This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle
What I Built
I built LocalLedger AI, a privacy-focused Windows desktop application designed to help freelancers, individuals, and small business owners manage receipts, expenses, and financial records.
Managing financial records manually can be time-consuming. LocalLedger AI aims to simplify this process through receipt processing, document extraction, transaction management, and financial reporting.
Key Features
- 🧾 Receipt and invoice processing using OCR and PDF extraction
- 💰 Expense and transaction management
- 📊 Financial reports and expense insights
- 🔒 Local-first record storage using SQLite
- 🤖 Optional local AI integration using Ollama and Qwen 2.5 1.5B
- 🖥️ Windows desktop application built with Electron
The project could be useful to freelancers, independent professionals, and small businesses looking for a straightforward way to organize financial records.
Demo
🎥 YouTube Demo: https://youtu.be/b8UtQg6KkzU
📥 Download the Windows Installer:
https://github.com/Suguda-Thakur-Marndi/LocalLedger-AI/releases/download/v1.0.0/LocalLedger.AI.exe
Code
💻 GitHub Repository:
https://github.com/Suguda-Thakur-Marndi/LocalLedger-AI
The project is built with open-source technologies and is available for others to explore, learn from, and contribute to.
How I Built It
I built LocalLedger AI using a combination of web technologies, desktop application tooling, document-processing libraries, and local AI.
Tech stack:
- Frontend: React, TypeScript, Vite
- Desktop application: Electron
- Backend: Python, FastAPI
- Database: SQLite
- OCR and document processing: Tesseract OCR, PyMuPDF
- Local AI: Ollama with Qwen 2.5 1.5B
The application combines a desktop interface with a Python backend for financial record management and document processing. Ollama provides an option to run supported AI workflows locally rather than depending on a hosted AI API.
My goal was to explore how local AI and open-source tools can make everyday financial workflows more accessible while supporting user privacy.
Why Does Open Innovation Matter?
Open innovation matters because useful software should be possible to build without depending entirely on closed platforms or paid AI APIs.
For LocalLedger AI, open-source technologies made it possible to combine document processing, local data storage, desktop development, and local inference into one application.
Using local AI tools such as Ollama and an open-weight model such as Qwen 2.5 1.5B also gave me an opportunity to explore AI workflows without requiring a hosted model API for those local inference tasks.
Open innovation allows developers to inspect implementations, adapt tools to their needs, learn from existing projects, and collaborate with the wider community.
For a financial application, local-first design is particularly interesting because financial documents can contain sensitive information. Local processing can reduce the need to send those documents to external AI services, although the privacy guarantees depend on how each workflow is configured.
Prize Categories
GitHub — Best Use of GitHub Copilot
I'm exploring ways to improve LocalLedger AI's development workflow through automated quality checks, GitHub Actions, and pull-request reviews.
The intended workflow includes frontend and backend testing, automated checks on pull requests, and reviewing proposed code changes before merging.
This category entry should be retained only if I use and document qualifying GitHub Copilot functionality—such as its coding agent, CLI, app, or pull-request review features—in accordance with the official challenge rules. GitHub Actions alone or the use of another AI coding assistant does not establish eligibility.
Built for Hacktoberfest Week 2: Side Hustle. 🚀
Feedback, suggestions, and contributions are welcome!
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