Muhammad Ahmad Shahid@iammahmads

The Challenge: In the high-stakes world of online trading, traditional risk management is often "too little, too late." Sophisticated bad actors use "Smurfing" (rapid-fire small transactions) and complex "Circular Laundering" paths to bypass standard rule-based filters. The Solution: Sentinel is a proactive safety layer designed to sit between the user and the platform, processing every transaction through a multi-stage Hybrid Decision Engine: Behavioral Analysis: A real-time Velocity Engine that identifies bot-like patterns and "Smurfing" attempts by tracking transaction frequency. ML Statistical Scoring: A high-precision model that evaluates 7-dimensional feature payloads to assign a "Probability of Fraud" score based on historical anomaly signatures. Graph Structural Intelligence: A neighborhood-mapping engine that detects structural risk. It identifies circular fund movements and "Laundering Hubs" by analyzing the relationship between senders and receivers in a dynamic graph. The Intelligence Layer: When Sentinel identifies a high-risk event, it doesn't just provide a number. It leverages Forensic AI (LLM) to generate a human-readable "Reasoning" report. This allows security teams to understand the why behind a "CRITICAL" alert instantly.

Our project is an AI-powered coding assistant called *Code Copilot*, designed to help programmers improve and understand their code more effectively. It is built using Python, Gradio for the user interface, and Blackbox AI (GPT-4) to provide intelligent coding responses. When a user pastes a piece of code or asks a coding question, the system sends that input to the AI model to generate helpful advice and explanations. At the same time, the app uses Python’s ast (Abstract Syntax Tree) module to deeply analyze the structure of the code by identifying how many functions, loops, and conditionals it has, and how deeply the code is nested. Based on this analysis, the app automatically generates suggestions such as simplifying nested logic, breaking large functions into smaller ones, or replacing basic loops with more efficient techniques like list comprehensions. Everything—including the AI response, code pattern analysis, and smart suggestions—is displayed in a clean, user-friendly interface. This makes Code Copilot a powerful tool for both beginner and experienced developers to write cleaner, more efficient Python code with real-time feedback.

AI code Commentor and Documentation Generator aims to remove headache to remove writing comments, documentation for developers which is real pain for some beginner level developers fellow, we come with idea where a user interface only requires a piece of code and AI will automatically provide comments, documentation, provides improvements if any and gives the time complexity. with more than 90% success rate, we present our project to lablab with plans to broaden the scope of project in future. we are using Trae.ai as our IDE, llma from novita ai and fast api to handle api request also serving a static html page to provide interface to users. we provide simple user interface, to generate comments and documentation at ease.

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This is your chance to push boundaries, solve real-world challenges, and create impact at the official hackathon of the RAISE Summit 2025 at one of Paris's most iconic venues: Le Carrousel du Louvre. 📅 July 4–8, 2025 — Collaborate online with a global community of innovators. 🏛️ July 8–9, 2025 — Limited on-site access to the RAISE Summit in Paris (300 participants), exclusively for selected teams. 🏆 July 9, 2025 — Winners will be announced live on stage during the RAISE Summit. 🌟 Expert mentors will guide you throughout your hackathon journey. 🤝 Work solo or build your dream team to create something extraordinary. 💰 Compete for a share of up to $150,000 in prizes. 🔒 Sign up before the Kick-Off Stream to secure your spot!

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