DEV Community: Aiman Fatima

GHOSTLY AI

Aiman Fatima — Tue, 27 Oct 2026 10:29:57 +0000

👻 Ghostly AI — The Haunted Story Generator

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine.

What I Built

Ghostly AI is a spooky interactive website that brings horror stories to life through the theme of haunted machines, mysterious messages, and forgotten memories.

Users can enter their own story ideas, choose a horror mood, and generate creepy stories. The project combines a dark, neon-inspired interface with an offline story generator and an optional integration with Ollama for locally running open-weight AI models.

I chose this idea because machines are a big part of our lives, but imagining what happens when a machine starts behaving in mysterious ways makes technology feel a little spooky!

Demo

👻 Try Ghostly AI:

https://aiman00-coder.github.io/GHOSTLY-AI/

Code

💻 GitHub Repository:

https://github.com/Aiman00-coder/GHOSTLY-AI

The project is built using three separate files:

index.html — Website structure
style.css — Dark theme, animations, and visual design
script.js — Story generation and interactive features

How I Built It

I built Ghostly AI using HTML, CSS, and JavaScript.

HTML provides the website structure, CSS creates the spooky atmosphere with purple neon effects and floating animations, and JavaScript handles user input, horror moods, story generation, and copying stories.

The project includes a rule-based offline story generator and an optional integration with Ollama to run an open-weight language model locally.

The local AI integration is designed to generate original stories from user prompts without relying on a hosted AI API. It requires Ollama and a compatible model to be installed and running.

Why Does Open Innovation Matter?

Open innovation gives developers the freedom to experiment, customize technology, and build projects without depending entirely on closed AI services.

With locally running open-weight models, developers can explore AI while keeping prompts on their own machines instead of sending them to an external AI provider. They can also experiment with different models and change how their applications behave.

Another important part of this project is its offline story generator, which provides a basic experience without an internet connection or an AI model.

For me, this project is an opportunity to explore how open technologies can make AI more accessible, customizable, and privacy-friendly.

My Agent Session

I used AI assistance to help plan the project structure, write the HTML, CSS, and JavaScript, and prepare the project documentation.

An agent session recording can be added here if available.

The Spirit of It

Imagine opening an old computer late at night and seeing a message appear on the screen:

"YOU LEFT ME HERE."

That small moment inspired Ghostly AI. I wanted to turn a simple website into an interactive experience where technology, imagination, and horror meet.

Sometimes, the creepiest stories begin with a machine that should have been switched off.

Prize Categories

This project explores the open-source AI theme through its optional local-model integration.

Add specific partner prize categories here only if the project meets their eligibility requirements.

Made with curiosity, code, and a little bit of darkness. 👻

LazyMate AI

Aiman Fatima — Wed, 21 Oct 2026 04:17:31 +0000

🦥 LazyMate AI — Work Smarter, Rest Easier

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 3: Slow and Steady.

What I Built

I built LazyMate AI, a student-friendly productivity website that helps people manage their daily tasks without feeling overwhelmed.

Sometimes, we have so many things to do that even deciding where to start becomes difficult. LazyMate AI is designed to make that easier by allowing users to:

📝 Add, delete, and organize daily tasks.
🎯 Set priorities for different tasks.
✅ Track completed tasks and overall progress.
💾 Save tasks in the browser using local storage.
🧠 Generate a simple, low-stress task plan using a locally running AI model.

The goal is simple: spend less energy organizing your work and more time doing what matters.

Demo

🌐 Live Website: Try LazyMate AI

The website provides a simple dashboard where users can manage tasks and monitor their progress.

Note: The AI planning feature requires Ollama and the selected model to be running locally. The deployed GitHub Pages website cannot access Ollama running on your personal computer automatically.

Code

💻 GitHub Repository: Aiman00-coder/LazyMate-AI-

The project is built using three separate files:

index.html — Website structure
style.css — Styling and responsive design
script.js — Task management, browser storage, and AI integration

How I Built It

I used HTML, CSS, and JavaScript to build the website and create an interactive dashboard.

For the AI component, I designed the application to connect to Ollama with the Llama 3.2 model, allowing the assistant to generate task plans using local inference instead of a paid AI API.

The JavaScript sends unfinished tasks and their priorities to the local model when the user requests a plan. The model then suggests a manageable order of tasks, small steps, and breaks.

This approach also gave me an opportunity to learn about browser storage, asynchronous JavaScript, API requests, and connecting an AI model to a frontend application.

Why Does Open Innovation Matter?

Open innovation makes it easier for developers and students to experiment with AI without depending entirely on paid, closed APIs.

By using a locally running model, LazyMate AI can keep task data on the user's machine during AI processing, offer more control over prompts, and make it possible to experiment with different compatible models.

It also helped me understand that building an AI-powered application doesn't always require an expensive API subscription.

The model needs to be downloaded first, and its license should be checked before describing it as fully open-source.

