Unlock the Power of Modern C#—From Your First Line of Code to Building Custom Objects!

Are you ready to master one of the most powerful and versatile programming languages in the world, but don't know where to start? Do you find other programming books too abstract or outdated? The C# Foundations is your practical, hands-on guide to learning C# from the ground up, designed specifically for the modern developer.

This book isn't just about syntax; it's about building a rock-solid mental model of how software works. Through clear explanations, real-world analogies, and step-by-step examples, you will journey from writing your first "Hello, World!" to designing your own custom data types with classes. This volume lays the essential groundwork for advanced topics like AI, cloud development with Azure, and game engineering with Unity.

Inside This Volume, You Will Discover:

• Solid Foundations: Grasp the core of the .NET ecosystem and understand how your code comes to life.
• Data Mastery: Learn to store, manage, and manipulate data using variables, arrays, and dynamic lists (List).
• Control Flow Logic: Build intelligent programs that can make decisions with if/else statements and perform repetitive tasks with loops (for, while, foreach).
• Code Reusability: Master the art of writing clean, reusable code by defining your own methods with parameters and return values.
• Object-Oriented Thinking: Dive into the heart of C# by learning to define your own blueprints (classes) with fields, properties, and constructors to model any real-world entity.
• Memory Management Essentials: Understand the crucial difference between the Stack and the Heap, a concept that separates amateur coders from professional developers.

Who Is This Book For?

Whether you're a complete beginner with zero programming experience, a student in a computer science course, or a developer transitioning from another language like Python or JavaScript, this book provides the structured, foundational knowledge you need to succeed. Each chapter builds logically on the last, ensuring you never feel lost.

Don't just learn to code—learn to think like a modern developer. Your journey to becoming a proficient C# programmer starts now.

Get the complete discounted "C# & Ai Masterclass" 10-Volumes set!

Table of contents

Chapter 1: The .NET Runtime & 'Hello World' - Understanding the Console

Chapter 2: Variables & Primitives - Integers, Doubles, and Storing Values

Chapter 3: Text Handling - Strings, Concatenation, and Interpolation

Chapter 4: Basic Math - Operators, Arithmetic, and Precedence

Chapter 5: Interacting with Humans - Console.ReadLine and Conversion

Chapter 6: Making Decisions - Booleans, Comparison, and 'if/else'

Chapter 7: Complex Decisions - Logical Operators (&&, ||) and Switch Statements

Chapter 8: The Loop of Logic - 'while' and 'do-while' Iterations

Chapter 9: The Counted Loop - Mastering the 'for' Loop

Chapter 10: Debugging & Logic - Finding Errors and Stepping Through Code

Chapter 11: Arrays - Storing Fixed Sequences of Data (Vectors)

Chapter 12: Iterating Data - The 'foreach' Loop and Array Traversal

Chapter 13: Defining Methods - Writing Reusable Code Blocks (Void)

Chapter 14: Data Flow - Parameters, Arguments, and Return Values

Chapter 15: Scope & Memory - Stack vs Heap and Variable Lifetime

Chapter 16: The Class Blueprint - Defining Custom Types

Chapter 17: Objects & State - Fields, Properties, and Encapsulation

Chapter 18: Constructors - strict Object Initialization

Chapter 19: Static vs Instance - Shared Data vs Individual Data

Chapter 20: Introduction to Lists - Moving beyond Fixed Arrays

Chapter 21: Math for AI - Using System.Math for Sigmoids and Distance

Chapter 22: Data Normalization - Converting Ranges (Loops + Math)

Chapter 23: Text Processing - Splitting Strings and Basic Tokenization

Chapter 24: File I/O - Saving and Loading Training Data (Simple Text)

Chapter 25: Capstone Project - Building a Rule-Based NLP Chatbot

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Ready to bridge the gap between foundational C# and professional AI development?

Many developers master the basics of object-oriented programming but struggle to build the complex, resilient, and high-performance systems required for modern Artificial Intelligence. Simple class hierarchies and basic memory management are not enough when you're dealing with massive datasets, GPU resources, and asynchronous API calls. This is where most developers get stuck.

Volume 2 of the C# and .NET Masterclass Series is your comprehensive guide to moving beyond the fundamentals. This book is meticulously crafted for intermediate C# developers who are ready to design and build the architectural backbone of production-ready AI applications. You will learn not just the "what" but the "why" behind the advanced patterns that separate hobby projects from enterprise-grade AI systems.

