ARslan Ahmad@ARslan_Ahm

StreetFix closes the loop between citizens and city operations teams. A citizen photographs a pothole, broken streetlight, blocked drain or overflowing bin, adds an optional note and location, and submits it in seconds. The report is analysed by an open model served on AMD infrastructure. StreetFix extracts the issue category, visible evidence, safety risk, severity and the responsible department. It then compares the new report with nearby open cases of the same category. If another citizen already reported the same problem, StreetFix merges the two into one case and explains why, instead of creating a duplicate ticket. Operations staff get a priority queue ranked by a transparent score: severity, number of reports, safety risk and age. Every point of the score is explained on screen. Staff can triage, assign, request more information and resolve a case, and each action is recorded in a public timeline. Citizens track their case with its ID and can ask questions such as "What is happening with my report?" The assistant answers only from the official case record, and if something has not been recorded it says so instead of guessing. The model never makes the risky decisions. Priority scoring and the status workflow are deterministic code, and every AI result is labelled with its source and model, so the app never claims AMD inference that did not happen. Built with Node.js, Express and a lightweight web frontend. Goals: fewer duplicate tickets, faster triage of dangerous issues and clearer accountability.

Health Assistant AI is an innovative healthcare solution designed to empower individuals with quick and accurate health predictions. Built using Python, machine learning, and Streamlit, this app integrates multiple datasets related to diseases, symptoms, and patient profiles. The core functionality of the app involves: Symptom Input: Users can enter their symptoms interactively. Disease Prediction: The app utilizes a trained machine learning model to predict potential diseases based on symptoms. User-Friendly Interface: Streamlit enables a responsive and intuitive UI for non-technical users. Next Steps: The app provides general recommendations, enabling users to take preliminary actions before consulting a healthcare professional. This project aims to demonstrate how AI can revolutionize early disease detection, offering users insights into their health without requiring prior medical expertise. The app is lightweight, easy to deploy, and designed for accessibility on various devices.

🌎 Hybrid Hackathon | 💻 Online Build: 3–8 November 2026 | On-site Phase: 8 November 2026 📍 On-site Venue: Pierpont Place 163 W Pierpont Ave, Salt Lake City, UT 84101, USA ✨ Please note that travel and accommodation are not covered.

🌎 Hybrid Hackathon | 💻 Online Build: 12-17 October 2026 | On-site phase: 17-18 October 2026 To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. The AMD Developer Hackathon: ACT III is a hands-on event for developers, founders, engineers and builders who want to push what’s possible with AI on real infrastructure. 📍 If approved for the on-site experience, you'll also have the opportunity to showcase your project live. Travel and accommodation will not be covered.

💻 Online Hackathon | ⏳ Build: 48 hours | 📅 September 25–27, 2026 The official IBM Bob 2.0 hackathon. Build what's next in AI-assisted development, alongside the developers and experts shaping it. ⭐ Get help from experts throughout the event 🤝 Join a team or build solo 💰 Prize pool: $12,000 🧑🏻‍💻 Register before the Kick-Off Stream to start building with Bob

🕒 2 days to innovate and build groundbreaking applications using the latest generative AI tools 🌟 Expert mentors will guide you every step of the way. 🤝 Work alone or form a team to build something extraordinary. 🏆 Win an opportunity to participate in the Lablab NEXT acceleration program. 🧑🏻‍💻 Sign up before the Kick-Off Stream to secure your spot!