.NET AI Engineering — No Python Required
Structured learning paths, end-to-end projects, and verified debugging fixes. Built by developers who ship AI-powered .NET to production.
51 production-verified guides · Written by a Senior .NET Engineer with 13+ years experience
Structured Learning Paths
Follow a guided path from beginner concepts to production-ready skills. Each path builds on the last — work through them in order.
AI Foundations for .NET Developers
Beginner · 5 articlesCore AI concepts, RAG, prompt engineering, and generative AI fundamentals for C# developers.
- 1. Microsoft.Extensions.AI: The New Foundation for .NET AI Development
- 2. What Is Generative AI? Complete Guide for .NET and C# Developers
- 3. How LLMs Work: Token Prediction, Context Windows, and Inference for .NET Engineers
- +2 more articles
.NET AI with Semantic Kernel
Intermediate · 5 articlesPlugins, memory, orchestration, and the full Semantic Kernel SDK from setup to production.
- 1. Semantic Kernel for .NET Developers: Complete 2026 Guide
- 2. Getting Started with Semantic Kernel in .NET: Step-by-Step Setup
- 3. Semantic Kernel Plugins: Build Reusable AI Tools in C#
- +2 more articles
AI Agents in .NET
Intermediate · 6 articlesAgent architecture, Microsoft Agent Framework, multi-agent orchestration, and MCP integration.
- 1. Build Your First AI Agent in .NET with Semantic Kernel
- 2. Microsoft Agent Framework: Complete Guide for .NET Developers
- 3. AI Agent Architecture in .NET: Design Patterns for Production
- +3 more articles
Production AI Engineering
Advanced · 2 articlesResilience, observability, cost optimization, and deployment patterns for AI-powered .NET apps.
- 1. Securing AI Applications in .NET — API Keys, Data Privacy, and Guardrails
- 2. Designing AI Workflows — Orchestration Patterns for .NET Applications
Machine Learning with ML.NET
Beginner · 3 articlesPredictive ML, ONNX models, and traditional machine learning pipelines in C# without Python.
- 1. ML.NET Guide: Predictive Machine Learning for .NET Developers
- 2. ML.NET Sentiment Analysis: Complete Tutorial in C#
- 3. ONNX Models in .NET: Run AI Without Azure
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Build projects, fix errors, or stay current — each pillar serves a specific developer need.
Hospital
Fix real errors fast. Verified debugging solutions sourced from GitHub Issues, StackOverflow, and production incidents.
Workshop
Build complete projects end-to-end. Step-by-step tutorials that ship real AI-powered .NET applications.
News
Stay current with .NET + AI ecosystem releases, industry shifts, and what they mean for .NET developers.
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Hot topics across the .NET + AI ecosystem
Latest Releases
Stay current with the .NET + AI tooling ecosystem
MCP C# SDK v1.0: Authorization, Tool Sampling, and Async Requests for .NET Agents
Model Context Protocol C# SDK v1.0 GA: authorization support, tool sampling, and async long-running requests for .NET AI agents using Semantic Kernel and Agent Framework.
Microsoft Agent Framework RC: Multi-Agent Orchestration, MCP Support, Frozen APIs
Microsoft Agent Framework RC ships frozen APIs, multi-agent orchestration, and native MCP support for .NET teams using Semantic Kernel. Production readiness checklist included.
GPT-5 and o-Series for .NET: Tool Use, Reasoning, and Structured Output
GPT-5 and o-series reasoning models (o1, o3, o4-mini) improve tool calling, structured output, and context windows for .NET AI agents built with Azure OpenAI SDK.
Open-Source LLMs for .NET: Llama 3, DeepSeek, and Qwen via Ollama
Llama 3, DeepSeek, and Qwen enable local inference in .NET via Ollama and ONNX Runtime — no cloud APIs, no inference costs, full offline capability in C#.
Recently Fixed Errors
Verified debugging solutions for real-world production issues
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SDK release summaries, architecture patterns, verified error fixes, and curated learning resources. Read by .NET developers building AI in production. No spam. Unsubscribe anytime.