The era of intelligent, AI-assisted development has arrived—and with the power of .NET , large language models , and Microsoft Copilot in Visual Studio Code , building sophisticated business tools has never been more accessible. By integrating Copilot directly into your VS Code workflow, you can accelerate your coding. In this post, we’ll walk through how to create an AI-powered Request for Proposals (RFP) response writer that blends the performance and flexibility of .NET with the n

You can use Blazor with Aspire and Visual Studio Code .   Install Visual Studio Code One of the advantages of using Visual Studio Code rather than Visual Studio , is that Visual Studio Code is lightweight and runs on more platforms. Go to: https://code.visualstudio.com/download to download and install Visual Studio Code for your OS.   Launch VS Code after installation.   Install the C# Dev Kit Extension In Visual Stu

You can easily implement external authentication providers to allow the users of your Blazor application to log in with Facebook, Google, Microsoft, Twitter, and many others. Simply follow the directions here: Facebook and Google authentication in ASP.NET Core | Microsoft Docs . However, you have to write custom code to associate an external login to an existing login. The method, covered here, will ask the user to enter the password of the existing account to make the association. Note:

You can easily embed Power BI reports into a Server Side Blazor Application. This method allows you to display a Power BI report without requiring the user to have a Power BI account. Note: To move to production you'll need a capacity .   Set-Up For The Application To set up to run the sample application, we follow the directions in this article: Embed Power BI content with service principal and an application secret . The first step is to start with a Power

Local Internal AI Chat Bot is a Blazor web application designed to bring the power of AI-driven chat and knowledge retrieval to your local environment. Leveraging Ollama hosting a phi3.5 model and advanced vector search, this project enables users to interact with a chat bot that can answer questions using custom, user-provided data. Documents are chunked, embedded, and stored for fast semantic search, ensuring relevant and accurate responses. The interface, built with Radzen