Mirza Yasir Abdullah Baig@mirzayasirabdullahbaig

ActionPilot is an AI-powered meeting execution agent designed to solve one of the most common productivity challenges in modern organizations: turning meeting discussions into real, trackable execution. In many workplaces, meetings generate lengthy audio recordings, unstructured conversations, and scattered notes, making it difficult for teams to capture important decisions, assign responsibilities, and follow up effectively. Employees often spend hours manually creating summaries, writing follow-up emails, and organizing action items, while managers struggle to maintain accountability and ensure tasks are completed on time. ActionPilot addresses this problem by automating the entire post-meeting workflow using advanced AI technologies.

PromptGuard is an AI security platform built to protect LLM-based applications from adversarial prompts. It analyzes user inputs in real-time and classifies them as Safe, Suspicious, or Dangerous using Meta's llama-3.3-70b-versatile model running on Groq's ultra-fast inference API. What sets it apart is its RAG pipeline — organizations can upload their own security policies, which get chunked, embedded into 768-dimensional vectors using nomic-embed-text, and searched via cosine similarity at query time, so every verdict is grounded in your actual rules. It also includes a policy Q&A chatbot for natural language queries against your knowledge base. Built as a zero-dependency single HTML file and deployed on Netlify, PromptGuard requires no build step and goes live in under 60 seconds.

RepoPilot AI is an AI-powered web application designed to help developers quickly understand and analyze GitHub repositories. Built for the IBM Bob Hackathon 2026, the platform simplifies the process of exploring unfamiliar codebases by using artificial intelligence to generate insights about a repository’s architecture, technologies, and overall structure. Instead of manually reading hundreds of files, developers can instantly receive organized explanations and summaries, making onboarding and project understanding significantly faster. The application works as a single-page web app, meaning everything runs inside the browser without requiring complicated installation steps or backend setup. Users simply provide the URL of a public GitHub repository, and RepoPilot AI automatically fetches repository data using the GitHub API. It then processes the information with the help of Groq large language models to generate intelligent analyses. This allows developers to quickly identify frameworks, libraries, dependencies, and architectural patterns used in a project. One of the main strengths of RepoPilot AI is its ability to visualize and explain software architecture. The platform helps users understand how different parts of a project are connected, including system layers, modules, and dependencies. This is especially useful for developers joining a new team or contributing to open-source projects, as it reduces the time needed to understand complex codebases. The application also includes an AI-powered chat feature that allows users to ask questions directly about the repository. Developers can interact with the codebase conversationally by asking questions such as how authentication works, where APIs are defined, or which technologies are used in the project. The AI responds with context-aware answers, creating an experience similar to having a knowledgeable assistant explain the repository in real time.

AI Trading Agents An automated AI-powered trading system designed to analyze market data, generate trading signals, and execute trades using configurable strategies. 🚀 Features 📊 AI-based trading decision making 🔌 Integration with Kraken API 🧪 Paper trading mode for safe testing ⚙️ Configurable trading parameters 📈 Modular and extensible architecture 🗂️ Project Structure AI-Trading-Agents/ │── ai_agent.py # Core AI trading logic │── app.py # Main application entry point │── config.py # Configuration settings │── kraken_client.py # Kraken API integration │── paper_trader.py # Paper trading simulation │── requirements.txt # Dependencies │── .env # Environment variables

MoltTrend Claw is a privacy-first autonomous crypto intelligence agent developed for the SURGE × OpenClaw Hackathon. Built on the OpenClaw sovereign, local-first runtime, the agent continuously monitors live cryptocurrency trends using the CoinGecko API and applies advanced AI reasoning through Gemini to detect emerging market narratives. It analyzes sentiment shifts, identifies momentum patterns, and transforms raw market data into structured, actionable insights without requiring constant human supervision. Designed with persistent memory and long-term analytical capabilities, MoltTrend Claw stores historical trend data in JSON to track narrative evolution over time. Through its multi-page Streamlit dashboard, users can explore real-time trend metrics, visual analytics, and historical agent-generated reports. By combining autonomous execution, intelligent forecasting, and structured memory, the project demonstrates how AI agents can support smarter, data-driven crypto decision-making while maintaining user privacy and local control.

SmartStore AI is a simulation-based robotics startup prototype built to address one of retail’s most common and costly challenges: inefficient shelf monitoring and delayed restocking. Retail stores frequently experience out-of-stock situations that lead to lost sales and poor customer experience due to manual and reactive monitoring processes. SmartStore AI solves this by introducing an AI-driven system that continuously monitors shelf status, tracks customer traffic intensity, and measures how long shelves remain empty. Using a priority-based decision engine, the system automatically generates restocking tasks and simulates an autonomous robot executing them through a structured workflow (Dock → Shelf → Restock → Return). Built with Python and Streamlit, the platform includes a live interactive dashboard that displays shelf conditions, traffic levels, robot position, restocking activity, and performance metrics in real time. Designed as a simulation-first solution, SmartStore AI validates autonomous retail operations in a safe and scalable environment, while laying the foundation for future expansion into multi-robot systems, digital twin environments, computer vision integration, and demand forecasting. The project represents a production-minded, startup-ready robotics solution that bridges AI, automation, and retail intelligence.

