In today's fast-paced digital landscape, legacy software can be a bottleneck to innovation and growth. Yet, a green field rebuild is hardly ever your best way to success. There is so much value in your existing system that can be salvaged and modernized.
Join us for a hands-on exploration as we delve into modernizing a legacy .NET Framework 4.8 / WinForms application for the cloud and AI age. Through a series of interactive modules, participants will learn essential skills such as deciphering legacy code using AI, transitioning to modern technology stacks, and implementing best practices for refactoring, performance, security, and quality assurance. To finish it off, we will infuse the application with some AI to make it smart and ready for the new era.
By the end of the workshop, we will have transitioned from a legacy .NET / WinForms application to a cloud-ready, AI infused web application with Blazor.
You will learn:
- Decipher legacy code: Learn techniques and tools to navigate and understand existing .NET Framework 4.8 codebases.
- Apply modern refactoring and architecture techniques, improving software step-by-step, optimize performance and costs, enhance security, and ensure software quality.
- Transition to modern stacks: Select and implement contemporary technology stacks suitable for cloud-native applications, and using a modern front end technology, infused with AI.
Attendee Requirements:
- You must provide your own laptop computer (Windows or Mac) for this hands-on lab.
- A modern desktop browser such as Chrome or Edge
- A GitHub account, which we will use to onboard you to our organization and provide you with the Lab Environment.
The HOL will run out of GitHub Code spaces, so we will provide the full lab environment. You can sign up for a free GitHub account here: http://github.com
Optional:
This is only for running the starting application, which is a .NET 4.8 Windows Forms application, you need the following:
- Docker Desktop
- Microsoft Visual Studio Enterprise 2022 (64-bit) + .NET framework 4.8 on the local machine