Md. Jihad Hossain

AI Traffic Congestion Predictor

Developed a machine learning model to predict traffic congestion using time-series data and weather patterns. Built a web app to visualize predicted congestion areas.

💪 Abs Calculator Web App

A responsive and animated fitness web app that estimates how long it takes to achieve visible abs based on personal health metrics and lifestyle. Built with pure HTML, CSS, and JavaScript, it provides personalized timelines, calorie targets, and progress insights—all client-side with zero data tracking.

🎮 The Impossible Login

A fun and interactive login form where the "Login" button playfully escapes the user when incorrect inputs are detected. Built with JavaScript and DOM manipulation, it demonstrates real-time event handling and front-end logic to create a dynamic and engaging user experience.

🧠 Equation Mastermind

A smart equation solver that handles multiple types of mathematical equations and clearly displays the solution process. Designed to assist learners by breaking down steps, this tool supports solving both linear and quadratic equations interactively.

🍔 Food Delivery System

A full-featured web-based platform that enables customers to order food online from a single restaurant. The system includes user registration, menu browsing, order placement, and real-time order tracking, along with dedicated dashboards for admin and delivery personnel to manage operations efficiently.

🔐 Scalable User Management System

A robust and modular user management platform designed for scalability and security. This system features secure authentication, role-based access control (RBAC), and clean architectural separation—ideal for integration into enterprise-level applications.

• 🔑 Secure login & token-based authentication
• 🛡️ Role-based access control for users, admins, and managers
• ⚙️ Modular, maintainable, and scalable code architecture
• 🧩 Built with clean separation of concerns (routing, services, middleware)

Designed as a foundational system that prioritizes both functionality and extensibility, ready to support production-grade systems.

♟️ MiniChess AI Lab

MiniChess is a compact chess variant played on a 5×6 board, designed for quick gameplay and experimenting with chess AI logic. This project explores core AI techniques such as move generation, board evaluation, and strategic decision-making in a simplified chess environment—ideal for testing and visualizing algorithms like Minimax and Alpha-Beta Pruning.

• 🧠 AI-driven decision-making with customizable heuristics
• ♟️ Smaller board enables faster iterations and lightweight computation
• 🔍 Built for analyzing core game logic and AI search behavior

A fun and educational project blending game design with artificial intelligence—optimized for learning and experimentation.

🎨 Simple CSS Loaders

A collection of lightweight and visually appealing CSS loaders created without any JavaScript or external libraries. These loaders are ideal for use in web applications, portfolios, or UI prototypes to improve user experience during content loading.

• ⚙️ Pure CSS – no JavaScript involved
• 💡 Includes spinners, pulsers, and wave animations
• 🎯 Easily customizable and responsive

Perfect for designers and developers looking for clean, performant loading animations.

Wumpus World Game – AI Logic & Exploration

Developed a grid-based Wumpus World simulation to demonstrate core AI reasoning techniques including logical inference, knowledge representation, and intelligent agent navigation. The game visualizes how an agent explores an unknown environment while avoiding hazards like pits and the Wumpus.

• Implemented rule-based agent behavior using percepts (breeze, stench, glitter)
• Simulated environment with randomized safe and unsafe cells
• Applied propositional logic and inference to make decisions
• Built with Python and Tkinter for interactive GUI

AutoPrompt-AI – Intelligent GitHub Prompt Generator

AutoPrompt-AI is an AI-powered tool designed to automatically generate high-quality prompts from GitHub repositories. It extracts key insights from code files and metadata, helping developers create descriptive and effective prompts for AI applications, documentation, or learning tools.

This project showcases the integration of natural language processing and repository parsing to automate the prompt creation process. Built with modular architecture and AI-enhanced logic, it ensures accurate, relevant prompt generation from complex codebases.

Key Features: Prompt extraction from GitHub repos · Code analysis with AI insights · Developer-friendly UI · Customizable prompt formats