Subhan Asif@Subhan_Lab

This project is a fully autonomous, multi-agent AI trading system built on top of the Alpaca Trading API. Our architecture leverages a sophisticated pipeline of specialized AI agents working in tandem to identify, analyze, and execute profitable options and equity trades. The pipeline begins with a Deterministic Filter that scans live market data to identify high-probability setups based on volume, liquidity, and implied volatility. These candidates are then passed to our Market Agent (for technical thesis generation) and News Agent (for real-time sentiment analysis). An Options Agent synthesizes this data to construct optimal multi-leg strategies. Finally, a Risk Assessment Engine mathematically validates every trade against strict portfolio constraints—such as max daily loss and exposure limits—before our Execution Agent routes the orders live to Alpaca. To visualize this complex orchestration, we built a stunning, real-time Next.js dashboard. It provides traders with an interactive command center to monitor live portfolio equity, track individual AI agent decisions, view strategy payoff charts, and audit the complete execution lifecycle. By combining strict quantitative risk management with advanced LLM reasoning, we’ve created an institutional-grade trading terminal.

**Engineering Studio AI — X Studio X** is an end-to-end multi-agent engineering platform that transforms product briefs into coordinated engineering packages in minutes. The system orchestrates **11 specialized AI agents** across research, mechanical and electrical engineering, firmware, simulation, cost/business/legal analysis, technical review, adversarial testing, exploratory QA, validation, and final quality gating. Rather than simply generating isolated outputs, X Studio X follows a structured engineering lifecycle: **Generate → Simulate → Review → Challenge → Validate → Reconcile → Deliver** Agents work independently and in parallel where appropriate, while dedicated review and validation agents challenge assumptions, identify failure modes, reconcile cross-disciplinary inconsistencies, and verify outputs against project requirements. Generated artifacts can include Bills of Materials, component selections, electrical schematics, source code, mechanical/CAD specifications, simulation results, cost analyses, compliance reports, and verification and validation results. We also built a **CI/CD Dashboard entirely with native.builder**, integrating GitHub OAuth 2.0, workflow monitoring, build/deployment status, and a Supabase backend. Together, this creates a pipeline from: **Product Brief → Engineering Artifacts → V&V → GitHub → CI/CD** Both applications are deployed on Render for live judge evaluation. **Stack:** Python, FastAPI, Fireworks AI, native.builder, React, Supabase, GitHub OAuth, Docker, and Render. X Studio X treats AI engineering as a **systems-engineering and orchestration problem**, demonstrating how specialized agents can function as a coordinated virtual engineering organization rather than a single AI assistant.

What is LogiSecure AI? LogiSecure AI is an enterprise-grade autonomous logistics platform that combines real-time global supply chain monitoring with secure on-premise AI. Through a 5-step agentic workflow, it detects disruptions, analyzes their impact, and autonomously responds while keeping sensitive shipment data on local AMD hardware. The Problem: Logistics companies face a critical trade-off: use cloud AI and risk exposing confidential operational data, or keep data on-premise and lose real-time global visibility. This results in delays, inefficiencies, and reactive decision-making. Our Solution: LogiSecure AI is a 100% on-premise agentic control hub that: Monitors global supply chains using aviation, maritime, GPS, weather, and geopolitical APIs. Detects disruptions with AI-driven risk scoring. Matches incidents with confidential local shipment data. Performs local LLM inference on AMD ROCm to optimize routes and assess impact. Automatically updates routes and prepares client notifications. Technical Implementation: Built with FastAPI, LangGraph, and Fireworks AI, featuring: 5-step multi-agent workflow Real-time data from OpenSky, AISstream, OSRM, and NewsData Unified air, sea, and land shipment tracking AMD ROCm-ready on-premise inference Dockerized deployment Interactive Swagger API Business Value: 🔒 Data Sovereignty: Zero cloud data leakage ⚡ Faster Operations: Response time reduced from hours to seconds 💰 Lower Costs: Eliminates cloud inference fees 🛡️ Risk Mitigation: Proactive disruption management 🌍 Competitive Edge: Secure real-time supply chain visibility

Enterprise investment due diligence is slow, opaque, and inconsistent. Analysts spend hours collecting information across disconnected tools with no systematic review process and no audit trail. Aegis Agent solves this with a genuine multi-agent workflow. Four specialized AI agents — Forensic Planner, Data Analyst, Risk Auditor, and Executive Finalizer — collaborate through Band chat rooms to analyze investment opportunities. Every agent handoff happens via Band. Every decision is logged. When a user submits a company and deal context, Aegis creates a dedicated Band room and the agents execute a structured workflow: 1. Planner (AI/ML API): Decomposes the task, selects optimal inference models 2. Analyst (Featherless AI): Synthesizes findings, cites sources with URLs 3. Reviewer (Featherless AI): Validates analysis, scores risk 0–100, loops back if gaps found 4. Finalizer (AI/ML API): Produces polished dossier with verdict, alternatives, and reasoning chain Users can pause the room and intervene at any point — the agents adapt and continue with user direction baked into their context. The frontend renders this entire operation as a live war room dashboard. Acts are cinematic. Handoffs are visible. Partner tool usage is unmissable. The result: auditable, explainable, enterprise-grade AI investment analysis.

Pay As You Learn is an AI-powered education platform designed to make quality learning accessible and affordable for everyone. Traditional learning platforms require upfront payments, which can be a barrier for many students. Our solution removes this limitation by introducing a flexible, performance-based payment model where users pay gradually as they progress through their courses. The platform uses intelligent AI tutors to provide personalized learning experiences, track student performance, and adapt content based on individual needs. By combining smart automation with outcome-driven pricing, Pay As You Learn ensures that students only pay for the value they receive, creating a fair, motivating, and inclusive learning environment.

🌎 Online Hackathon | 💻 Dates: 28 August–4 September 2026 | 🏆 $6,000 Prize Pool Build AI trading agents on Alpaca — autonomous agents and trading apps using Alpaca's Trading API, MCP server and CLI. 🌍 Fully online hackathon. Join and build from anywhere in the world.

🌎 Online Hackathon | 📅 August 3–10, 2026| 💻 Powered by native.builder The official native.builder hackathon, hosted by NativelyAI and lablab.ai. Join founders, developers, designers, and builders to create real AI-native applications powered by native.builder: from idea to deployed product, fully in the cloud. 💻 This hackathon is fully online. No travel required.

To participate in the hackathon, simply click Sign up with AMD. If you are not already a member of the AMD AI Developer Program (ADP), you will need to create an account. ADP members can access AI tutorials, experts, and community support. In addition, new members can claim a free month of DeepLearning.ai pro, $100 in AMD GPU cloud credits, and $50 in Fireworks AI API credits. All hackathon participants will receive on hackathon launch additional compute and API credits for their projects. Ready to build what's next on AMD? Sign up with AMD below to secure your spot.