How I Built a Go App in 4 Hours with Cursor AI

TL;DR

Cursor AI isn't just a code editor; it's a productivity multiplier. I built a full-stack app in a new language in just 4 hours. It's a game-changer for learning and shipping faster. #CursorAI #DeveloperTools #VentureStep

INTRODUCTION

Remember the early days of learning to code? The endless debugging sessions, the frustration of learning new syntax, and the sheer time it took to build even the simplest application. Today, a new class of AI-powered tools promises to act as a sidekick for programmers, handling everything from debugging to writing entire blocks of code. One of the most talked-about tools in this space is Cursor AI, an AI-native code editor designed to supercharge developer productivity.

In this episode of Venture Step, host Dalton Anderson puts Cursor AI to the ultimate test. Despite having no prior experience with the Go programming language, he set out to build a full-stack To-Do application with a Go backend and a Java front end. This wasn't just a theoretical exercise; it was a hands-on experiment to see if Cursor could genuinely accelerate the development process, especially for someone learning a new technology from scratch.

Dalton shares his entire journey, from the initial setup to the final, functional application. He breaks down Cursor's core features, explaining how it leverages the entire codebase for context, allows for instant refactoring, and can generate comprehensive documentation on the fly. This episode is a deep dive into the practical impact of AI on software development and a look at how these tools are radically lowering the barrier to entry for aspiring developers.

KEY TAKEAWAYS

• Cursor AI dramatically accelerates development by using your entire codebase as context, providing more accurate and relevant suggestions than standard AI chatbots.
• The platform significantly lowers the barrier to learning new programming languages, enabling users to build functional projects from day one.
• Features like one-click code refactoring and automatic documentation generation can eliminate hours of tedious and time-consuming developer work.
• Cursor allows you to switch between leading AI models like GPT-4, Claude, and Gemini on the fly to find the best tool for your specific task.
• By integrating AI directly into the editor, Cursor streamlines the workflow and removes the friction of switching between your code and a separate AI chat window.

FULL CONVERSATION

What is Cursor AI?

Dalton: We're going to talk about Cursor AI, which is a fork of VS code. And if you're not familiar with any of these topics, that's fine. I'll go over them. So you won't be lost if you are new to programming. Cursor AI is supposedly a groundbreaking AI powered editor, which is a fork of VS code. We're going to delve into the capabilities of cursor AI, discuss the potential impact on software development, and just share my personal experience of Cursed Ray Eye, because I built an app that I'll show you today in Golang and Java.

Understanding "Forking" in Software Development

Dalton: Cursor AI is a fork of VS code with enhanced AI capabilities. What is a fork? Without getting too technical, there's this language called Git, which is used for version management. And if you have a repository of code, think about it as your architectural blueprint of your house. And then you published it online, open sourced it to everyone. Say that your friend comes along, likes the house, but doesn't really like some of the additions that you have. And maybe one wants to downsize or maybe another friend has a completely different approach on something that you did, and that would be a fork. They are completely different versions, but they came from your open-sourced architectural blueprint.

So if you think about the house, think about now all the code for VS code is open-sourced. And so what Cursor did was they forked VS code at a certain point and they kind of built their own VS code with all these AI capabilities on top of it. It's basically VS code with AI capabilities, but that's what a fork is, in a non-technical manner. I think it has an intuitive interface where it has a corner panel that could pop out on the right side if you want to have a chat, similar to what you would see on ChatGPT, Google's Gemini, or Anthropic's Claude.

But one thing that's a little different is you could switch on the fly which model you wanna use with premium. You can use certain models like the non-premium OpenAI or like the non-premium Gemini models. But when you have Cursor Premium, you're open to whatever model you really want.

How Cursor AI Is Different from ChatGPT

Dalton: What's the difference with me just taking my code and sending it over to ChatGPT or Gemini or Claude? Well, now, with cursor AI being the intermediary between the foundational models and your code base, it uses your code base as a source of information.

So when you ask a question, your code is used as context. And you can specify, okay, I wanna use information from the web. I wanna use these following files within my app or my program. And then from there, it would combine all that information and the foundational model has that additional context where it could serve you in an expedited manner.

It’s like, well, I wanna do X, Y, Z and you're sending your instructions that you have. And so instead of the model saying, "Okay, what do you have on line four?" and then you tell the model, "I have this," there's not this back and forth. The model already knows what you have, so it can make the changes appropriately. Then it gives you suggestions of what it thinks and you can accept them.

My Experiment: Building a To-Do App with Go and Java

Dalton: What I did with Cursor is I built a Go Lang app with Java. So the backend is Go and then the frontend is Java. The frontend is a command line interface. So you have the command line and you're putting in your inputs. I'm storing this in a JSON file and then Go is bringing it back and displaying it on my local host. The Java front end interacts with the back end via a RESTful API.

This would be very difficult for me to do myself because this is my first Go project. I don't know Go. And so I think the whole process took me about four hours. But you have to keep that in mind, I had to set up my computer, which involves setting up these directories, downloading these files, getting them formatted correctly. It's sometimes time consuming.

I honestly don't think that I would be able to complete this project, definitely not in four hours without the help of Cursor. Like Cursor really made it possible.

The Biggest Challenge: Navigating File Paths

Dalton: One of the issues I had was related to file management. Originally I had everything put together on my documents folder. Then when I was creating everything for the app, I had to make a new directory using the terminal. Somehow I had files from my directory that I created via terminal and then I had files from my original creation which was in the documents folder, and then cursor was getting confused on which file to use.

