Muhammad Ahmad Shahid@iammahmads

Macro‑Sentry runs a full pipeline. First, it gathers macro and crypto signals. Then an LLM produces a strict JSON decision—BUY, SELL, or HOLD—along with a position size and reasoning. Next, the backend executes the trade via Kraken CLI. For a hackathon demo, we run safely in paper mode by default, but the same pipeline can run live if you configure Kraken CLI for a real account. For the ERC‑8004 trust layer, the agent can register an on-chain identity, produce EIP‑712 signatures for trade intents, and emit validation artifacts that create an auditable history of key decisions and actions. Those artifacts and reputation signals are displayed in the dashboard so judges can see not only what happened, but how it can be verified. Now I’ll show the demo. On the Dashboard, you can see performance metrics and the latest decision pulled from the backend. When I click Auto‑Trade Now, the system runs the complete loop: it fetches signals, the LLM outputs a decision, and we execute through Kraken CLI—paper mode in this demo. The result shows the action, an order id, the risk mode, and the reasoning. That trade is also logged to the Portfolio view so we can track returns and drawdowns over time. In on-chain mode, we additionally post a validation artifact linked to that trade, so it becomes verifiable on-chain and contributes to the agent’s reputation. What makes Macro‑Sentry competitive is that it’s not just a UI demo—it’s a deployable pipeline with safe defaults. You can run it with zero keys for a clean demo experience, and then switch to live execution and on-chain artifacts for real-world operation.

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.

⏱️ 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

Teams work together with multiple AI agents in shared workspaces to design, build, and launch complete AI products — from initial idea to market-ready app. 📅 February 23– March 1, 2026 ⏱️ 6 days to build and ship an end-to-end agentic AI product 🤝 Work together in shared AI workspaces where your team and AI agents collaborate in real time 🔧 Build with multiple AI agents that handle development, design, testing, and documentation together 🎯 Create everything in one place — from ideation to product to marketing materials, all by prompting and observing 🚀 Focus on real-world products that teams could actually ship and use 🏆 $3,500 in prizes + premium 3-month Complete.dev access for top teams 🧑‍💻 Join your team workspace and start building together

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.