Michael Inso@michael_g_inso578

The project above is a browser extension that uses machine learning to train a model with a user's browsing history data and use it to make a decision on granting or blocking permissions for websites.

Welcome to Neon City Metaverse, the ultimate virtual world where you can live, work, and play like never before! Imagine a fully immersive experience where you can customize your own avatar.

Step into the future and explore the bustling streets of a high-tech, downtown metropolis. See towering skyscrapers, and advanced technology everywhere you look.

Kraken xStocks AI Trading Agent is an autonomous trading system designed to analyze, execute, and optimize financial decisions in real time. Built on MeDo’s no‑code AI infrastructure, it integrates Kraken’s API for live market data, enabling predictive analytics, backtesting, and automated trade execution. The agent leverages reinforcement learning and sentiment analysis to adapt to market volatility while maintaining risk‑adjusted performance. Its dashboard visualizes portfolio metrics, trade history, and performance trends, empowering users to monitor and refine strategies dynamically. The project demonstrates scalable AI‑driven finance automation with transparent analytics and modular workflow design.

AgentGuard is an enterprise-grade AI agent security monitoring platform designed to safeguard intelligent workflows in real time. As organizations increasingly deploy autonomous agents to handle sensitive operations, the risks of policy violations, data leakage, and malicious activity grow exponentially. AgentGuard addresses these challenges by providing continuous monitoring, adaptive threat detection, and automated compliance enforcement. The platform integrates seamlessly into enterprise environments, offering audit trails, governance dashboards, and customizable rule sets that empower teams to maintain transparency and accountability. By combining advanced anomaly detection with policy-driven safeguards, AgentGuard ensures that AI agents operate safely, ethically, and in alignment with organizational standards. Built with scalability and modularity in mind, AgentGuard supports diverse industries—from finance and healthcare to logistics and creative enterprises—delivering a flexible solution that adapts to evolving threats. Our mission is to enable enterprises to harness the full potential of AI agents while maintaining trust, compliance, and operational resilience.

The AgentForge AMD AI Agent Build project is a next‑generation framework designed to showcase the power of adaptive agents in real‑world applications. Built on Bolt.new and optimized for AMD’s developer ecosystem, it combines modular workflows, intelligent automation, and creative branding into a single cohesive platform. Our vision is to empower builders, researchers, and enterprises to rapidly prototype and deploy AI agents that can seamlessly integrate into diverse environments — from analytics and finance to creative media and operational transformation. At its core, the project leverages modular agent architecture, allowing developers to plug in specialized components for data ingestion, reasoning, and action execution. This flexibility ensures that the system can adapt to different industries and use cases without requiring a complete rebuild. By integrating workflow automation with intuitive interfaces, the AMD AI Agent Build reduces friction in experimentation and accelerates innovation cycles. The project also emphasizes creative branding and user engagement. We believe that AI agents should not only be technically powerful but also accessible and inspiring to interact with. Through visually engaging content, streamlined documentation, and community‑driven design, the platform bridges the gap between technical depth and user experience. This approach aligns with the ethos of hackathons: rapid iteration, collaboration, and storytelling. From a technical perspective, the build demonstrates scalable performance and cross‑platform adaptability. It highlights how AMD’s infrastructure can support demanding AI workloads while remaining efficient and cost‑effective. By showcasing real‑world applications — such as intelligent assistants for email prioritization, gaming optimization, and creative livestream branding — the project proves that AI agents can deliver tangible value across domains.

Agentic Economy Arc is a hackathon-ready demonstration of how autonomous agents can transact seamlessly using sub-cent USDC nanopayments. Built on Arc, the project highlights the future of machine-to-machine commerce by enabling agent-to-agent payment loops with transparent settlement feeds, velocity metrics, and margin comparisons. The demo includes a live dashboard where users can trigger workloads, view transaction streams, and analyze settlement data. It integrates proof APIs, JSON export buttons, and x402-compatible metadata for every transaction, ensuring judge-ready validation. A new autonomous agent loop generates bursts of paid interactions, updating feeds with velocity, net margin, and total volume. By addressing the inefficiencies of traditional gas costs, the project demonstrates why micropayments are critical for scalable digital economies. While deterministic demo data is used in this workspace, the architecture is designed for seamless integration with Circle/Arc credentials. Agentic Economy Arc not only proves the viability of nanopayments but also strengthens the hackathon’s agent-to-agent commerce track by showing how autonomous systems can sustain real-time, low-cost economic interactions. This project represents a step toward a fully agentic economy where digital agents transact, negotiate, and collaborate without human intervention.

