Tally: A Private, Local-First Receipt Organizer

atharvarsingh2ee-lab — Tue, 13 Oct 2026 11:57:34 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

Tally is a receipt and invoice organizer for freelancers and solo business owners. It reads receipt images, extracts bookkeeping details for review, saves expenses in the browser, and exports them as CSV. It aims to help people keep expense records without adding another cloud bookkeeping subscription.

The current demo supports manual entry, browser OCR, and CSV export. Local-model extraction is part of the app, but a model is not currently running in the hosted demo.

Demo

Try Tally

The demo is served from a Codespace and is available while that Codespace is running.

Code

The source files (server.py, index.html, styles.css, and app.js) are in my project workspace. I have not published a public GitHub repository yet.

How I Built It

Tally uses Tesseract.js, an open-source OCR engine, to read receipt images in the browser. For field extraction, the Python server can send recognized text to an Ollama model running locally; the setup guide suggests qwen2.5:3b. Extracted details are shown for review before saving. Expense records are stored in browser local storage.

The public demo’s Codespace does not currently have Ollama or a model running, so AI extraction is unavailable there. When run locally with Ollama, the model request stays on that machine.

Why Does Open Innovation Matter?

A hosted AI API might be easier to operate, but it can add a per-document cost and requires sending receipt text to a third party. With Ollama, a freelancer can run inference on their own machine, avoid per-receipt model fees, and switch models or adjust prompts to suit their workflow. Tesseract.js also provides OCR without uploading the receipt to a recognition service.

The tradeoff is setup: users need Ollama and a compatible model for extraction, and OCR downloads its library and language data from a CDN on first use. This public demo is a prototype, not a hosted private-inference service.

mlhacks:)

"Fieldnotes: A Local AI Garden Planner for Getting Outside"

atharvarsingh2ee-lab — Sun, 11 Oct 2026 17:30:52 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass.

What I Built

Fieldnotes is a browser-based garden planner that turns a few minutes of screen time into a practical reason to get outdoors. A gardener enters their region, hardiness zone, typical first frost, available growing space, and planting interests. Fieldnotes then creates a short weekend plan with manageable sowing, planting, and garden-care ideas.

I built Fieldnotes to make getting started feel approachable, whether you have a raised bed, a balcony, or just a sunny windowsill. The goal is simple: get a useful plan, then put the phone away and spend some time outside.

I’m Atharva, a student at Podar International School and an enthusiastic coder. I built this project to explore how open AI can support a small, practical habit in everyday life.

Demo

The project is currently available as a local preview at http://localhost:8000. It is not publicly deployed yet, so this address is only accessible in the development environment.

Code

The application source is in index.html in this project. This workspace is not connected to a public Git repository, so a public source-code link is not available yet.

How I Built It

Fieldnotes is built with HTML, CSS, and JavaScript. For AI, it uses the open-weight Qwen 2.5 0.5B Instruct model in quantized ONNX format, running in the browser through the open-source Transformers.js library.

The browser loads the model on the first request, then uses it to turn the gardener’s selected details into four concise suggestions. Garden details are included only in the local prompt; the app does not send them to a hosted inference API. If the model cannot be loaded, Fieldnotes clearly labels its built-in starter suggestions as a fallback.

Why Does Open Innovation Matter?

Open-weight models make it possible to run this kind of assistant on the gardener’s own device instead of sending personal details to a closed, hosted AI service. That gives people more control over their data and lets developers inspect the model setup, adjust the prompt, or replace the model as the project grows.

There is also no per-request API charge. After the app library and model have been downloaded and cached, the model’s inference can work without an internet connection. The first visit does require internet to download those assets, and the quality of its gardening suggestions still needs to be checked against local conditions.

For Fieldnotes, open innovation makes a more private and adaptable tool possible: one that can help someone decide what to do in their garden without requiring an account or a permanent connection to an AI server. The screen is only the starting point; the best part of the plan happens outside.

DEV Community: atharvarsingh2ee-lab

Tally: A Private, Local-First Receipt Organizer

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?

mlhacks:)

"Fieldnotes: A Local AI Garden Planner for Getting Outside"

What I Built

Demo

Code

How I Built It

Why Does Open Innovation Matter?