Inside, you will master:

• Advanced Polymorphism: Design swappable AI backends to seamlessly switch between cloud services like OpenAI and local models.
• Type-Safe AI Pipelines: Build robust data processing chains with C# Generics and constraints, eliminating runtime errors and ensuring data integrity.
• Master Memory Management: Learn to control GPU memory and other unmanaged resources with the IDisposable pattern, a critical skill for preventing leaks in high-performance tensor operations.
• Functional Programming in C#: Leverage Delegates, Lambda Expressions, Func<>, and Action<> to create dynamic, plugin-based architectures that are clean, modular, and easy to extend.
• Immutable Data Structures: Use C# Records to design thread-safe chat histories and data objects, a cornerstone of reliable concurrent systems.
• Fluent API Design: Apply Extension Methods to create elegant, chainable interfaces for your AI and tensor operations, making your code more readable and maintainable.
• Robust API Communication: Master JSON serialization and build resilient clients that can handle API timeouts, rate limits, and other common network failures gracefully.

This is more than a coding manual—it's an architectural playbook. Each chapter builds upon the last, culminating in a capstone project where you will design a complete, plugin-based chatbot architecture that applies all the principles you've learned.

Whether you're a .NET developer breaking into AI, an engineer transitioning from Python, or a student ready to build real-world systems, this book will give you the tools and confidence to architect sophisticated AI applications in C#.

Unlock the power of .NET for AI.

Check also the other books in this series

Table of contents

Chapter 1: Inheritance - Creating a Base 'Model' Class

Chapter 2: Polymorphism - Swapping Inference Engines (Virtual/Override)

Chapter 3: Abstract Classes vs Interfaces - The 'ILanguageModel' Contract

Chapter 4: Composition - Agents Having Tools vs Being Tools

Chapter 5: The Ultimate Base Class - System.Object and Boxing

Chapter 6: Enums and Flags - Managing Agent States (Idle, Thinking, Error)

Chapter 7: Generics <T> - Creating Reusable Data Pipelines

Chapter 8: Generic Constraints - Enforcing Type Safety in ML Pipelines

Chapter 9: Records - Immutable Data for Chat History (Messages)

Chapter 10: Nullable Reference Types - Handling Hallucinations and Empty Responses

Chapter 11: Delegates - Callbacks for Token Streaming

Chapter 12: Lambda Expressions - Inline Logic for Data Filtering

Chapter 13: Events - Reacting to 'ModelFinishedThinking' Signals

Chapter 14: Func<> and Action<> - Passing Logic as Parameters

Chapter 15: Extension Methods - Building Fluent Interfaces for AI Chains

Chapter 16: Exception Handling - Managing API Timeouts and Rate Limits

Chapter 17: File I/O - Saving and Loading Conversation Contexts

Chapter 18: Serialization (JSON) - Communicating with OpenAI/REST APIs

Chapter 19: Garbage Collection & IDisposable - Cleaning up GPU/Memory Resources

Chapter 20: Capstone Project - Building a Plugin-Based Chatbot Architecture

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Unlock the high-performance C# skills that power modern Artificial Intelligence.

Are you a C# developer ready to move beyond standard application development and dive into the world of high-performance data manipulation and AI? This book is your essential guide to bridging the gap between traditional collections and the powerful vector embeddings that are the lifeblood of today's neural networks.

In Volume 3 of the C# & AI Masterclass series, we leave the basics behind and explore the internal mechanics of .NET's most powerful data structures. You won't just learn how to use a Dictionary; you'll understand the hashing, collision resolution, and memory management that make it fast. You won't just write a LINQ query; you'll build clean, declarative, and scalable data pipelines that are the foundation of professional AI development.

What you will master in this volume:

• Deep Dive into Data Structures: Go under the hood of List, Dictionary, and HashSet to write highly efficient code.
• LINQ for Functional Data Pipelines: Master Method and Query syntax to transform data declaratively. Learn the secrets of deferred execution and how to avoid common performance traps.
• Zero-Allocation Memory Management: Harness the power of Memory and Span to slice and process large datasets without triggering the garbage collector—a critical skill for real-time AI.
• The Path to Embeddings: Understand the journey from raw collections to the structured vectors that AI models require, and learn how to prepare your data for the world of machine learning.
• Practical, AI-Focused Examples: Build components of real-world AI systems, including tokenizer vocabularies, graph traversal algorithms, and data normalization pipelines.

This book is for intermediate C# developers who are serious about performance and want to apply their skills to the exciting and rapidly growing field of Artificial Intelligence. If you're ready to stop just using collections and start commanding them, this book is your next step.