NeuralTrade v1.01** is a next-generation AI trading agent engineered to merge deep learning, adaptive intelligence, and modular workflow design into a single cohesive system for financial automation. Unlike traditional algorithmic trading bots, NeuralTrade is built to continuously evolve, learning from market conditions, sentiment signals, and historical data to refine its strategies in real time. Its architecture integrates predictive modeling, reinforcement learning, and advanced risk management, enabling it to operate across multiple asset classes including equities, forex, and digital currencies. The project emphasizes transparency, scalability, and accessibility. Traders and researchers can experiment with customizable modules, tailoring workflows to suit their unique strategies while maintaining executive-level autonomy. NeuralTrade’s modular design ensures seamless integration with diverse platforms, APIs, and data sources, making it a versatile tool for professionals and independent experimenters alike. Beyond execution, NeuralTrade v1.01 is designed to serve as a research companion. It provides structured insights, comparative analyses, and scenario forecasting, empowering users to test hypotheses and validate strategies before committing capital. Its adaptive intelligence reduces manual overhead, enhances profitability, and opens pathways for creative experimentation in finance. The system also supports community-driven engagement, encouraging collaboration, feedback, and modular asset sharing. By combining scientific rigor with creative flexibility, NeuralTrade v1.01 positions itself as more than just a trading agent—it is a platform for innovation, discovery, and independence in the evolving landscape of financial technology. With its focus on modular branding, workflow optimization, and cross-platform deployment, NeuralTrade is not only a technical solution but also a foundation for building sustainable, community-supported trading ecosystem

🎯 Overview This simulation platform demonstrates a fully autonomous warehouse robotics system with 10+ robots operating in a coordinated fleet across multiple warehouse facilities. The system showcases real-world applications of AI in logistics, including adaptive learning, predictive analytics, swarm intelligence, and energy optimization. Track Alignment: Autonomous Robotics Control in Simulation (Track 1) Key Capabilities 🎮 Real-Time Control: 10-robot fleet with intelligent task assignment and pathfinding 🧠 Adaptive Learning: Congestion-aware speed adjustment and traffic pattern analysis 🌐 Multi-Warehouse Network: Robot transfers across 6 interconnected facilities 🎙️ Voice Commands: Natural language control with 60+ voice commands 📊 Predictive Analytics: ML-powered task completion and maintenance forecasting 🤝 Swarm Intelligence: Emergent behavior detection and collaborative tasking ⚡ Energy Management: Battery optimization and charging station analytics 🔮 Digital Twin: What-if scenario analysis and system simulation ✨ Core Features 🤖 Autonomous Fleet Management 10 Robots, Zero Manual Control The system manages a fleet of 10 autonomous robots that: Self-assign tasks using intelligent priority algorithms Navigate using A* pathfinding with dynamic obstacle avoidance Maintain safe distances with real-time collision detection Return to charging stations when battery levels are low Adapt behavior based on congestion patterns Robot Status Monitoring: Real-time position tracking Battery level indicators Task assignment visibility Speed and efficiency metrics Transfer status (cross-warehouse) 🧠 Adaptive Learning System Congestion-Aware Intelligence The adaptive learning system continuously analyzes traffic patterns and adjusts robot behavior: Traffic Analysis: 3x3 zone grid monitors robot density in real-time Speed Modulation: Robots automatically slow in congested areas (0.3x-1.0x speed) Pattern Learning: System learns high-traffic zones over time

A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets This project implements a Stock Trading Bot, trained using Deep Reinforcement Learning, specifically Deep Q-learning. Implementation is kept simple and as close as possible to the algorithm discussed in the paper, for learning purposes. Generally, Reinforcement Learning is a family of machine learning techniques that allow us to create intelligent agents that learn from the environment by interacting with it, as they learn an optimal policy by trial and error. This is especially useful in many real world tasks where supervised learning might not be the best approach due to various reasons like nature of task itself, lack of appropriate labelled data, etc. The important idea here is that this technique can be applied to any real world task that can be described loosely as a Markovian process. This work uses a Model-free Reinforcement Learning technique called Deep Q-Learning (neural variant of Q-Learning). At any given time (episode), an agent abserves it's current state (n-day window stock price representation), selects and performs an action (buy/sell/hold), observes a subsequent state, receives some reward signal (difference in portfolio position) and lastly adjusts it's parameters based on the gradient of the loss computed. There have been several improvements to the Q-learning algorithm over the years, and a few have been implemented in this project: Vanilla DQN DQN with fixed target distribution Double DQN Prioritized Experience Replay Dueling Network Architectures Trained on GOOG 2010-17 stock data, tested on 2019 with a profit of $1141.45 (validated on 2018 with profit of $863.41):

