Tayyeba Saleem@tayyebasaleem

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.

STARK AI is a next-generation autonomous trading agent designed to solve the "black box" problem in algorithmic finance. By combining state-of-the-art market intelligence with decentralized trust, STARK AI ensures that every trade is both smart and verifiable. The agent utilizes the Strykr PRISM API for deep-asset resolution and real-time sentiment signals, allowing it to navigate complex market conditions with neural precision. To ensure total transparency, STARK AI is registered on the ERC-8004 Identity Registry (Agent #51) and signs every trade intent using EIP-712 domain-separated signatures. All activity is visualized through a premium glassmorphism React dashboard, providing a "Director Mode" for interactive overrides and a real-time Verifiable Trust Log of all execution artifacts.

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.

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.

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.

📅 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.

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. 🤝 Go solo or team up — ship something real. 💰 $21,500+ total prize pool & 1 × AMD Radeon™ AI PRO R9700 GPU up for grabs. 📍 On-site Venue (May 9-10): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 📍On-site participation is by invitation only. Travel and accommodation expenses will not be covered. 🧑‍💻 Developers, builders, and innovators - bring your boldest ideas and let’s turn them into real AI systems on AMD.