Laurent Kempé

I installed Omarchy on my first-generation Microsoft Surface Book, mostly to find out whether this 2015 laptop could still make a good .NET development machine.

I opened GitHub on my phone before my morning coffee had finished brewing.

For years, my git worktree setup was modest: two worktrees for active development, and one more for code reviews.

A few years ago, I wrote about how ReSharper's Search with Pattern feature helped me refactor a massive C# codebase in minutes.

.NET 11 Preview 1 ships a groundbreaking feature: Runtime Async.

When Anthropic introduced Agent Skills for Claude, it appeared to be another proprietary AI customization feature.

In C#, partial has long been a practical bridge between human-authored code and tool-generated code.

Extension methods have been a core C# feature since version 3.0, enabling developers to add methods to types without modifying source code.

If you've been working with C# since the introduction of null-conditional operators in C# 6.0, you've likely appreciated how ?.

C# 14 introduces the field keyword, a contextual keyword that reshapes how we write property accessors.

As developers, we often find ourselves tied to specific providers.

Now that we've explored how to leverage Model Context Protocol (MCP) servers to utilize external Tools and AI models in C# applications, how to write your own Standard Input/Output (STDIO) MCP server in C# using the modelcontextprotocol / csharp-sdk, and…

My previous post showed how easy it is to develop a .NET C# MCP server and write a client able to communicate with it.

In my previous post, I demonstrated how to use C# with Microsoft.Extensions.AI, Ollama, and a nuget package called mcpdotnet to interact with an existing MCP Server.

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).

.NET Aspire 9.1 was just released on February 25th, 2025.

In my previous post, we saw how to build the simplest Semantic Kernel local AI agent using Semantic Kernel and Ollama in C#.

The recent release of Microsoft's Semantic Kernel Agents Framework RC1 provides an excellent opportunity to explore its capabilities with C#.

In my earlier posts, I've demonstrated how to install Ollama using the Windows installer.

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…