Start your journey from collections to embeddings today and transform the way you handle data in C#.

Check also the other books in this series

Table of contents

Chapter 1: Lists, Dictionaries, and HashSets in Depth

Chapter 2: Custom Collections and Enumerators

Chapter 3: Introduction to Vectors - Arrays vs Tensors

Chapter 4: Immutable Collections for Thread-Safe Data

Chapter 5: Memory<T> and Span<T> - Zero Allocation Slicing

Chapter 6: LINQ Method Syntax vs Query Syntax

Chapter 7: Filtering and Projection (Select/Where)

Chapter 8: Sorting and Grouping Complex Data

Chapter 9: Parallel LINQ (PLINQ) for Big Data

Chapter 10: Building a Custom LINQ Provider

Chapter 11: System.Numerics.Tensors - The Foundation of AI

Chapter 12: Calculating Cosine Similarity with C#

Chapter 13: Matrix Multiplication Basics in .NET

Chapter 14: Distance Algorithms (Euclidean vs Manhattan)

Chapter 15: Normalizing Data for Machine Learning

Chapter 16: Parsing CSV/JSON Datasets for Fine-Tuning

Chapter 17: Tokenization Basics - Counting Words vs Tokens

Chapter 18: Streaming Data Processing with IAsyncEnumerable

Chapter 19: Handling Missing Data in Datasets

Chapter 20: Project - Building a Keyword Extraction Engine with LINQ

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Master High-Performance C# to Build the Next Generation of AI-Powered Applications

The AI revolution is here, but is your C# code ready for it? Modern AI systems, powered by Large Language Models (LLMs), demand more than just functional code—they require high-throughput, low-latency, and massively concurrent architectures. A simple blocking API call or an unhandled race condition can bring a sophisticated AI pipeline to its knees. This book is your definitive guide to mastering the advanced asynchronous and parallel programming techniques required to build production-grade AI systems in .NET.

Moving far beyond basic async/await, this volume deconstructs the patterns and primitives that power scalable software. Through practical examples, deep architectural insights, and a comprehensive capstone project, you will learn how to architect resilient pipelines that can ingest, process, and stream data efficiently, all while maintaining a responsive user experience.

Inside, you will discover how to:

• Architect Non-Blocking AI Pipelines: Master the `async/await` state machine and understand the critical difference between CPU-bound and I/O-bound work to eliminate performance bottlenecks.
• Stream LLM Responses in Real-Time: Implement the "typewriter effect" using IAsyncEnumerable<T>, providing users with instant feedback from generative AI models.
• Manage API Rate Limits and Backpressure: Use SemaphoreSlim and System.Threading.Channels to build resilient systems that gracefully handle API throttling and prevent memory exhaustion when your AI generates data faster than your UI can handle it.
• Process Data in Parallel: Leverage Parallel.ForEachAsync and the Scatter-Gather pattern (Task.WhenAll) to concurrently process large batches of documents for embeddings, dramatically reducing ETL times for RAG systems.
• Ensure Thread Safety: Protect your application from data corruption with locks, monitors, and concurrent collections, a critical skill for any multi-threaded AI application.
• Write Deterministic Tests for Async Code: Learn how to mock non-deterministic AI behavior to create fast, reliable unit and integration tests for your asynchronous pipelines.
• Build a Complete RAG Ingestion Engine: Apply all the concepts in a capstone project where you build a high-throughput ETL system from scratch—the foundational component for any modern semantic search or question-answering application.

This book is for intermediate to advanced C# developers and architects who are ready to move beyond theory and build the scalable, high-performance backend systems that the AI era demands. If you're tired of fighting with unresponsive applications, memory leaks, and flaky tests in your async code, this is the guide you've been waiting for.

Unlock the full potential of .NET and start building the next generation of intelligent, responsive, and resilient AI services today.

