Posts tagged with "AI"

A few years ago, I wrote about how ReSharper's Search with Pattern feature helped me refactor a massive C# codebase in minutes. The technique was powerful, but creating those custom search and replace patterns meant using ReSharper to define them. Alternatively, you could hand-craft XML in DotSettings files—tedious work that required memorizing the exact structure, generating GUIDs, and configuring placeholder properties correctly.

Recently, I explored how Agent Skills are becoming an open standard that works across different AI coding assistants like GitHub Copilot, Cursor, and JetBrains Junie. This got me thinking: what if I could combine these two powerful developer tools? What if an Agent Skill could automate the tedious part of creating ReSharper and Rider patterns?

This post shows how I built exactly that—a practical Agent Skill that generates properly formatted DotSettings XML for custom search and replace patterns in Rider and ReSharper, just by describing what you want in natural language to your AI coding assistant.

When Anthropic introduced Agent Skills for Claude, it appeared to be another proprietary AI customization feature. Today, we're witnessing something far more significant: an open standard reshaping how people across roles—developers, designers, product managers, and operations—work with AI assistants. AI coding agents' adoption of Agent Skills has transformed this technology from an interesting experiment into an essential developer tool.

If you've been using custom instructions or wondering how to make your AI assistant truly understand your project's workflows, Agent Skills provides a compelling solution.

As developers, we often find ourselves tied to specific providers. The same applies to Large Language Model (LLM) providers. This can limit our flexibility and control over our applications. In this blog post, we'll explore how to decouple ourselves from LLM commodities by leveraging Dapr's Conversation building block. This approach allows us to switch between different LLM providers seamlessly, ensuring that our applications remain adaptable and future-proof.

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 how we can dockerize your .NET C# MCP server to be able to distribute it for use by AI clients. It's time to take a step further and explore how to leverage Server-Sent Events (SSE) MCP servers so that we can deploy them remotely, for example on a Raspberry Pi.