surrounding experience matters: file uploads, storage, inspection workflows, error handling, and making the limitations of AI clear to users.
I learned to treat model outputs as suggestions that people should review, rather than automatically treating generated descriptions as facts.
What's Next?
I want to continue improving DepositProof by:
- Refining photo uploads and AI analysis.
- Making move-in and move-out comparisons more useful.
- Improving PDF reports and evidence organization.
- Strengthening deployment and AI backend configuration.
- Making the project easier for other developers to run and contribute to.
Why Open-Source AI Matters
Open-source AI gives developers the opportunity to experiment, learn, and build practical tools without depending entirely on paid AI services.
With DepositProof, I can run a vision-language model locally, experiment with its capabilities, and build a useful application around it.
My goal is to make rental documentation more organized, accessible, and practical with the help of open-source AI.
I'd love to hear your feedback and suggestions for making DepositProof more useful!
Built for Hacktoberfest 2026 using open-source AI.
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