MARIA NOOR@MARIA2468

Delivery drivers spend their shifts juggling screens: checking the next address, following the route, calling customers, accepting orders and updating each delivery, often while moving. Every glance at the phone is a moment of distraction on the road. Kora is a voice-first AI co-rider that moves that work into conversation. Just say "Kora" and speak naturally. Ask for your next stop and Kora reads it out while drawing the route on the map. Accept or decline new orders by voice, call or message a customer, check your order queue, track your daily target and end your shift, all hands-free. After each reply Kora keeps listening for a few seconds, so follow-up questions do not need the wake word again. Behind the scenes, one request can trigger several actions at once. Kora runs its tools in parallel rather than one after another, and the driver hears one clear answer. The live conversation runs on AssemblyAI's Voice Agent, connected to 20 purpose-built tools for routes, orders and customer communication. When the shift ends, AssemblyAI LeMUR turns the day's conversation into a shift report. Kora is built with safety and control in mind: it waits for a clear yes before accepting an order, auto-accept only works when the driver switches it on, and new orders are offered near the driver's own location. Drivers can choose from eleven co-rider voices. This hackathon build is an Android prototype. Orders come from a simulator and the customer-call demo reaches no real person, so judges can try the full flow safely.

PACA is a paper-trading options agent built for the Alpaca AI Trading Agents Hackathon. Every five minutes it scans a whitelist of liquid underlyings, computes momentum indicators (RSI, ATR, MACD histogram, EMA distances) and fires events such as gaps, breakouts, and MACD crosses. Deterministic gates filter out exhausted or already-held names. An LLM, or Claude in manual mode, then reasons like a disciplined momentum trader and picks at most one vertical debit spread per prompt, or passes. Code handles screening, sizing, risk caps, mechanical exits, and order placement. The LLM never bypasses those safety controls. Each cycle is journaled and published to a live dashboard.

KidoDev is an AI-powered coding and STEM learning platform inspired by Scratch, designed to help children develop computational thinking, creativity, problem-solving, and critical thinking through interactive, project-based learning. Students solve hands-on coding challenges and real-world projects that strengthen their programming skills, logical reasoning, and confidence. Powered by the Gemma-4 26B-IT model, KidoDev features intelligent AI Tutor and AI Co-Mentor agents that guide students step by step, explain concepts, provide personalized feedback, and encourage independent learning. Built on AMD GPU cloud infrastructure and Fireworks AI, the platform delivers fast, scalable, and responsive AI experiences. KidoDev also includes parental controls, teacher and school dashboards, classroom management, progress tracking, achievements, gamification, and detailed learning analytics, creating a safe, engaging, and personalized learning environment that prepares children with future-ready coding and problem-solving skills.

Our project is an AI-powered compliance analysis system that leverages Coral v1 and the Mistral AI API to evaluate GitHub repositories for domain-specific rules and regulations. The system is designed with a modular architecture consisting of a frontend, backend, and intelligent agents that work together seamlessly. The frontend allows users to input a GitHub repository URL, which is then processed by the backend and routed to specialized agents. These agents, in collaboration with the Fire-Crawl agent, extract the repository’s structure, metadata, and domain-relevant information. The results are compiled into a detailed compliance report, ensuring accuracy, efficiency, and adaptability across various domains.

One of the biggest challenges in modern research is dealing with information overload. Analysts, researchers, and organizations often need to process vast amounts of unstructured data—ranging from academic papers and PDFs to dynamic web content. Manually filtering, extracting, and validating insights is time-consuming, prone to bias, and difficult to scale. Our project addresses this gap by building a mini AI research lab, a system that combines retrieval, structured extraction, experimentation, and judgment. At its core, we use GPT-5 efficiently as a multi-role reasoning agent rather than just a conversational assistant. For example, GPT-5 powers the Extractor, transforming raw text into structured evidence with minimal noise. The Experimenter module leverages GPT-5 to simulate hypotheses, cross-check facts, and test consistency across different sources. Finally, the Judge role allows GPT-5 to evaluate credibility, logical soundness, and contextual relevance—acting as a safeguard against misinformation. By integrating GPT-5 with hybrid retrieval (FAISS + BM25), we ensure precise context delivery while minimizing token usage. This efficient workflow solves a real-world problem: enabling faster, more reliable, and scalable knowledge discovery.

OpsPilot is designed to enhance productivity and communication for professionals by leveraging AI and automation. It acts as a personal assistant for email management and scheduling, reducing the time spent on administrative tasks. Through intelligent integrations with services such as Google Calendar, Gmail, and Coral Protocol, OpsPilot assists with scheduling meetings, providing AI-powered summaries, managing inboxes, and offering insightful reports. Users can rely on OpsPilot to suggest optimal meeting times based on calendar routines, send follow-up emails, and offer smart suggestions to enhance work efficiency.