My Agent Session

I developed this project with AI-assisted coding and used the experience to explore how a frontend application can connect to a local AI model.

I haven't included a DevRelay session link yet.

The Lazy Payoff

LazyMate AI is built around one idea: you don't have to do everything at once to make progress.

Instead of repeatedly deciding which task to start, users can organize their tasks, identify priorities, and request an AI-generated plan.

The project is an exploration of how small automations can reduce decision fatigue and make everyday productivity feel more manageable.

Prize Categories

This project focuses on the challenge's Slow and Steady theme through task management and AI-assisted planning.

HustleMate AI

Aiman Fatima — Thu, 15 Oct 2026 17:45:31 +0000

HustleMate AI 🚀 — Your AI-Powered Side-Hustle Assistant

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

What I Built

I built HustleMate AI, a web-based assistant designed to help freelancers, creators, and small-business owners manage everyday business tasks more efficiently.

Starting a side hustle means wearing many hats: marketer, salesperson, product manager, and business planner. Not everyone has the budget to pay for multiple tools or hire professionals for every task.

HustleMate AI brings several useful tools together in one simple dashboard:

✍️ AI Content Writer — helps draft marketing content and social media posts.
🛍️ Product Listing Assistant — helps create clear, engaging product descriptions.
💰 Pricing Assistant — estimates service prices based on costs, time, and desired margins.
📄 Proposal Generator — helps prepare initial proposals for potential clients.
⚙️ AI Settings — allows users to configure their local AI model connection.

The goal is to reduce the time spent on repetitive business tasks so people can focus more on growing their side hustles.

Who is it for? Freelancers, online sellers, independent creators, and anyone building a small business.

What problem does it solve? It makes common business workflows easier to manage without needing a collection of separate tools.

Demo

🚀 Live Website: https://aiman00-coder.github.io/HustleMate-AI/

Explore the dashboard and try its available tools.

Code

💻 GitHub Repository: https://github.com/Aiman00-coder/HustleMate-AI

The project uses three core frontend files:

index.html — structures the dashboard and tool interfaces.
style.css — provides the styling and responsive layout.
script.js — handles interactions, pricing calculations, and AI requests.

The source code is available for people who want to explore, learn from, or contribute to the project.

How I Built It

I built HustleMate AI using HTML, CSS, and vanilla JavaScript to keep the application lightweight and easy to understand.

For local AI inference, the project is designed to connect to Ollama, which can run open-weight language models on a user's own machine. One example model configuration is qwen2.5:3b.

The intended architecture is straightforward:

The frontend collects a user's task and relevant details.
JavaScript sends an AI generation request to the configured Ollama endpoint.
The model generates a draft or suggestion.
The result is displayed in the dashboard for the user to review and edit.

The pricing assistant also provides a calculation-based workflow for estimating prices.

Because the frontend is deployed on GitHub Pages while Ollama normally runs locally, AI requests require a compatible local Ollama setup and appropriate browser-origin configuration. The hosted website does not itself host the AI model.

Why Does Open Innovation Matter?

Open innovation matters because powerful tools should not be limited to people and businesses that can afford expensive software subscriptions.

By building around open-source frontend technologies and supporting locally run open-weight models, HustleMate AI explores several benefits:

More control: Users can choose a compatible model and manage their own local setup.
Privacy-conscious AI: With a correctly configured local setup, prompts can be processed on the user's machine instead of being sent to a hosted AI provider.
Flexibility: Developers can experiment with different models and improve the workflows.
Accessibility: Freelancers and small businesses can explore AI-assisted work without needing to build an AI model from scratch.
Community collaboration: Other developers can suggest improvements, fix bugs, and add new features.

A closed API can be convenient, but it may tie an application to a particular provider, pricing model, or service. Open-weight models give developers another option: run inference locally and adapt the application around their needs.

For me, this project is a small example of how open innovation can make practical AI tools more approachable for people building something of their own.

Prize Categories

Please select the partner categories you are entering according to the official challenge rules. Only list categories for which the project meets the eligibility requirements.

🌿 Touch Grass: An Open-Source AI Nature Companion to Get You Offline and Outdoors

Aiman Fatima — Fri, 09 Oct 2026 19:30:09 +0000

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

🌿 Touch Grass — Your Nature Companion

What I Built

I built Touch Grass, a nature-themed web application designed to encourage people to spend less time in front of screens and more time exploring the real world.

As students and developers, we often spend hours using laptops, smartphones, and other digital devices. Technology helps us learn and create, but spending all our free time online can make us forget the simple things around us, such as taking a walk, observing trees, listening to birds, or enjoying fresh air.

I wanted to build something that uses technology to encourage people to take a break from technology itself.

Touch Grass makes this idea simple and enjoyable through small outdoor challenges and activity suggestions. Instead of asking people to change their entire routine, it encourages them to start with small, achievable actions.

