In the previous post “Leveraging Microsoft.Extensions.AI for Tool Calling in C#“, we explored how to create custom tools that enhance the capabilities of Large Language Models (LLMs). We demonstrated how integrating these technologies enables developers to build applications with advanced AI capabilities, facilitating more complex interactions.

In this post, we’ll take a step further and explore how to leverage Model Context Protocol (MCP) servers to utilize external Tools and AI models in C# applications. We’ll continue using Ollama to run local AI models.

.NET Aspire 9.1 was just released on February 25th, 2025. It comes with great new dashboard features, and there is more! One feature I am particularly interested in is the ability to use Dev Containers.

In this post, I will show you how to use the new .NET Aspire 9.1 with a Dev Container and JetBrains Rider. You could also use Visual Studio Code.

In my previous post, we saw how to build the simplest Semantic Kernel local AI agent using Semantic Kernel and Ollama in C#. In this short post, we will see how simple it is to extend the capabilities of the Semantic Kernel local AI agent by adding function calling.

The recent release of Microsoft’s Semantic Kernel Agents Framework RC1 provides an excellent opportunity to explore its capabilities with C#. In this post, we’ll walk through creating a simple agent using this framework and running it locally with Ollama.

Let’s dive in!

In my earlier posts, I’ve demonstrated how to install Ollama using the Windows installer. However, for developers, there’s a more streamlined method to set up Ollama on your machine.

In this blog post, we’ll explore how to run DeepSeek-R1 by harnessing the capabilities of .NET Aspire alongside Ollama on your local environment.

In the previous post “Learning AI function calling in C# with Llama 3.2 SLM and Ollama running on your machine“, we wrapped our head around the concept of tool calling and implemented a C# source generator enabling our functions to be called by Llama 3.2 SLM using Ollama.

In this post, we will explore how to use Microsoft Extensions AI for tool calling in a simple .NET CLI application. We will leverage the power of Ollama and Llama 3.2 SLM to call functions and interact with the AI model using C#.

I’ve been trying to wrap my head around function/tool calling for a while now, and I’m excited to share what I’ve learned with you. It’s a powerful way to let developers integrate advanced AI features directly into their applications. We’ll walk through understanding the core concepts, setting up your environment, and implementing a practical example using a C# source generator.

Microsoft recently unveiled Phi-3, the latest iteration of their Small Language Model (SLM). And hot on its heels is Ollama, a powerful tool that enables you to run SLMs and LLMs right on your own machine.

Excited to dive in? In this guide, I’ll show you how to harness the power of Phi-3 and Ollama using C# and Semantic Kernel. I’ll walk you through the process of creating a simple console application to get you started on your SLM journey.

So, let’s get coding and unlock the potential of Phi-3 and Ollama on your machine!

C# 12 is the latest, just released, version of the popular programming language that runs on the .NET 8 platform. It introduces several new features that aim to improve the expressiveness, performance, and safety of the language. In this post, we will explore some of these features and see how they can benefit your code.

My previous blog posts talks about built-in container support in the .NET SDK. It allows you to create and publish Docker images for your .NET applications without writing any Dockerfile. I also showed how to use the Chiseled Ubuntu base images optimized for .NET and containers. We saw how to publish your Docker images using the .NET SDK to GitHub Packages / Container Registry.

Today, I want to show you how to publish your Docker images using the .NET SDK to GitHub Container Registry using GitHub Actions. It brings us to the next level of automation.