Check also the other books in this series

Table of contents

Chapter 1: The Cost of Latency - CPU vs I/O Bound in AI Inference

Chapter 2: The State Machine - How 'async' and 'await' really work

Chapter 3: Task vs ValueTask - Optimizing Memory in Hot Loops

Chapter 4: Context and Deadlocks - ConfigureAwait(false) in Libraries

Chapter 5: Converting Legacy Sync Code to Async Patterns

Chapter 6: From Lists to Streams - Introduction to IAsyncEnumerable<T>

Chapter 7: The 'await foreach' Loop - Consuming Data Asynchronously

Chapter 8: Streaming LLM Tokens - Implementing the 'Typewriter Effect'

Chapter 9: System.Threading.Channels - Building Producer/Consumer AI Pipelines

Chapter 10: Handling Backpressure - When the AI Generates Faster than the UI

Chapter 11: Concurrency vs Parallelism - Managing Threads in .NET

Chapter 12: Parallel.ForEachAsync - Batch Processing Embeddings efficiently

Chapter 13: The Scatter-Gather Pattern - Querying Multiple Models Simultaneously (Task.WhenAll)

Chapter 14: Thread Safety - Locks, Monitors, and Concurrent Collections

Chapter 15: Throttling - Using SemaphoreSlim to Respect API Rate Limits

Chapter 16: The CancellationToken - Stopping an Hallucinating Model Mid-Stream

Chapter 17: Timeouts and Delays - Avoiding Forever-Hanging Requests

Chapter 18: Exception Handling in Async Tasks - Unwrapping AggregateException

Chapter 19: Testing Async Code - Deterministic Testing for Non-Deterministic AI

Chapter 20: Capstone Project - Building a High-Throughput Async Document Ingestion Engine (ETL for RAG)

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Unlock the Future of Application Development by Mastering AI Backend APIs with ASP.NET Core!

The AI revolution is here, but powerful models are useless without robust, scalable, and secure backend infrastructure to serve them. This book is the definitive guide for experienced .NET developers looking to bridge the gap between traditional web development and the high-performance demands of modern AI applications. Move beyond simple CRUD APIs and learn to build the backbone of the next generation of intelligent software.

Written for production-focused engineers, this comprehensive volume deconstructs every critical component of an enterprise-grade AI Web API. You will progress from foundational architectural decisions to advanced real-time communication patterns, culminating in the creation of a complete backend for a ChatGPT-clone.

Inside, you will master:

• High-Performance Architecture: Learn to choose between Controllers and Minimal APIs and leverage Dependency Injection to manage the lifecycle of heavy AI models, preventing costly memory leaks and performance bottlenecks.
• Real-Time Streaming: Build incredibly responsive user experiences by streaming LLM tokens and audio data with Server-Sent Events (SSE), SignalR, and low-level WebSockets.
• Efficient Microservices Communication: Supercharge your internal service communication by replacing slow REST/JSON calls with high-throughput gRPC and Protocol Buffers.
• Production-Grade Resilience: Fortify your application against network failures and API outages by implementing essential resilience patterns like Retries and Circuit Breakers with Polly.
• Security & Cost Control: Secure your endpoints with JWTs and API Keys, manage costs with intelligent Hybrid Caching, and protect your resources from abuse with sophisticated, policy-based Rate Limiting.
• Complete Observability: Instrument your entire application with OpenTelemetry to gain deep insights into traces, metrics, and structured logs, making it easy to diagnose latency and errors in a distributed system.
• OpenAI Compatibility: Learn to create OpenAI-compatible plugin specifications, allowing your API to be seamlessly integrated into the broader AI ecosystem.

This is not a theoretical overview; it is a practical, hands-on guide packed with code examples, architectural diagrams, and best practices forged in real-world scenarios. By the time you finish this book, you will have the skills and confidence to design, build, and deploy AI backend APIs that are not only intelligent but also scalable, secure, and resilient.

Stop just using AI—start building the platforms that power it. Grab your copy and become a leader in the new era of software development!

Check also the other books in this series

Table of contents

Chapter 1: Anatomy of an ASP.NET Core Project

Chapter 2: Controllers vs Minimal APIs - Performance Choices

Chapter 3: Dependency Injection (DI) Service Lifetimes

Chapter 4: Middleware Pipelines and Request Handling

Chapter 5: Configuration and Options Pattern (Managing API Keys)

Chapter 6: Server-Sent Events (SSE) for Streaming LLM Tokens

Chapter 7: SignalR - Building Real-Time Chat Channels

Chapter 8: WebSockets - Low Latency Audio Streaming

Chapter 9: gRPC for High-Performance Inter-Service AI Calls

Chapter 10: Background Services (IHostedService) for Model Loading

Chapter 11: Consuming OpenAI/Azure APIs with HttpClientFactory

Chapter 12: Resilience Patterns - Retries and Circuit Breakers (Polly)

Chapter 13: Caching Responses to Save API Costs (HybridCache)

