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.

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.

In this hybrid hackathon powered by Arc and Circle, developers will build the next generation of Agentic economic applications using Circle’s Nanopayments infrastructure with settlement on Arc in USDC. For the first time, high-frequency, usage-based transactions between users, APIs, and AI agents can operate without gas overhead eroding margin. Arc provides stablecoin-native settlement with predictable, dollar-denominated USDC fees and deterministic, sub-second finality — enabling real-time coordination and economically viable per-action pricing at scale. 📅 April 20 – 26, 2026 • Apr 20 – 25 (Online): Submit projects by Apr 25. • Apr 25 (On-site Build – SF, CA): Teams refine projects. • Apr 26 (On-site Demos & Awards – SF, CA): Live pitches & winners announced. Get guidance from Arc and Circle experts through on-site support. 📍 On-site Venue (April 25-26): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 🤝 Go solo or team up, it's your flow. 🏆 Compete for a $10,000 Prize Pool. 📍 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!

Grab your ideas, spin up compute, and start building what’s next! Start with $100 in AMD Developer Cloud credits and begin building right away. Get access to hands-on tutorials, workshops, and curated learning resources to help you build faster and go further. To unlock credits and join the hackathon, sign up for the AMD AI Developer Program. 🤝 Go solo or team up — ship something real. 💰 $10,000 prize pool up & 1 × AMD Radeon™ AI PRO R9700 GPU for grabs. 📍 On-site Venue (May 9-10): MindsDB SF AI Collective 3154 17th St, San Francisco, California, USA 📍On-site participation is by invitation only. Travel and accommodation expenses will not be covered. 🧑‍💻 Developers, builders, and innovators - bring your boldest ideas and let’s turn them into real AI systems on AMD.

⏱️ Build the next intelligent 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. 🧑‍💻 Apply now to design, prototype, and showcase enterprise-ready innovation.

⏱️ 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.

⏱️ 4 weeks to design and ship a working Web3 product or AI agent. 📅 February 4 to March 1, 2026 • Feb 4 – Hackathon opens. Teams can start building and submitting. • Feb 5 to March 1 – Build, test, and iterate. • March 1 – Final submissions due. 🚨 Important eligibility reminder To be eligible for prizes, teams must post their submission video on X and tag @lablabai and @Surgexyz_. 🤝 Build solo or with a team. 💰 $50,000 prize pool in $SURGE Token. ⚠️ Self-service hackathon This is a fully self-service hackathon. There are no live streams, kickoff sessions, or scheduled workshops. Participants can register, build, and submit at any time before the submission deadline.