Mirza Yasir Abdullah Baig@mirzayasirabdullahbaig

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.

AskTheWeb: AI-Powered Website Question Answering System AskTheWeb (also known as WebMind AI) is an advanced Question-Answering application designed to tackle information overload by transforming static websites into interactive, conversational experiences. Built using Streamlit, the application leverages the speed and intelligence of Google Gemini 2.0 Flash to understand and synthesize web content in real-time. The core functionality relies on a robust RAG (Retrieval-Augmented Generation) pipeline designed for accuracy and persistence. When a user inputs a URL, the system employs Requests and BeautifulSoup to scrape and clean the HTML data, acting as an efficient ETL transformer to remove messy code and script tags. This cleaned text is converted into high-dimensional vector embeddings using Google’s GenAI SDK and stored in ChromaDB, which acts as the application's "Long-Term Memory". This architecture allows users—such as students, researchers, and analysts—to ask complex natural language questions and receive instant answers that are grounded specifically in the context of the provided URL. Unlike standard chatbots, AskTheWeb ensures that answers are relevant to the specific source material provided. We also addressed significant technical challenges during development, specifically ensuring compatibility with cloud environments. We implemented a custom solution using pysqlite3-binary to patch SQLite version incompatibilities on Streamlit Cloud, ensuring the vector database runs smoothly in production. The result is a scalable, modular tool that makes researching the web faster and more intuitive.

This AI-powered web application recommends movies similar to the one you like using content-based filtering with TF-IDF vectorization and cosine similarity. It’s built with Streamlit to provide an interactive and user-friendly experience, fetching movie posters dynamically via the OMDb API. ✨ Key Features: 🎯 Get top 5 similar movies instantly 🧩 AI-driven content similarity engine 🖼️ Real-time poster fetching from OMDb API ⚡ Clean, fast, and responsive Streamlit UI 🧰 Tech Stack: Python | Streamlit | Pandas | NumPy | Scikit-learn | Requests API The dataset (movies.csv) serves as the foundation of the Movie Recommendation System. It contains essential metadata about movies such as titles, genres, overviews, keywords, and other descriptive features that help the model understand each movie’s characteristics. This structured data enables the system to learn patterns and similarities between different movies, allowing it to recommend films with related content, themes, or genres to the user. The preprocessing phase (preprocess.py) plays a crucial role in transforming raw movie data into a clean, usable format. It removes duplicates, handles missing values, and performs text normalization to ensure consistent input for the model. The movie metadata is then vectorized using TF-IDF (Term Frequency–Inverse Document Frequency) to convert textual information into numerical feature vectors. This enables the use of cosine similarity to measure how closely related two movies are based on their content, forming the core of the recommendation engine.

AgentFlow: AI for Productivity A smart to-do list that prioritizes tasks into 4 quadrants: Urgent & Important | Important, Not Urgent | Urgent, Not Important | Not Urgent, Not Important AI agents provide instant resources & tools for each task. Users can add custom agents or discover new ones from lablab.ai How It Works Tech Stack: Frontend: Next.js + TailwindCSS AI: Gemini 2.5 Flash Deployment: Vercel Process: User creates a task. AgentFlow assigns it to the right priority box. AI recommends best agents/resources (e.g., blog writer, code assistant). Task can be tracked, updated, or deleted once done.

⏱️ Design, build, and deploy trustless AI trading agents. 📅 March 30 – April 12, 2026 🌐 Build AI trading agents that execute strategies using ERC-8004 or Kraken CLI. 🤝 Build solo or in teams to create autonomous trading, risk, yield, or protection agents. 💰 $55,000 prize pool, allocated and shared via Surge and Kraken. Your team should have registered project at early.surge.xyz in order to be eligible for the prize. Get access in your team profile or our discord 🔑 Access to early.surge.xyz If prompted for login credentials, use: • Username: admin • Password: JBRv2xWG7AzwVrLz88

Join our $1,000,000+ startup challenge series powered by Surge 📌 This announcement outlines the launch of a Global Human+AI Exchange — aligning universities, global cities, talent networks, and industry to accelerate human-centered AI innovation, technology transfer, and economic growth. 👉 Read more here ⏱️ 8 days to turn your idea, or existing product, into an investable demo. 📅 February 6 - 15, 2026 • Feb 6 - 14 (Online Phase) - Collaborate and build online with developers and AI innovators from around the world. All projects must be submitted by the end of the online phase on February 14th. 🕙 Doors open at 10:00 AM, first come, first served. • Feb 14 (On‑site Build Day) - Selected participants will be invited to an exclusive in‑person session to refine their projects and connect directly with mentors. • Feb 15 (On‑site Demos & Awards) - Live pitching sessions to a panel of judges and ecosystem partners, followed by the official winner announcement. 🌟 Get feedback from startup mentors and technical experts. 📍 On-site Venue (Feb 14–15): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 📲 Real-time on-site updates (SF) For real-time announcements and information during the on-site portion (Feb 14–15), join the LabLab SF Chapter WhatsApp group: 👉 Join the WhatsApp group 🤝 Join solo or with a team. New founders and existing startups are welcome. 🏆 $1,000,000+ in credits, token prizes, perks and funding opportunity. 📍 On-site participation is by invitation only. Travel and accommodation expenses will not be covered. 🧑‍💻 Apply now to build, validate, and pitch with purpose.

Join Deriv and lablab.ai for a high-intensity hybrid hackathon where top builders create AI prototypes, demo their work, and get fast-tracked to interviews. When • Sat, Jan 31, 2026- On-Site Pre-Hackathon: Deriv AI Meetup (Dubai) • Fri, Feb 6 - Opening words, Challenge, and Hackathon kick-off. (Dubai) • Sat, Feb 7 - Build and ship your prototype - 12 hours (Onsite +Online) • Sun, Feb 8 - Demos, judging, and next steps for standout teams. (Onsite +Online) On-Site Pre-Hackathon: Deriv AI Meetup Sat, Jan 31, 2026 · 2:00–6:00 PM (GST) · Dubai In-person talks, workshops, team formation, and networking with the Deriv team. 👉 Deriv AI Meetup registration What's in it for you • USD $10,000+ in prizes across categories • Mentors available to unblock you when you're stuck • Fast-track interviews for top performers - skip the queue • Real problems to solve - not toy demos Format • Teams of 1-3 • Hybrid: Dubai on-site or fully remote • On-site spots confirmed after screening Location • One by Omniyat, 24th Floor, Al Mustaqbal Street, Business Bay, Dubai, UAE • Travel and accommodation not provided. Apply now • Spots are limited. Shortlisting begins right after the event.