Chapter 14: Rate Limiting Users in AI Applications

Chapter 15: Creating an OpenAI-Compatible Plugin Specification

Chapter 16: Authentication (JWT) & API Key Management

Chapter 17: Logging & Telemetry with OpenTelemetry

Chapter 18: API Documentation with Swagger/OpenAPI

Chapter 19: Error Handling and ProblemDetails

Chapter 20: Capstone - Building a 'ChatGPT-Clone' Backend API

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Transform Your Data Layer into the Brain of Your AI Application

The era of "dumb" databases is over. In the age of Generative AI, your application's data access layer must do more than just CRUD operations—it must understand meaning, manage context, and fuel intelligent decisions. Volume 6 of the C# & AI Masterclass is the definitive guide for .NET developers who need to bridge the gap between traditional Entity Framework Core architectures and the cutting-edge world of Vector Databases and Retrieval-Augmented Generation (RAG).

Unlock the Power of Intelligent Data Access

This book moves beyond simple tutorials. It dives deep into the architectural patterns required to build enterprise-grade AI systems. You will learn how to hybridize your SQL databases with vector search capabilities, ensuring your AI applications are not only smart but also scalable, secure, and transactional.

What's Inside:

• Vector Integration in EF Core: Learn to store, index, and query high-dimensional embeddings using PostgreSQL (pgvector) and SQL Server 2022 without leaving the comfort of C#.
• Building RAG Pipelines: Architect robust retrieval systems that combine the precision of SQL keywords with the semantic understanding of vector search (Hybrid Search).
• AI Memory Management: Design schemas for chat history that allow LLMs to "remember" past interactions while enforcing permissions and context limits.
• Advanced Concurrency & Transactions: Solve the "split-brain" problem of syncing relational data with vector stores using Unit of Work patterns and Compensating Transactions.
• SaaS Multi-Tenancy: Implement strict data isolation for AI models in multi-tenant environments using Row-Level Security (RLS) and dynamic DbContexts.
• Auditing & Observability: Use EF Core Interceptors to create an immutable audit trail of every prompt and context retrieved by your AI.

Whether you are building a semantic search engine, an intelligent chatbot, or an automated document analysis system, this book provides the blueprint for your data infrastructure.

Stop building prototypes. Start architecting intelligent systems with C# and EF Core today.

Check also the other books in this series

Table of contents

Chapter 1: ORM Basics - Context, Models, and SQL

Chapter 2: Code-First Migrations and Database Design

Chapter 3: Relationships (One-to-Many, Many-to-Many)

Chapter 4: Querying with LINQ to SQL

Chapter 5: Change Tracking and Saving Data

Chapter 6: Introduction to Vector Stores - Why SQL isn't enough

Chapter 7: Using pgvector with PostgreSQL and EF Core

Chapter 8: Storing Embeddings (List<float>) in SQL Server

Chapter 9: Implementing Nearest Neighbor Search (ANN)

Chapter 10: Hybrid Search - Combining Keywords + Vectors

Chapter 11: Designing a Schema for Chat History

Chapter 12: Storing Structured Logs from LLM Chains

Chapter 13: Concurrency Control in Multi-User Chat

Chapter 14: Transactions and Rollbacks

Chapter 15: Performance Tuning - Indexing Vector Columns

Chapter 16: Repository Pattern vs Direct Context

Chapter 17: Multi-Tenancy for SaaS AI Apps

Chapter 18: Seeding Databases with Synthetic Data

Chapter 19: Interceptors - Auditing AI Prompts

Chapter 20: Capstone - Building a Semantic Search Engine for Documentation

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Architect the Future of Intelligent Systems with C# and Kubernetes

The era of the monolithic AI script is over. To build the next generation of enterprise software, you must stop treating AI agents as experimental toys and start treating them as resilient, scalable cloud-native microservices.

Cloud-Native AI Agents with C# is the definitive guide for .NET architects and developers who need to operationalize Artificial Intelligence at scale. This advanced volume moves beyond the basics of API calls, taking you deep into the engineering challenges of orchestrating distributed agent swarms.

In this book, you will not just write code; you will design systems. You will learn how to decouple cognitive logic from execution environments, manage expensive GPU resources efficiently, and build self-healing infrastructure that adapts to bursty inference workloads.

What You Will Build and Master:

• Containerization Strategies: Master multi-stage builds to package Python/C# hybrid agents into secure, lightweight containers.
• Advanced Orchestration: Use Kubernetes Operators and StatefulSets to manage the complex lifecycle of stateful agents.
• Event-Driven Scaling (KEDA): Implement autoscaling architectures that react to RabbitMQ queue depth and Kafka lag, ensuring your agents scale to zero and burst to infinity.
• High-Performance Inference: Implement dynamic batching, GPU partitioning, and asynchronous pipelines to maximize hardware ROI.
• Service Mesh Integration: Secure your agent swarm with Istio, enabling mTLS, circuit breaking, and canary deployments for new model versions.
• Capstone Project: Combine every concept to build a production-grade Distributed RAG (Retrieval-Augmented Generation) Pipeline from scratch.