Auto-DevOps Agent is an AI-powered automation system designed to simplify and accelerate the setup of CI/CD pipelines for developers. Built for hackathons and real-world projects alike, it leverages a multi-agent architecture where each agent performs a specific DevOps task—detecting the tech stack, generating test/build/deploy steps, and combining them into a valid GitHub Actions YAML workflow. It also provides easy-to-understand explanations for each CI/CD step, enabling developers to learn while automating. The system supports various project types such as Python, Node.js, and Java, and is designed to eliminate the manual, error-prone work often required when configuring CI/CD tools. The agents communicate using structured prompts and are powered by LLMs via the Groq-hosted OpenAI SDK using the LLaMA 3.1-8B model. The full stack includes: Frontend: React.js (for user interaction and input handling) Backend: FastAPI (serving the AI agents and managing request logic) Agents: AI multi-agent system using prompt engineering and OpenAI SDK CI/CD Output: GitHub Actions YAML configuration Optional Extensions: Docker, GitHub Secrets, and platform deployment integrations The current version generates test, build, and deploy pipeline steps. Future enhancements will include direct deployment to platforms like Railway, Streamlit, Vercel, and Render, along with automated GitHub secret management and full repository initialization. This makes Auto-DevOps Agent an ideal solution for developers, students, and teams aiming to streamline DevOps without needing deep YAML or DevOps knowledge.

### **AI-Powered Reverse Engineering Assistant** Businesses struggle to stay ahead due to long R&D cycles, high costs, and limited competitor insights. Our AI-powered reverse Engineering Assistant leverages IBM Granite AI and NLP to automate benchmarking, feature extraction, and innovation scoring, helping companies make data-driven decisions faster. ### **Key Features** #### 🔍 **Competitor Product Analysis** - Extracts specs, features, pricing, and feedback from manuals, reviews, patents, and datasheets. - Uses AI to highlight strengths, weaknesses, and innovation gaps. #### 📊 **AI-Powered Benchmarking** - Compares user products against competitors to find areas for improvement. - Analyzes key components, pricing strategies, and market trends. #### ⚙️ **Faster R&D Through Reverse Engineering** - Speeds up product development by analyzing competitors in real time. - Reduces trial-and-error with AI-driven prototyping insights. - Ensures compliance by focusing on publicly available data. ### **Industries That Benefit** ✅ **Consumer Electronics**: Smartphones, laptops, smart devices. ✅ **Automotive**: Vehicle features, safety, energy efficiency. ✅ **Pharmaceuticals**: Drug formulations, medical devices. ✅ **Manufacturing**: Machinery, production techniques. ✅ **Software & SaaS**: AI models, cloud platforms, enterprise apps. ### **Business Impact** ✅ Cuts R&D time by **50%**, accelerating innovation. ✅ Saves costs by providing **ready-to-use insights**. ✅ Enhances market competitiveness by aligning with trends. ✅ Reduces risk by avoiding investment in outdated features. ### **Conclusion** The AI-powered reverse Engineering Assistant helps businesses **innovate smarter** and outperform competitors. By integrating **IBM Granite AI**, it **automates competitor analysis**, making R&D **faster, smarter, and more cost-effective**. **Revolutionizing R&D with AI!**

Our project is an AI-driven assistant built using React, Python, and DeepSeek Groq API, featuring 7 specialized agents to enhance users' professional and personal lives. These agents include a Business Analyst for market insights, a Health Analyst for medical advice, and a News Fetcher for real-time updates on selected topics. The system empowers users by providing expert-driven insights, enabling informed decision-making in business, healthcare, and current affairs. Designed for efficiency, it ensures users stay ahead with crucial information, making it a valuable tool for entrepreneurs, professionals, and individuals seeking reliable, AI-powered guidance in their daily activities.

🌎 Online Hackathon | 💻 Month-long challenge | Dates: Sep 1–30, 2026 | 🏆 $10,000 Prize Pool ($5k cash + $5k in AAI credits) A month-long online challenge run by lablab.ai together with AssemblyAI, the Voice AI infrastructure company for builders. Every participant builds on AssemblyAI. You can join at any point — registration stays open for the whole build window, so you can start on day one or pick the project up halfway through and still submit. 🌍 Fully online hackathon. Join and build from anywhere in the world.

🌎 Online Hackathon | 💻 Dates: 28 August–4 September 2026 | 🏆 $6,000 Prize Pool Build AI trading agents on Alpaca — autonomous agents and trading apps using Alpaca's Trading API, MCP server and CLI. 🌍 Fully online hackathon. Join and build from anywhere in the world.

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. ADP members can access AI tutorials, experts, and community support. In addition, new members can claim a free month of DeepLearning.ai pro, $100 in AMD GPU cloud credits, and $50 in Fireworks AI API credits. All hackathon participants will receive on hackathon launch additional compute and API credits for their projects. Ready to build what's next on AMD? Sign up with AMD below to secure your spot.