DevBug-AI is an AI-powered system that automates bug classification and developer recommendation for software teams. As applications scale, bug triage becomes slow and error-prone due to unclear reports, inconsistent categorization, and manual assignment. DevBug-AI removes this bottleneck by intelligently analyzing bug reports and assigning them to the most suitable developers. Using natural language processing and machine learning, the system classifies bugs from their title and description and recommends the top three developers based on historical bug data, domain, and tech stack. Confidence scores are provided to support reliable decision-making. The solution is built as a Streamlit web app with a unified ML pipeline. It uses TF-IDF and sentence embeddings for text understanding, Scikit-learn for bug classification, and LightGBM for developer recommendation. The system is trained on 50,000+ real bug reports and gracefully handles unseen technologies by mapping them to an “Other” category. DevBug-AI reduces triage time, improves assignment accuracy, and enables data-driven bug management at scale. Future work includes integrations with Jira and GitHub, workload-aware recommendations, and LLM-based bug summarization—making it a practical, production-oriented AI solution for modern engineering teams.

AgentPay-AI is a proof-of-concept platform that demonstrates how Generative AI services can be monetized using pay-per-use, token-based billing—similar to real-world AI APIs. Built with Streamlit and Google Gemini, the system simulates a USDC-style wallet that estimates token usage, deducts balance per request, and only executes AI tasks when sufficient funds are available. This project addresses a major gap in AI demos: cost transparency and usage accountability. AgentPay-AI showcases how AI-as-a-Service (AIaaS), agent marketplaces, and crypto-enabled AI platforms can implement realistic billing logic. Key Highlights: Token-based cost estimation Simulated USDC wallet per session Controlled AI execution based on balance Google Gemini / PaLM integration Simple, intuitive UI Designed as a hackathon and portfolio project for GenAI, SaaS, and Web3 applications.

An AI-powered interactive web application built with Streamlit that predicts whether a candidate will get placed in a job (or admitted) based on academic performance and other features. The model simplifies decision-making for students, HR teams, and academic advisors by providing data-driven placement predictions. The user inputs academic and background features, including: SSC percentage HSC percentage Degree percentage MBA percentage Work experience Specialization Gender And more Inputs are one-hot encoded for categorical features. A Logistic Regression model (trained offline) is loaded using Pickle. The model outputs a binary prediction: “Placed” or “Not Placed”. The result is displayed on the Streamlit app in a clear, user-friendly format.

Learn how modern AI agents interact with the live web through hands-on workshops, real infrastructure, and a community builder weekend in San Francisco. 📅 May 25–31, 2026 • May 25 – 30: Online Build Phase • May 30: Hybrid Build Day (Online & Onsite) • May 31: Demos & Awards (Online & Onsite) 🌍 Hybrid event. Join online from anywhere or build onsite in San Francisco. 🚀 The onsite experience will take place during the opening of Bright Data’s new SF space, The Web Data Loft. 📍 On-site Venue (May 30–31): The Web Data Loft 625 2nd St, San Francisco, CA 🏆 $5,000 cash prize + Bright Data AI Startup Program opportunities 🍕 Free food, drinks, networking, workshops, mentorship, and live demos 🧑‍💻 Apply now to build AI agents powered by real-time web access.

📅 June 12–19, 2026 • June 12: Kick-off Stream • June 12–19: Online Build Phase • June 19: Project Submissions Ends 🌍 Fully online hackathon. Join and build from anywhere in the world. 🤝 Build practical enterprise workflows where AI agents communicate, coordinate, exchange context, and complete work together. 🧠 Powered by Band and Codeband, built for collaborative AI systems and agent orchestration. 🏆 Prize Pool: TBA 🧑‍💻 Apply now to build the next generation of enterprise multi-agent systems.

Grab your ideas, spin up compute, and start building what’s next! Start with $100 in AMD Developer Cloud credits and begin building right away. Get access to hands-on tutorials, workshops, and curated learning resources to help you build faster and go further. To unlock credits and join the hackathon, sign up for the AMD AI Developer Program. 💰 $10,000 total prize pool Developers, builders, and innovators - bring your boldest ideas and let’s turn them into real AI systems on AMD.

Build solutions that improve how software is built. Work with an AI that understands your codebase, helping you reduce repetitive work and build with real context. ⏳ Build your project in 48 hours. 🚀 Get hands-on with IBM Bob and explore how AI fits into real development workflows before it becomes standard. 💡 Build tools and workflows that developers would actually use. ⭐ Learn directly from experts throughout the event. 🤝 Join a team or build solo. 💰 Prize pool: $10,000 🧑🏻‍💻 Register before the Kick-Off Stream and start building with Bob.

⏱️ Build the next generation of Autonomous Agents in the heart of Europe’s AI Revolution. 📅 May 13 – 20, 2026 May 13 – 19: Online Build Phase. May 19: An exclusive on-site build day at Milan AI Week for selected participants. May 20: Awards Ceremony. 📍On-site Venue (May 19–20): Fiera Milano (Rho), Milan, Italy 🤝 Join solo or with a team. 🎟 FREE Milan AI Week conference ticket included for participants. 💰 $32,000+ prize pool 🧑‍💻 Apply now to turn your autonomous agent into a market-ready enterprise solution.

📅 May 11–19, 2026 • May 11 – 19: Online Build Phase. • May 18 (Hybrid Build Day – Online & Onsite) • May 19 (Demos & Awards – Online & Onsite) 🌍 Hybrid event. Join online from anywhere or build onsite at the venue. 🏆 Winners will be announced live on stage at the AI & Big Data Expo North America on May 19 📍 On-site Venue (May 18–19): San Jose McEnery Convention Center, CA, USA 🤝 Join solo or with a team. 🎟 FREE AI & Big Data Expo North America ticket included for participants that are able to join the on-site phase. 🏆Compete for a $10,000 prize pool 🧑‍💻 Apply now to design, prototype, and showcase enterprise-ready innovation.