Whether you are building a customer support bot that handles thousands of concurrent users or a complex multi-agent system for automated reasoning, this book provides the architectural patterns, C# code samples, and Kubernetes manifests you need to succeed.

Stop scripting. Start engineering. Scale your intelligence.

Check also the other books in this series

Table of contents

Chapter 1: Foundations of Cloud-Native AI: From Monoliths to Microservices

Chapter 2: Your First Agent: Containerizing a Simple C# Microservice

Chapter 3: The AI Agent as a Microservice: Core Principles and Design Patterns

Chapter 4: Advanced Containerization: Optimizing Runtimes for AI Workloads

Chapter 5: Scaling Principles: Latency, Throughput, and State Management

Chapter 6: Architecting for Inference: The Role of C# and Modern .NET

Chapter 7: Stateful Intelligence: Managing Agent Lifecycles with Kubernetes Operators

Chapter 8: Building the Control Plane: Agent Orchestration in C#

Chapter 9: The Distributed Nervous System: Inter-Agent Communication Patterns

Chapter 10: Dynamic Scaling: Orchestration with Kubernetes Autoscalers

Chapter 11: Scaling Inference Pipelines: From Theory to Practice

Chapter 12: High-Throughput Inference: Implementing Asynchronous Pipelines

Chapter 13: The Agent's Toolbox: Integrating External Services with MCP and .NET

Chapter 14: High-Performance Patterns: GPU Resource Management and Batching

Chapter 15: Scaling Inference Workloads: From HPA to Event-Driven Autoscaling

Chapter 16: Production-Ready Agents: CI/CD and Zero-Downtime Model Updates

Chapter 17: Infrastructure as Code: Managing Agent Deployments with Kubernetes Operators

Chapter 18: The Service Mesh: Resilience and Observability with Istio

Chapter 19: Persistent Intelligence: Managing Model Weights with Kubernetes Storage

Chapter 20: Orchestrating Agent Swarms: High-Throughput Inference with KEDA

Chapter 21: Advanced Orchestration: Building Custom Controllers and Schedulers

Chapter 22: Operationalizing Agent Lifecycles: Health Checks and Graceful Shutdowns

Chapter 23: Architecting Distributed Inference: Dynamic Batching and Stateful Orchestration

Chapter 24: From Agent to Swarm: Managing State and Communication at Scale

Chapter 25: Advanced Orchestration: GPU Partitioning and Stateful Agent Swarms

Chapter 26: Capstone Project: Building a Distributed Retrieval-Augmented Generation (RAG) Pipeline

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Unlock the Power of Agentic AI with C# and Microsoft Semantic Kernel

Are you a .NET developer ready to evolve from building static applications to architecting cognitive systems? The Core of AI Engineering is your definitive guide to mastering the Microsoft Semantic Kernel SDK.

This volume goes far beyond simple chatbots. It creates a bridge between the deterministic world of C# code and the probabilistic reasoning of Large Language Models (LLMs). Through rigorous theoretical foundations and production-ready code examples, you will learn to build Autonomous Agents that can plan, execute, and adapt.

What's Inside:

• Deep Architecture: Master Dependency Injection, Connector Patterns, and the Kernel architecture to build modular, testable AI systems.
• Native & Semantic Plugins: Teach your AI to interact with the real world by converting C# methods and APIs into intelligent tools.
• Advanced Planning: Utilize Stepwise and Handlebars Planners to enable auto-orchestration of complex, multi-step workflows.
• RAG & Memory: Build sophisticated Retrieval-Augmented Generation pipelines using Vector Stores and Kernel Memory to ground AI in your data.
• Robust Engineering: Implement Filters, Hooks, and Circuit Breakers to handle hallucinations, enforce security, and ensure reliability.
• Multi-Agent Systems: Orchestrate teams of specialized AI personas to solve complex problems collaboratively.
• Capstone Project: Build a fully functional, desktop-native "Jarvis" assistant that interacts with Windows.

Whether you are connecting to Azure OpenAI or running local models via Ollama, this book provides the engineering patterns necessary to build enterprise-grade AI software. Stop writing scripts; start engineering intelligence.

