Steps to get started with EdgeAI for Beginners: Fork and clone the repository, join the Azure AI Foundry Discord.
Course covers Edge AI from fundamentals to production, including Small Language Models (SLMs), hardware optimization, real-time inference, and deployment strategies.
Edge AI benefits: Privacy & security, real-time performance, cost efficiency, resilient operations, and regulatory compliance.
Edge AI defined: Running AI locally on devices for low latency, privacy, and offline capability.
SLMs like Phi-4, Mistral-7B, and Gemma are optimized for edge deployment with reduced memory and compute demands.
Course modules include Introduction to EdgeAI, SLM Model Foundations, Deployment Practice, Model Optimization Toolkit, SLMOps Production, AI Agents, Platform Implementation, and Foundry Local Toolkit.
Hands-on projects: Local AI chat apps, RAG pipelines, multi-agent systems, model routers, and API frameworks.
The Golden Seed Fractal is a 96-fold self-similar visualization of the Atlas of Resonance Classes.
The Atlas of Resonance Classes is a 96-vertex graph that serves as the foundation for constructing all five exceptional Lie groups (G₂, F₄, E₆, E₇, E₈) through categorical operations.
The Golden Seed Vector is a 96-dimensional configuration in the E₈ root system, encoding the full exceptional group hierarchy.
The fractal exhibits unique properties: 96-fold self-similarity, fractal dimension ≈4.155, and 8-fold rotational symmetry.
Applications span quantum computing, AI, physics, and decentralized systems, providing structured embedding spaces and symmetry frameworks.
The project is implemented in Rust with exact arithmetic, formal verification in Lean 4, and comprehensive documentation.
The Atlas is derived from first principles as the stationary configuration of an action functional, ensuring mathematical correctness.
The crate is designed for rigorous peer review, with all mathematical claims verifiable from code and tests serving as formal proofs.