One of the issues with cursor, I think, is when you have a file open and you used it in the chat, even though you close out a file and you're not using it anymore, it will still use that file. So you have to be careful. It was difficult for me to tell because it doesn't show the full file path in cursor, which I think they should start to. It took me some time to figure that out that, "Hey, even though I closed the file, it's still using the old files that I don't want to use anymore." So just keep that in mind. Set it up once, don't do two projects and things would be a lot less confusing.

The Power of Prompt Engineering for Code Generation

Dalton: The way that I went about it is I set up a chat on Gemini and I said, "Hey," and this is a good thing to do, I'll set up a podcast to talk about it, but if you set up a chat and you give your chat a role like, "Hey, you're a senior developer for 20 years and you program in Go and various languages, but your primary language is Go. I want you to use XYZ coding methodologies to make sure you have clean, concise code."

I think AI really likes role playing. And so if you give it a role, it will do great. And so it will definitely improve. That level of prompt engineering will help you out with your outputs.

Live Demo: The To-Do App in Action

Dalton: Okay, so here we are. We have this Todo app. The app is called Todo-app.go. There's a folder called backend, which has main.go. Our front end folder is called todo-app-java. It has all of the different types of APIs that you can hit and also how you're going to add and remove tasks. If you go and look at the JSON file, we already have three tasks in here. The command to run the backend is go run main.go. So after I run that, it will say "listening and serving HTTP on localhost 8080."

Then we go back over to our other terminal, which is the Java one. To run it, I use java TodoApp. So now I'm in the app, so you could choose your actions of what you want to do. Let's do view tasks. Okay, so we have our first task ID, description, blah, test, completed, false. So let's toggle a task for completion. We want to complete task one. Now it's completed.

Generating Documentation and Refactoring Code Instantly

Dalton: Let’s say I have a new hire coming in on Monday. I can ask the chat, "Can you please write some documentation for this project for a new hire?" So you're seeing it real time here, the queries for cursor are longer. So I asked it, and it writes up API documentation. It talks about the project structure, the important files, key components of the task structure, and the main functions of the app. It's super useful.

You can also ask it to, if you click on all the code, you can ask it to refactor your code, which I think is huge. A lot of times when you write something, you have an initial stab. When you're writing code, it's the same thing where you just write the code and then afterwards you go through and you optimize the code, you make it better, you make it clearer.

This is what would take hours. You could do in 30 seconds, you just click this button and it refactors the code. It helps because you're not spending time on things that don't add value. It will optimize the code, make it faster, better, clearer, more concise, everything for you for next to no effort.

Lowering the Barrier to Entry for New Developers

Dalton: What's really cool about cursor and the new capabilities is it makes it easy to get started. And I think that's the biggest hurdle for people. They have these big ideas, but the issue is getting started and it's a daunting task sometimes to learn a new language. This gets you started. You're not gonna be an expert. You're not gonna write anything complex.

You're not going to make some crazy game-changing app with no knowledge, but it will get you started and get you confident and see how the code functions. And then you can have your own little approaches and you could learn by having the cursor break down the code for you.

I think that's invaluable because I think the best way to learn is to do, right? You can read as many coding books as you want, but you're not gonna get any better unless you actually do the task. I learned a lot about Go just in these last couple of hours. I probably wouldn't have learned that much and been as engaged as I was if I wasn't using cursor because obviously I wouldn't have been able to make this project in under four hours.

A Quick Dive into the Go Programming Language

Dalton: Go was created at Google by Robert Griesemer, Rob Pike, and Ken Thompson. It was created for reliability, scalability, and is mostly used for cloud infrastructure or APIs.

Go is described as like a brutalist language. There isn't much freedom, but there isn't anything extra, right? So Go doesn't like when you have your own little style of coding. It's very concise, to the point, simple. That's Go. You don't have a lot of developer freedom.

So it's good for large companies that have to maintain large code bases because if you have all this developer freedom, then it's hard to keep the code standardized. With Go, it makes it a lot easier. It's a compiled language. What that means is when you run Go, everything's ran at the same time and it's compiled, which makes it have faster execution speeds. So it's way, way, way faster than Python, which is an interpreted language.

Final Thoughts on Cursor AI's Impact

Dalton: To recap Cursor AI's capabilities, it's a fork of VS code with features like selecting your foundational model, making inline edits, and refactoring your code base. You can ask it questions and select which files or even web content you want to use as context.

There's all these activities that would take up a lot of time that you no longer have to do anymore, which I think is great. And then it could help you write the code as well. So it's a no brainer. It's so good.

The capabilities that it will have in a year, a year and a half from now is really, really exciting stuff. It's gonna be cool. So what do you think about Cursor AI? Have you used it? Are you curious about it? If so, please share down below.

RESOURCES MENTIONED

• Cursor AI
• Visual Studio Code (VS Code)
• Go (Golang)
• Java
• Git
• OpenAI / ChatGPT
• Google / Gemini
• Anthropic / Claude
• Meta / Llama
• Python
• C#
• C++
• X (formerly Twitter)

INDEX OF CONCEPTS

Cursor AI, VS Code, Git, Fork, Golang, Go, Java, OpenAI, ChatGPT, Google, Gemini, Anthropic, Claude, Meta, Llama, GitHub Copilot, API, RESTful API, JSON, Compiled Language, Interpreted Language, Python, C#, C++, Robert Griesemer, Rob Pike, Ken Thompson, Prompt Engineering, Code Refactoring, Reflection 70B Model