Perfect for Senior Developers and Software Architects looking to define the future of .NET development.

Get the complete discounted "C# & Ai Masterclass" 10-Volumes set!

Table of contents

Chapter 1: Introduction to Semantic Kernel (SK) for .NET

Chapter 2: Configuring the Kernel - Azure OpenAI vs Ollama

Chapter 3: Prompt Templates and Semantic Functions

Chapter 4: The ChatCompletion Service

Chapter 5: Connector Handling and Dependency Injection

Chapter 6: Native Functions - Calling C# Code from LLMs

Chapter 7: Converting Existing APIs into Plugins

Chapter 8: The Handlebars Template Engine for Prompts

Chapter 9: Prompt Engineering Techniques in C# (Few-Shot, CoT)

Chapter 10: YAML Prompts and Serialization

Chapter 11: The Planner Concept - Auto-Orchestration

Chapter 12: Stepwise Planner vs Handlebars Planner

Chapter 13: Creating Autonomous Agents with Loops

Chapter 14: Multi-Agent Systems - The Persona Pattern

Chapter 15: Handling Hallucinations and Errors in Plans

Chapter 16: TextMemory and Volatile Memory Store

Chapter 17: Building a RAG Pipeline with Kernel Memory

Chapter 18: Chunking Strategies for PDF/Text

Chapter 19: Filters and Hooks - Intercepting Agent Thoughts

Chapter 20: Capstone - Building a Full 'Jarvis' Assistant for Windows

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Unleash the Power of Local AI with C# and .NET

Are you a C# developer looking to integrate cutting-edge AI without relying on expensive, slow, and privacy-invading cloud APIs? Book 9: Edge AI & Local Inference is the definitive guide to running Large Language Models (LLMs), Computer Vision, and Audio models directly on your user's hardware.

Move beyond Python wrappers. This volume teaches you how to architect high-performance, native .NET solutions using ONNX Runtime, LlamaSharp, and Microsoft.ML. You will learn to build applications that are offline-capable, lightning-fast, and completely private.

What's Inside:

• Local LLM Integration: Run Llama 3, Phi-3, and Mistral locally using quantized models (GGUF/ONNX).
• Offline RAG Systems: Build a private "Chat with your Data" pipeline using local vector databases and embedding models.
• Hardware Acceleration: Optimize inference using CUDA, DirectML, and NPUs directly from C#.
• Real-Time Vision & Audio: Implement YOLOv8 object detection and Whisper transcription with zero latency.
• Professional Architecture: Master asynchronous streaming, memory management for VRAM, and thread-safe UI integration (WPF/WinForms).

Whether you are building a smart IoT gateway, a privacy-focused desktop tool, or a high-throughput local server, this book provides the production-ready code and architectural patterns you need.

Stop paying per token. Start building on the Edge.

Table of contents

Chapter 1: Cloud vs Local - Privacy, Latency, and Cost

Chapter 2: Understanding Model Formats - ONNX vs GGUF

Chapter 3: Quantization Explained (FP16, INT8, INT4)

Chapter 4: Hardware Acceleration - CUDA, DirectML, and NPUs

Chapter 5: Setting up the Local Environment

Chapter 6: Introduction to LlamaSharp

Chapter 7: Loading GGUF Models (Llama 3, Phi-3)

Chapter 8: Managing Context Windows Locally

Chapter 9: Streaming Inference to the Console

Chapter 10: Stateful Chat Sessions in Local Memory

Chapter 11: Introduction to Microsoft.ML

Chapter 12: Running BERT for Text Classification

Chapter 13: Object Detection with YOLO and ONNX

Chapter 14: Text-to-Speech (TTS) with Local Models

Chapter 15: Whisper.net - Local Audio Transcription

Chapter 16: Integrating AI into WPF/Windows Forms

Chapter 17: Background Processing without Freezing UI

Chapter 18: Offline RAG - Querying Local Files

Chapter 19: Fine-Tuning Basics (LoRA concepts)

Chapter 20: Capstone - Building a Private, Offline Coding Assistant

If printed, this ebook would span over 350 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Check also the other books in this series

Table of contents

Chapter 1: Cloud vs Local - Privacy, Latency, and Cost

Chapter 2: Understanding Model Formats - ONNX vs GGUF

Chapter 3: Quantization Explained (FP16, INT8, INT4)

Chapter 4: Hardware Acceleration - CUDA, DirectML, and NPUs

Chapter 5: Setting up the Local Environment

Chapter 6: Introduction to LlamaSharp

Chapter 7: Loading GGUF Models (Llama 3, Phi-3)

