Bittensor Subnet for crypto prediction, including TAO, BTC, ETH so on...
CrowdShield is an AI-powered crowd and disaster management system offering real-time zone monitoring, predictive analytics, and dynamic alerting. Designed for public safety and event control, it features a modular dashboard, multilingual support, and scalable frontend architecture.
Advanced Enemy AI System for Unity (C#). Features a Modular Behavior Tree, Custom A* Predictive Ambush, and Audio-Driven state machines.
FluxStation is a high-performance Industrial IoT platform that bridges the gap between mechanical hardware and software intelligence. It transforms raw sensor data from physical machinery into a 3D Digital Twin, using Python-driven analytics to predict mechanical failures before they occur.
An intelligent full-stack parking management system featuring predictive AI slot selection, dynamic pricing logic, and a premium dashboard with multiple custom UI themes. Built with React and Node.js.
Neural world models for autonomous robots—learning from experience, imagining multiple futures, estimating uncertainty, and planning safe trajectories with Neural-MPC, MPPI, occupancy prediction, Transformer world models, and ROS 2.
AI-powered rural home care system for emergency response, monitoring, and longitudinal risk intelligence.
Interactive futuristic intelligence system generating multi-section predictive reports using the Claude AI API.
SaaS Full-Stack de gestão financeira B2B. Integra arquitetura escalável e IA preditiva para análise de fluxo de caixa em tempo real.
FlowAI is a predictive, signal-free traffic coordination system that replaces stop-based signals with dynamic speed and lane assignments. Utilizing real-time trajectory prediction and deterministic conflict optimization, it eliminates intersection waiting periods for continuous vehicle flow.
Predictive AI Agent Memory & Second Brain — trusted long-term memory, vector search, temporal knowledge graph, decision intelligence, and MCP.
Learn predictive AI with C# and ML.NET by predicting house prices with the Ames Housing dataset. Profile the data, analyze correlations, build a baseline, add numeric and categorical features, compare experiments, use AutoML, evaluate errors, save the model, and make predictions.
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