clauseCheck: The AI Side Hustle That Saves You from Sketchy Contracts

Mohammadali Shaikh — Mon, 19 Oct 2026 06:34:34 +0000

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

What I Built

I built clauseCheck—a privacy-first contract and terms auditor designed for freelancers, small business owners, and everyday consumers who constantly have to sign binding agreements, terms of service, or freelance contracts without knowing what hidden traps lie in the fine print.

What it does: Users paste a dense legal contract or terms document, and clauseCheck instantly analyzes it to deliver a clear Risk Score (0-100), a plain-English executive summary, and explicit bullet points highlighting dangerous "red flags" (such as automatic renewal traps, unfair liability shifts, or forced arbitration).

The Side Hustle & Monetization Angle: Freelancers and small agencies waste hundreds of dollars on legal fees or lose money signing bad client contracts. clauseCheck can be monetized as a premium micro-SaaS or a specialized desktop tool for independent contractors who need an instant, reliable second opinion on client scope and liability clauses.

Demo

Local Run Instruction: Because clauseCheck is designed to protect user privacy by running 100% locally, the app is built to run on your local machine rather than a public cloud server.
GitHub Repository: Github Repo
Quick Local Setup:
1. Clone the repo and run npm install.
2. Ensure you have Ollama running with Gemma (ollama run gemma).
3. Run npm run dev to launch the local auditor dashboard instantly!

💡Go through Readme.md for complete setup

Here is a short demo video:

Code

GitHub Repository: clauseCheck

How I Built It

clauseCheck is built around Google's Gemma open-weight model running locally via Ollama for text analysis and structured JSON parsing.

Frontend & Backend: Developed using Next.js (React & TypeScript) for a fast, unified full-stack architecture.

Step-by-Step Request Lifecycle

User Input Phase:
The user opens the Next.js local development server in their browser and pastes a dense terms of service agreement, privacy policy, or freelance contract into the text area.

API Request Dispatch:
Clicking "Audit Document" fires a POST request to the local Next.js API route (/api/audit) containing the raw text string.

Prompt & AI Processing (Local Inference):

The backend route formats the text with a strict system prompt instructing Gemma to act as a legal advocate and return a rigid JSON object.
The request is sent locally to Ollama running gemma (http://localhost:11434), ensuring 100% data privacy because no files ever touch a third-party cloud server.

Response & UI Rendering:
Ollama returns the structured JSON response back to the Next.js frontend, which instantly renders the color-coded risk score badge, summary breakdown, and warning flags on the screen.

Why Does Open Innovation Matter?

Open innovation and open-weight models are absolute deal breakers for legal and contract-based tools:

Absolute Privacy: Legal documents, employment contracts, and proprietary freelance agreements contain sensitive financial and personal data. Sending them to a closed, third-party commercial API violates client confidentiality and privacy expectations. Running Gemma locally means client data never leaves the user's laptop.
Zero API Cost for a Micro-SaaS: For a side hustle tool to be profitable, operating costs must be near zero. Relying on closed commercial API tokens for heavy document parsing would quickly eat into profit margins. Local inference on open-weight models costs $0 per query, making the business model actually viable.

Prize Categories

Gemma / Open-Weight Models

What to Cook Tomorrow? I Built an AI Chef for My Mom

Mohammadali Shaikh — Mon, 05 Oct 2026 06:55:18 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

Every single evening, the same universal question echoes through kitchens everywhere: "What on earth should I cook tomorrow?"

For this challenge, I built Tomorrow's Tadka 🍳—an intelligent, culinary-inspired web application designed specifically for my mom. Instead of staring blankly into a full fridge or wondering what to make with random leftovers, she now has a personal AI chef that solves two distinct everyday dilemmas:

🛒 Leftover Rescue Mode: Toss in a random mix of remaining ingredients (like leftover cooked rice, half an onion, and some curd), and the app crafts gourmet home-style recipes out of them.
✨ Seasonal & Weather Surprise Mode: Automatically detects local weather, seasonal vegetables, and regional context to suggest comforting, highly relevant meals.

Demo

Live Deployed App: Check out Tomorrow's Tadka on Render
The live demo runs OpenAI's open-weight gpt-oss-20b through Groq's free tier. The private, run-it-yourself mode uses Meta's Llama 3.2 3B locally through Ollama, so your ingredients never leave your computer.

💡 A Quick Note on the Live Demo:
Because this app is hosted on a free cloud tier, the server may "sleep" after a period of inactivity. If the first recipe generation takes 30–60 seconds to wake up, don't worry—the kitchen is just preheating!

