
2
2
Pakistan
1 year of experience
I am currently pursuing a Bachelor of Science degree in Computer Science, where I am honing my skills in various programming languages and technologies. My academic journey is complemented by a deep interest in Data Structures and Algorithms (DSA), which I actively practice and improve through platforms like LeetCode. This combination of theoretical knowledge and practical problem-solving equips me to tackle complex challenges and develop efficient solutions. Key Skills and Interests: Programming Languages: Proficient in C++ and Python, with hands-on experience in HTML and CSS for web development. Data Structures and Algorithms: Dedicated DSA learner with a focus on mastering fundamental concepts to enhance my problem-solving abilities. LeetCode Enthusiast: Regularly solving coding challenges on LeetCode to sharpen my skills and prepare for technical interviews. Web Development: Building a solid foundation in web technologies, including HTML and CSS, to create responsive and user-friendly web applications. My passion for computer science drives me to continuously learn and grow. I am committed to leveraging my skills to contribute to innovative projects and collaborate with like-minded professionals. Whether itβs developing efficient algorithms or creating engaging web applications, I am always eager to take on new challenges and expand my horizons.

QuantTrader Lite is a fully autonomous AI-powered crypto trading agent designed to solve three core problems in modern trading: information overload, lack of trust in AI decisions, and slow human reaction time. Built for the Lablab.ai Hackathon 2026 (Kraken CLI Track), the system continuously fetches live market data, analyzes it using Groq AI (Llama 3), and executes paper trades via Kraken CLIβall without human intervention. π§ How It Works The system follows a 5-step autonomous pipeline: Market Data Ingestion Fetches real-time BTC price and 24h change from CoinGecko API. AI Decision Engine Groq-powered Llama 3 analyzes market trends and generates a BUY / SELL / HOLD signal along with a clear, human-readable explanation. Trade Execution The decision is executed using Kraken CLI in sandbox mode (paper trading). Logging & Transparency Every action is recorded in a structured trade_log.json file for auditability. Live Dashboard A Streamlit interface displays signals, trade history, and charts with auto-refresh every 60 seconds. π‘ What Makes It Different Explainable AI Every decision includes a clear reasonβno black-box trading. Fully Autonomous Runs continuously with zero human input. Hackathon-Compliant Direct integration with Kraken CLI ensures full alignment with challenge requirements. Simple but Powerful Built entirely in Python with a lightweight, production-ready architecture. π Tech Stack Groq API (Llama 3) β AI decision-making Kraken CLI β Trade execution (sandbox) CoinGecko API β Live market data Streamlit β Real-time dashboard Python 3.11+ β Core system π― Impact QuantTrader Lite transforms crypto trading from manual, overwhelming, and opaque into a system that is: β‘ Fast π Transparent π€ Autonomous It not only tradesβbut also teaches users why each decision is made, bridging the gap between AI and human trust.
12 Apr 2026

TravelBuddyAI is a smart travel assistant web application designed to help users plan their air travel quickly, efficiently, and affordably. The main goal of this project is to simplify the flight search process and assist users with relevant travel-related information using the power of artificial intelligence and user-friendly web technologies. This application allows users to input natural language queries like βWhatβs the cheapest flight from Lahore to Dubai?β, and it responds intelligently with helpful, personalized information such as flight options, estimated prices, and travel advice. The project aims to provide users with a smooth and engaging experience for finding the best travel routes, flight timings, and pricing, all from one simple interface. At the heart of TravelBuddyAI lies a question-answer-based system that responds to user queries using a Flask-powered backend. It interprets the userβs input and provides relevant travel recommendations. For example, users can ask about economy vs business class prices, the best time to book a flight, or compare airlines for a particular route. The frontend is developed using basic HTML, CSS, and JavaScript, ensuring a responsive and user-friendly interface. The user can type in their query in a text box and click on the βAskβ button. The application then communicates with the backend via an API and displays a response within seconds. Frontend: HTML5, CSS3, JavaScript Backend: Python with Flask API Communication: RESTful API using fetch (POST method) Cross-Origin Support: Flask-CORS to handle frontend-backend communication Templates: Jinja2 rendering for Flask
8 Jul 2025