Introduction to AI autocomplete tools - C# Tutorial

- [Lecturer] In this video, we're going to talk about the evolution of autocomplete tools for developers and how AI is changing everything. But before we get into that, I'm going to talk a little bit about how I've seen this evolve over the years. When I first started coding back in the '80s, I was using a program called HyperCard. I remember it came with a giant book full of documentation. I had to look up all the commands and functions in the index of the book. This process didn't help me understand the concepts behind the code, just the functionality. Unfortunately, this didn't really change until the early 2000s. Eventually, the internet became more accessible, which opened up a new world for learning how to code. Search engines and online communities allowed me to see how others coded and to find answers to my questions more easily. This was a significant improvement over the manual reference books, which were still being shipped with software at the time. As IDEs evolved, they started including code completion features. Code completion is a feature in IDEs that predicts and suggests completions are partially typed code, reducing the need to remember detailed syntax. This meant I no longer had to memorize all the parts of a language. I could focus on understanding the concepts while the IDE helped me with the specifics. This marked a shift towards more efficient coding. In the past few years, AI has taken code completion to a whole new level. With the release of more capable large language models or LLMs like chat GPT, I can now focus on my ideas and let the AI help with the specifics, functionality, and even explanations. These LLMs are advanced AI systems trained on vast amounts of text, capable of generating and understanding code to assist developers. This brings us to GitHub's Copilot. I started testing GitHub's Copilot when it was first announced. It was a game changer for generating code for my fantasy game console, Pixel Vision 8. However, it wasn't very intuitive at the time. There was a learning curve, but the potential was evident. Initially, I felt like Copilot was suggesting other people's code that didn't always fit the context of what I was working on. Despite this, I could see the promise in enhancing my coding and making me more efficient while providing valuable suggestions. Now, copilot is built on top of open AI's codex. This transformation makes it more than just an auto complete engine. It's an intelligent assistant that helps from ideation to execution, significantly enhancing the coding process. We've come a long way from the manual reference books to AI driven code completion. Tools like GitHub's Copilot are not just about making coding easier. They allow us to focus on creativity and problem solving. Embracing these AI tools can significantly boost your productivity and efficiencies as we'll learn in this course.