However, Tomorrow's Tadka is designed to live privately on your own machine. If you want an instant, lightning-fast experience that keeps your kitchen data 100% offline and secure, we warmly recommend following the setup steps in the GitHub repo to run it locally on Ollama!

Code

You can explore the full source code and repository structure over on GitHub:
👉 GitHub Repository - Tomorrow's Tadka

2005Mohammadali / moms-meal-planner

An intelligent, local AI-powered meal planner that transforms kitchen leftovers or leverages live weather, locality, and seasonal produce into delicious family recipes.

🍳 Tomorrow's Tadka

"The question we all ask every evening: What should I cook tomorrow?"

Tomorrow's Tadka is a chef-inspired web application designed to help you make the most of what you have in your kitchen or align your meals with the current weather and season. Powered locally by Llama (via Ollama or Groq), it delivers practical, home-style recipes with a warm culinary interface.

✨ Key Features

🛒 Leftover Rescue Mode: Input random fridge ingredients to instantly generate creative meals using basic pantry staples.
✨ Surprise Me Mode: Automatically detects your location, live weather, and seasonal produce to suggest comforting, highly relatable local dishes.
🔒 Privacy-First: Runs locally on your machine via Ollama, keeping your data private.
🎨 Chef-Inspired UI: Features custom culinary typography, an interactive card layout, and smooth mode-switching transitions.

🛠️ Tech Stack

Frontend: HTML5, CSS3, Vanilla JavaScript (Custom culinary design system)
Backend: Python, Flask, Gunicorn
AI Integration:…

View on GitHub

How I Built It

Tomorrow’s Tadka is built around an open-source, lightweight AI stack designed for speed and privacy:

Open-Weight Model: At its core, the app runs on Llama 3.2 (3B) via Ollama, executing local inference straight on the machine.
Full-Stack Architecture: A clean, responsive HTML/JS frontend (index.html) paired with a Python Flask backend (server.py) that parses structured JSON prompts into rich, card-based UI elements.
Open APIs for Context: It integrates with Open-Meteo open weather and geocoding services to fetch live environmental data without relying on heavy commercial tracking tools.

Why Does Open Innovation Matter?

Open innovation was the absolute key to making this project practical for a real home kitchen:

100% Private & Local: Because it runs on Ollama, it can operate locally on a laptop with zero internet dependency. My mom’s random fridge contents and location data never get shipped off to a corporate server she doesn't control.
Zero Cost to Run: There are no paid API tokens or subscription walls to worry about. It costs absolutely nothing to spin up an infinite number of dinner ideas.
Total Flexibility: The open-weight approach made it trivial to swap model runners or tweak prompt behavior to match authentic home-style cooking rather than generic corporate AI responses.

💡 A Note on Hosting vs. Local Usage:
While we’ve set up a public deployment so you can easily test Tomorrow's Tadka right away, this app was truly built to live quietly and privately right on your own machine. If you fall in love with using it for your daily meal planning, we warmly recommend following the instructions in the GitHub repo to run it locally. Running it locally keeps your kitchen data completely private, costs nothing, and gives you the coziest, most seamless experience possible!

🎁 Handing It Over

When I first showed it to her, she was deeply skeptical—fully expecting another complicated app or gadget she’d never touch. But when she typed those leftover random items and the screen instantly spun up a practical, comforting recipe instead of making her scroll through a 2,000-word food blog essay or watch lots of video on YouTube just to find a spice ratio, her expression completely changed.

She didn't give a grand tech speech or clap. She just looked at the screen, nodded approvingly, and said, “Wait, it actually didn't ask me to order anything fancy or go to the market? Keep this tab pinned on my phone.”

For any developer, that’s the ultimate win 😊.

Prize Categories

Used Render to deploy my app.

DEV Community: Mohammadali Shaikh

clauseCheck: The AI Side Hustle That Saves You from Sketchy Contracts

What I Built

Demo

Code

How I Built It

Step-by-Step Request Lifecycle

Why Does Open Innovation Matter?

Prize Categories

What to Cook Tomorrow? I Built an AI Chef for My Mom

What I Built

Demo

Code

2005Mohammadali / moms-meal-planner

An intelligent, local AI-powered meal planner that transforms kitchen leftovers or leverages live weather, locality, and seasonal produce into delicious family recipes.

🍳 Tomorrow's Tadka

✨ Key Features

🛠️ Tech Stack

How I Built It

Why Does Open Innovation Matter?

🎁 Handing It Over

Prize Categories