
6
3
Pakistan
2+ years of experience
I'm a Computer Science student focusing on Artificial Intelligence, Machine Learning, and Computer Vision. I build practical, impact-driven solutions — especially in healthcare — where I’m working on AI systems for diabetes management and diabetic retinopathy detection. I work with Python, Java, C++, NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and deep learning model development. I actively push myself through hackathons, research-based projects, and competitive programming. I’ve participated in global challenges such as the NASA Space Apps Challenge and multiple university AI competitions. My long-term goal is to pursue advanced research and secure a fully funded scholarship in the USA, while contributing to AI solutions that make life better for people, particularly in Pakistan’s healthcare ecosystem.

📉 The Problem: The High Cost of Clean Data For AI companies, data preparation is the single biggest bottleneck. The Cost Crisis: Centralized services like Scale AI charge massive markups (500%+) to cover corporate overhead. The Blockchain Barrier: Previous decentralized attempts failed because Gas Fees (on ETH/SOL) destroy unit economics. A company cannot efficiently pay $0.01 per label if the transaction fee is $0.05. 🚀 The Solution: Aigarth DataLabel Aigarth DataLabel is a B2B Data Marketplace built on Qubic that allows AI companies to crowdsource data verification at a fraction of the market rate. By leveraging Qubic’s feeless infrastructure, companies pay directly for results, not overhead or gas taxes. ⚡ Value Proposition for Companies 90% Cost Reduction: Because Qubic has zero transaction fees, companies pay only the worker's wage. Automated Quality Control: Our custom C++ Smart Contract enforces a "Rule of 3" consensus mechanism. A label is only accepted (and paid for) when multiple independent workers agree, guaranteeing high-accuracy datasets without manual oversight. Instant Scale: Companies can upload a dataset and tap into a global pool of verifiers immediately. Qubic handles the instant micropayments, removing payroll friction. Aigarth-Ready: We are optimized to generate Trinary Logic (-1, 0, 1) data, specifically positioning client companies to leverage the upcoming Aigarth ecosystem. 🛠 Tech Stack Core: Qubic Smart Contract (QPI) for trustless consensus and payout logic. Frontend: Next.js + Three.js "Corporate Terminal" interface. Integration: Qubic TypeScript Library for real-time batch processing. We are building the Scale AI of the decentralized web- feeless, fast, and enterprise-ready.
7 Dec 2025

The cryptocurrency market is a minefield where 99% of traders lose money, largely because no tool exists to tell them what to ask. While generic chatbots provide vague summaries, there has never been a dedicated system that actively investigates a project's technical and forensic health, until now. BlockchainSeeker is the first Agentic AI platform designed to automate professional-grade due diligence. It solves the "Information Asymmetry" problem by deploying a swarm of specialized agents that work in parallel, mimicking the workflow of a hedge fund research team but accessible to everyone. Unlike standard analysis tools, our system utilizes IBM watsonx Orchestrate to manage a hierarchy of intelligent agents, each with a specific mandate: The Orchestrator (Parent Agent): The first AI designed solely to manage a crypto audit workflow. It breaks down user queries (e.g., "Audit Solana") and commands child agents to execute specific forensic tasks. CodeAuditor (Child Agent): Goes beyond simple searches to verify repository health, commit velocity, and "dead code" signals that human investors often miss. NewsAnalyst (Child Agent): A specialized sentiment engine that filters global media to separate genuine utility from paid influencer marketing. RiskDetective (Child Agent): A forensic investigator that cross-references projects against a proprietary logic of known scams, rug pulls, and legal threats. BlockchainSeeker isn't just a chatbot; it is the first fully autonomous defense system for Web3 investors.
23 Nov 2025

Custom-Leet Agent is a powerful, AI-driven coding assistant designed to work seamlessly with Coral Studio. It specializes in solving popular LeetCode problems such as Two Sum, Valid Parentheses, Palindrome Number, and Binary Search, providing not just the solutions but also time and space complexity analysis, along with detailed explanations of each approach. The system consists of three core components: Flask Backend: A lightweight Flask server running on port 5000, which handles problem-solving requests, offering optimized solutions and analysis for LeetCode problems. Coral Studio Agent: An intelligent agent that connects to Coral Studio using Server-Sent Events (SSE), listens for mentions from other agents, processes the requests, and responds with relevant coding solutions or explanations. Web Interface: A simple, user-friendly HTML form allowing users to submit coding problems and receive immediate solutions
21 Sep 2025