🌱 Key Features

Nature Challenges: Discover simple outdoor activities, such as taking a short walk, observing a tree, listening to birds, or watching the sunset.
Mood-Based Suggestions: Choose your mood and available time to receive a suitable activity recommendation.
Progress Tracking: Complete challenges, earn points, and track your progress.
Nature Journal: Write down observations and memorable experiences from your time outdoors.
Local Data Storage: Save supported progress and journal entries in the browser using local storage.
Responsive Interface: Access the application through a modern web browser on desktop and mobile screens.

🎯 Who Is It For?

Touch Grass is for students, developers, remote workers, and anyone who spends a lot of time indoors or on digital devices and wants an easy way to reconnect with nature.

The main idea is simple: technology should not only keep us connected to our screens; it can also encourage us to step away from them.

The current version is a working front-end prototype. Its activity recommendation system uses predefined JavaScript rules. Integrating an open-source AI model for more personalized recommendations is a planned next step.

Demo

🌐 Live Website: https://aiman00-coder.github.io/Touch-Grass/

💻 GitHub Repository: https://github.com/Aiman00-coder/Touch-Grass

You can visit the live website to explore the interface and try the available features.

Code

The source code is available on GitHub:

🔗 https://github.com/Aiman00-coder/Touch-Grass

The project is built with separate HTML, CSS, and JavaScript files to keep the code easy to understand, maintain, and extend.

Technology Stack

HTML5: Structures the application and its content.
CSS3: Creates the visual design, layout, and responsive styling.
JavaScript: Powers the interactive features, activity recommendations, and progress tracking.
localStorage: Stores supported user progress and journal entries in the browser.

Project Structure

Touch-Grass/
├── index.html
├── style.css
├── script.js
└── README.md

I kept the implementation lightweight so that it can run directly in a browser without requiring a complicated installation process.

How I Built It

I built Touch Grass using HTML, CSS, and JavaScript, with the goal of making a simple, accessible, and interactive nature companion.

I started by designing the application's structure in HTML, then used CSS to create its nature-inspired appearance and responsive layout. JavaScript handles the interactions, including activity recommendations, challenge completion, points, and journal functionality.

The activity recommendation feature currently uses predefined rules based on the user's selected mood and available time. The application also uses browser local storage to retain supported progress and journal data between visits.

My Open-Source AI Roadmap

The longer-term goal is to make Touch Grass more personalized with an open-source AI model.

One approach I am considering is running a compatible open-weight language model locally through Ollama, with a small backend connecting the model to the web application.

The planned workflow would be:

The user selects their mood and the time they have available.
The application sends these preferences to a local backend.
An open-weight AI model generates a suitable outdoor activity.
The application displays the suggestion.
The user can try the activity and record the experience in their nature journal.

This would allow recommendations to be more flexible than a fixed list of predefined activities.

Transparency note: The current version does not yet integrate an AI model. The recommendation system is rule-based, and the open-source AI integration is planned future work. I want to develop this further so that open-source AI becomes a real part of the application's functionality rather than simply adding an AI label.

Why Does Open Innovation Matter?

Open innovation matters because it gives developers the freedom to learn, experiment, adapt, and build on existing technology.

As a first-year B.Tech CSE (AI/ML) student, I am still learning how to turn an idea into a working project. Open-source tools make this learning process more accessible by allowing developers to study implementations, understand how systems work, and improve their projects without having to build every component from scratch.

For Touch Grass, open-weight AI models could make personalized recommendations possible without requiring every interaction to depend on a closed, proprietary AI API. Running a model locally could also offer greater control over data handling and reduce dependence on an external inference provider, although hardware requirements and local setup would still need to be considered.

Open-source AI also gives developers the opportunity to experiment with different models and choose an approach that fits their project's needs.

I believe innovation should not be limited to people who already have access to expensive tools or large development teams. Open technologies help beginners like me learn by building real projects, understand the limitations of their work, and improve their ideas step by step.

For me, this challenge is an opportunity to explore how open-source AI can be used to solve a small but relatable problem: helping people spend more time outside.

My Agent Session

I do not have a DevRelay agent session to share for this submission yet.

I may add a session link here if I record one during further development.

Prize Categories

I will update this section according to the eligible partner categories and the technologies actually used in the project.

At present, the project is a front-end prototype built with HTML, CSS, and JavaScript. Open-source AI integration is planned but has not yet been implemented, so I am not claiming an AI partner category based on an integration that is not currently present.

Final Thoughts 🌿

Touch Grass started with a simple thought: what if we used technology to remind ourselves to spend a little less time using technology?

I wanted to build something small, useful, and easy to try. This project is also part of my learning journey as a first-year AI/ML student, and I hope to improve it by exploring open-source AI and adding more personalized features.

Sometimes, the best next step is not opening another tab. It is stepping outside.

Build something meaningful. Take a break. Touch grass. 🌱

Thanks for participating in Hacktoberfest Open-Source AI Challenge Week 1!