HustleMate AI

Anshika Singh — Thu, 15 Oct 2026 17:57:13 +0000

HustleMate AI 🚀 — Your Side Hustle Partner

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

Starting a side hustle is exciting. Managing everything that comes with it? Not so much.

Freelancers, creators, and small business owners often have to write content, create product descriptions, calculate prices, and prepare client proposals—all while trying to grow their business.

I built HustleMate AI to make those everyday tasks a little easier.

💡 What I Built

HustleMate AI is an AI-powered toolkit designed to help freelancers, creators, and small business owners manage the practical side of their hustle.

Instead of switching between multiple tools, users can access several useful features from one dashboard:

✍️ AI Content Writer: Generate content ideas and drafts for your projects.
🛍️ Product Listing Generator: Create product descriptions for online stores.
💰 Pricing Calculator: Estimate prices using costs, platform fees, and a target profit margin.
📄 Client Proposal Generator: Create a starting draft for professional client proposals.
⚙️ AI Settings: Configure the local AI endpoint and model.

The goal is simple: spend less time on repetitive business tasks and more time building something valuable.

Who is it for? Freelancers, student entrepreneurs, content creators, and people exploring their first side hustle.

🌐 Demo

Try the live website:

👉 https://anshikasingh139.github.io/HustleMate-AI-/

💻 Code

The project is open source and built with HTML, CSS, and JavaScript.

🔗 GitHub Repository: https://github.com/AnshikaSingh139/HustleMate-AI-

Feel free to explore the code, suggest improvements, or contribute!

🛠️ How I Built It

I built HustleMate AI using three core web technologies:

HTML for the structure and interface.
CSS for the responsive layout and dashboard styling.
JavaScript for navigation, form handling, pricing calculations, AI requests, and output management.

For the AI-powered features, I designed the application to connect to Ollama, which allows users to run an open-weight language model locally.

The default configuration uses the qwen2.5:3b model through Ollama's local API.

This approach lets users generate content using a model running on their own machine rather than requiring a paid, cloud-hosted AI API by default.

The pricing calculator also works independently of the AI features, so users can still calculate prices without running a language model.

🌱 Why Does Open Innovation Matter?

I believe AI tools should be accessible to people who are just getting started—not only to businesses that can afford expensive subscriptions.

Open-weight models and tools such as Ollama make it possible to experiment with AI, customize the experience, and run inference locally.

For HustleMate AI, this opens up some interesting possibilities:

More control: Users can choose which supported local model to run.
Local inference: AI requests can be processed on the user's own machine when configured correctly.
Lower dependency on paid APIs: The application does not require a paid, cloud-hosted AI API by default.
Room to experiment: Developers can modify the prompts, interface, and model configuration.
Learning through building: The project gave me an opportunity to explore how open AI models can be integrated into a practical web application.

Open innovation matters because it gives more people the freedom to build, experiment, and improve technology for their own needs.

🏆 Prize Categories

Add the specific partner prize categories you are entering, based on the official challenge rules.

Building HustleMate AI has been an opportunity to explore how open-source tools and open-weight models can help people take their first steps toward building a side hustle.

It's a starting point, and there's plenty more I'd love to add.

Your hustle. Your AI. Your growth. 🚀

Hacktoberfest #OpenSourceAI #SideHustle #Ollama #BuildInPublic

Touch-Grass AI Nature Companion

Anshika Singh — Sat, 10 Oct 2026 17:42:48 +0000

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

What I Built

I built Touch-Grass-AI-Nature-Companion, an outdoor adventure companion designed to help people spend less time on screens and more time in nature.

Touch-Grass-AI-Nature-Companion AI helps users discover outdoor activities, take daily nature challenges, track their outdoor progress, and generate personalized outdoor adventure plans.

The project includes:

Outdoor activity suggestions
A 20-minute screen-free challenge timer
An outdoor activity progress tracker
An AI-powered adventure planner
A responsive interface built with HTML, CSS, and JavaScript

The goal is simple: use technology to encourage people to disconnect from their screens and reconnect with the real world.

Demo

I built and tested the project locally.

Screenshots and a video demonstration can be added here.

Code

The project uses three separate frontend files:

index.html — Website structure
style.css — Responsive styling and user interface
script.js — Timer, activity tracking, and AI integration

GitHub repository: [Add your GitHub repository link here]

How I Built It

I built Touch-Grass-AI-Nature-Companion AI using HTML, CSS, and JavaScript.

For the AI component, I chose Ollama to run an open-weight language model locally on my computer.

The AI planner is designed to generate personalized outdoor activity plans based on the user's mood, available time, and preferred location.

The project is designed to use Gemma through Ollama. The AI functionality requires the local model and Ollama service to be installed and running.

The timer and progress tracker work directly in the browser using JavaScript.

Why Does Open Innovation Matter?

Open innovation makes Touch-Grass-AI-Nature-Companion AI more accessible, flexible, and privacy-friendly.

By using an open-weight model through Ollama, I can experiment with local AI inference instead of depending on a paid, closed AI API.

This approach offers several advantages:

Privacy: Prompts can be processed locally instead of being sent to a third-party AI provider.
Flexibility: I can experiment with different compatible models.
Accessibility: Local inference can avoid per-request API charges.
Learning: Working with open AI tools helps me understand how AI applications work beyond simply calling a hosted API.

The project also demonstrates that AI can be used to encourage healthier offline habits instead of keeping people glued to their screens.

My goal is to make the screen the starting point of an outdoor adventure, not the destination.

My Agent Session

Optional: Add a DevRelay agent session if available.

Prize Categories

Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass.

Thanks for checking out Touch-Grass-AI-Nature-Companion AI! 🌿

DEV Community: Anshika Singh