Chapter 8: Managing Context Windows Locally

Chapter 9: Streaming Inference to the Console

Chapter 10: Stateful Chat Sessions in Local Memory

Chapter 11: Introduction to Microsoft.ML

Chapter 12: Running BERT for Text Classification

Chapter 13: Object Detection with YOLO and ONNX

Chapter 14: Text-to-Speech (TTS) with Local Models

Chapter 15: Whisper.net - Local Audio Transcription

Chapter 16: Integrating AI into WPF/Windows Forms

Chapter 17: Background Processing without Freezing UI

Chapter 18: Offline RAG - Querying Local Files

Chapter 19: Fine-Tuning Basics (LoRA concepts)

Chapter 20: Capstone - Building a Private, Offline Coding Assistant

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.

Unlock the Raw Power of .NET for AI and High-Frequency Computing

Are you hitting the performance wall in your C# applications? Do Garbage Collection pauses kill your real-time AI inference latency? In the age of Large Language Models, standard coding practices aren't enough. You need to go deeper.

High-Performance C# for AI is not a beginner's guide—it is a masterclass in squeezing every nanosecond of performance out of the .NET runtime. This volume bridges the gap between high-level application development and low-level systems programming, equipping you with the tools to build blazing-fast tokenizers, tensor processors, and inference engines.

In this volume, you will master:

• Zero-Allocation Architectures: Stop feeding the Garbage Collector. Learn to use Span<T>, Memory<T>, and ref structs to process massive data streams with zero heap allocations.
• SIMD & Vectorization: Unleash the full potential of your CPU. Implement hardware-accelerated math (AVX2/AVX-512) for operations like Cosine Similarity and Softmax, achieving 10x speedups over standard loops.
• Advanced Memory Management: Dive into the internals of the GC, the Large Object Heap (LOH), and learn how to use ArrayPool and stackalloc to manage memory manually and safely.
• Unsafe Code & Interop: Learn the art of using pointers, the fixed statement, and memory pinning to interface directly with native libraries and raw memory buffers.
• Scientific Profiling: Move beyond guesswork. Use BenchmarkDotNet and dotnet-trace to measure, diagnose, and prove your performance gains with statistical rigor.

Whether you are building a local LLM runner, a high-frequency trading bot, or a real-time data processing pipeline, this book provides the architectural patterns and low-level techniques required to compete with C++ and Rust.

Stop waiting for the Garbage Collector. Take control of your memory. Build the next generation of AI infrastructure in C#.

Check also the other books in this series

Table of contents

Chapter 1: The Cost of Allocation - Measuring GC Pressure in AI Loops

Chapter 2: Span<T> - The Universal View for Zero-Allocation Slicing

Chapter 3: Memory<T> - The Asynchronous & Heap-Stable Counterpart

Chapter 4: ReadOnlySpan<char> - High-Performance String and Token Processing

Chapter 5: Renting Memory - ArrayPool<T> and Reusable Buffers

Chapter 6: Parallelism on a Single Core - Introduction to SIMD with Vector<T>

Chapter 7: Vectorizing Math - Writing Custom High-Performance Vector Operations

Chapter 8: The 'unsafe' Context - When and How to Use Pointers

Chapter 9: The 'fixed' Statement - Pinning Managed Memory for Native Interop

Chapter 10: stackalloc - Blazing-Fast, Temporary Memory on the Stack

Chapter 11: GC Internals - Generations, LOH, and Concurrent Collection

Chapter 12: Structs vs. Classes - A Performance Deep Dive

Chapter 13: The 'ref struct' Pattern - Enforcing Stack-Only Lifetime

Chapter 14: Avoiding Defensive Copies - 'in' Parameters and 'readonly' Members

Chapter 15: Low-Latency GC - Tuning the Runtime for Real-Time Inference

Chapter 16: The Art of Measurement - Mastering BenchmarkDotNet

Chapter 17: Profiling in Production - Using dotnet-trace and dotnet-counters

Chapter 18: Case Study - Optimizing a GPT-2 Tokenizer from Scratch

Chapter 19: Case Study - Accelerating Cosine Similarity with SIMD and Span<T>

Chapter 20: Capstone - Building a High-Performance Inference Pipeline for a Local LLM

If printed, this ebook would span over 300 pages. Each chapter is structured into theoretical foundations, an annotated basic example, an annotated advanced example, and five coding exercises based on real-world scenarios with complete solutions.