An artificial intelligence podcast that is written by ChatGPT, GPT-3.5, Open-AI davinci, and human assistance. The art is generated by Stable Diffusion, Open Journey, and Dall-E 2. It is read by Natural Readers text-to-speech and Lifelike Speech Synthesis Google Cloud. The platform used is Anchor.fm and the availability of the podcast are in Google Podcasts, Apple Podcasts, Amazon Music, Spotify, Castbox, Pocket Casts, RadioPublic, and Stitcher. The podcast description is: "Join us as we explore the rapidly advancing world of artificial intelligence, and what it means for our future. In each episode, we'll discuss the latest AI research and developments, and how they are poised to impact various industries and aspects of our daily lives. From self-driving cars to intelligent virtual assistants, we'll delve into the potential and the challenges of this rapidly evolving technology. Tune in to stay up-to-date on the future of AI and its impact on society." Created and written by Artificial Intelligences and Cyber World. Currently the podcast has 12 episode in season 1 which has one episode for introduction and special and it has 5 episode currently for season 2. AI has come a long way since its inception and has been widely used in various fields such as healthcare, finance, and transportation. AI-powered machines and systems have the ability to learn and adapt to new situations without the need for human intervention. This ability of AI has made it an integral part of various industries and has brought about significant changes in the way we work and live. The current state of the AI industry is quite promising. The AI market is expected to grow from $9.5 billion in 2018 to $118.6 billion by 2025. The adoption of AI is increasing at a rapid pace and is being used in a variety of applications such as image recognition, speech recognition, and natural language processing. The use of AI in healthcare has also shown promising results, with AI-powered systems.

## Inspiration - Classic cyberpunk novels and films such as "Neuromancer" by William Gibson, "Blade Runner" and "Akira" for the aesthetic and themes of advanced technology, artificial intelligence, and a gritty, neon-lit urban setting. - Virtual worlds and massively multiplayer online games like "Second Life" and "Cyberpunk 2077" for the idea of a fully customizable and immersive virtual world. - The bustling cities of the future such as Tokyo, Singapore and Hong Kong, which are known for their unique blend of advanced technology, towering skyscrapers, and diverse cultures. - The concept of "smart cities" and the Internet of Things (IoT), which can be incorporated into the game world to make it more interactive and responsive to player actions. - The open-world sandbox games like "Grand Theft Auto" and "Saints Row" for the potential for player choice and non-linear gameplay. - The virtual reality and augmented reality technology for providing a more immersive and interactive experience for the players. - The existing cyberpunk communities, such as the cyberpunk fashion, music, and culture. ## What it does The project "Neon Metropolis" is a fully immersive cyberpunk city virtual reality and metaverse that allows players to explore a neon-lit world filled with advanced technology and a diverse cast of characters. Players can create their own unique avatars and build their own virtual businesses and communities within the game world. The game offers endless possibilities for customization and personalization, allowing players to tailor the experience to their own preferences. The game also incorporates elements of "smart cities" and the Internet of Things (IoT) to make the game world more interactive and responsive to player actions. The project aims to offer a fully immersive and engaging virtual world experience with the potential for player choice and non-linear gameplay and the possibility of using Virtual Reality or Augmented Reality technology.

Learn how modern AI agents interact with the live web through hands-on workshops, real infrastructure, and a community builder weekend in San Francisco. 📅 May 25–31, 2026 • May 25 – 30: Online Build Phase • May 30: Hybrid Build Day (Online & Onsite) • May 31: Demos & Awards (Online & Onsite) 🌍 Hybrid event. Join online from anywhere or build onsite in San Francisco. 🚀 The onsite experience will take place during the opening of Bright Data’s new SF space, The Web Data Loft. 📍 On-site Venue (May 30–31): The Web Data Loft 625 2nd St, San Francisco, CA 🏆 $5,000 cash prize + Bright Data AI Startup Program opportunities 🍕 Free food, drinks, networking, workshops, mentorship, and live demos 🧑‍💻 Apply now to build AI agents powered by real-time web access.

AI Genesis is a global hybrid hackathon that brings AI builders together from around the world — starting online and culminating live at /function1 AI Conference & Exhibition 2026 in Dubai. 📅Oct 26 – Nov 3, 2026 • Oct 26 – Nov 2: Online build & collaboration All projects must be submitted by end of day on November 2. • Nov 2 (Online + Dubai): An exclusive on-site build day in Dubai for selected participants. • Nov 3 (Dubai): Live on-stage pitching & winners announcement at the /function1 Conference. 🌟 Get support from expert mentors throughout. 👥 Go solo or team up. 📍 Please note: If approved for the on-site experience, you’ll also have the opportunity to showcase your project live. Travel and accommodation will not be covered. 🧑🏻‍💻 Secure your spot now, sign up before the Kick-Off Stream!

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.

⏱️ Build the next generation of Autonomous Agents in the heart of Europe’s AI Revolution. 📅 May 13 – 20, 2026 May 13 – 19: Online Build Phase. May 19: An exclusive on-site build day at Milan AI Week for selected participants. May 20: Awards Ceremony. 📍On-site Venue (May 19–20): Fiera Milano (Rho), Milan, Italy 🤝 Join solo or with a team. 🎟 FREE Milan AI Week conference ticket included for participants. 💰 $32,000+ prize pool 🧑‍💻 Apply now to turn your autonomous agent into a market-ready enterprise solution.

📅 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.