
9
3
Pakistan
1 year of experience
Hi! I’m Fouzia Akbar, a data analyst with a strong background in mathematics and programming. I’m passionate about using data to uncover insights and solve real-world problems. With experience in Python, data visualization, and AI tools, I love exploring new technologies that push the boundaries of what's possible. I believe in continuous learning, creative problem-solving, and the power of collaboration. I’m here to connect, innovate, and contribute to impactful AI solutions.

QAaaS-MVP (Quality Assurance as a Service) implements the “Internet of Agents” concept by connecting multiple specialized AI agents into a cohesive system. Each agent performs distinct tasks—repository cloning, automated testing, code aggregation, and unit testing—while communicating through a central server for orchestration. This project demonstrates real-time agent registration, heartbeat monitoring, and task execution, allowing developers to automate QA pipelines efficiently. The modular architecture supports easy addition of new agents, integration with GitHub repositories, and optional Coral Studio monitoring. QAaaS-MVP showcases scalable AI-driven automation, making software development faster, more reliable, and smarter.
21 Sep 2025

SwarmAid is an AI-powered disaster response platform designed to demonstrate how multiple specialized AI agents can collaborate to improve crisis management. When disasters strike, information is often fragmented and response times are critical. SwarmAid brings together four agents – a Data Analyst that interprets satellite and hazard feeds, a Medic Coordinator that analyzes social signals to triage urgent medical needs, a Logistics Manager that plans safe and efficient delivery routes, and a Critic that validates and improves plans. By integrating real-world APIs such as NASA EONET, Twitter/X, and OpenRouteService with advanced AI models, SwarmAid simulates a coordinated, intelligent response system that empowers first responders, NGOs, and governments to save lives faster and more effectively.
24 Aug 2025

This project is a fully open-source AI application that automates the process of reviewing code changes in GitHub pull requests. It uses the power of BLACKBOX.AI’s Coding Agent and leverages Groq’s high-speed inference capabilities to run LLaMA models by Meta for analyzing code diffs and generating insightful, natural-language review comments. Built to enhance developer productivity, the tool seamlessly integrates with GitHub, scans PRs for changes, and offers intelligent suggestions including bug detection, optimization tips, and documentation prompts all within seconds. It features a clean, user-friendly interface that supports both GitHub-connected workflows and manual code review for standalone snippets. By combining ultra-low latency inference (via Groq) with sophisticated language modeling (via LLaMA), this tool aims to reduce review time, increase code quality, and make collaborative development more efficient for teams of all sizes.
8 Jul 2025