This AI Codes While You're AFK: I Built a Full App with Cursor Agent

Key Takeaways

Hello everyone. Picture this. You are grabbing coffee and sudden you have this amazing idea for a feature. Usually you would have to wait until you are back at your computer, right? Well, not anymore. What if I told you that you could start coding on your phone, have an AI agent work on it while you are in line, and then review the working code when you get back at your desk. Sounds like science fiction. Well, it's real and it's called Carser agent. Carser just launched their agent feature on web and mobile last week. And guys, this isn't just another AI coding tool. This is what happens when you give AI the ability to actually understand your entire project and work independently while you focus on the bigger picture. I have been testing it out since their launch and I'm totally blown away. So, what exactly is a cursor agent? Think of it as your personal AI developer that never sleeps, never gets frustrated and never judges you for forgetting semicollets. Traditional AI coding tools just autocomplete your next line. Cursor agent, it understands your entire project, write complete functions, refactors across multiple files. And here's the kicker, it can work completely autonomously while you are doing other things. The mobile and web interfaces are not trying to be full code editors. They are command centers. You describe what you want. The agent goes to work and you review the results. It's like having a junior developer who is incredibly fast. Just a quick note, Analytics Vidya has 100 plus free courses from beginner to advanced on topics like machine learning, generative AI, agents, etc. Complete with certification. Click the I button to explore. Now, >> now let's talk about the key features. Let me show you the features that make this special. First up, multi-device accessibility. I can literally start a task on my phone during my morning walk, check progress on my laptop at lunch, and finish it on my desktop in the evening. The context never gets lost. The second would be the autonomous operation. For example, I'm going to ask it to set up authentication for a project and then I'm going to grab a snap. When I come back, it should have the middleware, database models, and API endpoints all already all ready. And look at that complete authentication system with proper error handling. It even followed my project's existing patterns and existing structure as well without me having to explain them. For teams, you can share agents collaborate on different type of task, maintain consistent coding standards across different organization. It's like having a senior developer to give us their expertise available to everyone. Now, let's talk about the pricing because I know you are wondering about it. There is a free tier that is perfect for trying this out. You get 50 requests per month. The pro plan at $20 per month gives you like 500 requests and access to the premium models. For most developers, that is going to be very much plenty. All right, let's get started. I'm going to be building a complete to-do application and I'm starting right here on my desk. So you will have two options whether you can start with a web interface or mobile interface. Mobile interface is for the one where you can quickly type up your prompt connected with your repository and get started with your project. So here we can see I'm opening cursor agent on my desktop creating a new project. I will call it quick to-do and I'm going to tell it exactly what I want. So here I have my GitHub repository. So I started with my application by creating a new repository called todo application. So I started building it from the very scratch. I gave a prompt that analyze the code files of my existing repository and then make the changes accordingly. I want to build a modern todo applications with react and typescript. We should be able to add, edit and delete task and mark them as completed filtered by their status like all active and completed. Make sure the local storage persist. So here we will be building it from the scratch. We can see. So when I gave the prompt to the cursor agent, it started building first. What it did? It edited my package.json file. Over here we can see that statement. So after creating the package.json JSON file. It ran the two terminal commands to start our modules. So, npm install is the one which will be necessary to install all the dependencies required to run our project. After that, it installed other necessary dependencies which was required for our project. Look at this. It's already planning the component structure. It will be suggesting a todo component, a to-do list, a todo context for state management and even thinking about styling with the tailwind CSS. Over here you can look at different file structure made by the agent itself. If you look at different file structures, you will see that not only it created in a properly structured manner, it gave a proper syntax as well. So I'm approving this structure and now watch what happens. It is creating all these files simultaneously. There is the todo component with proper TypeScript interfaces. The context with all the crude operations and even the styling. Now the repository is set up with modern and responsive to-do application built using React. So after all the changes are done, we will run the application in our development mode. You can access it in our browser typically at at your local host or the port specified by the bite. If you want, you can directly make it live on GitHub as well, which will allow you to preview the live version. Okay, that was fun, but let's get real. To do ads are great for demos, but what about something you would actually use in production? Let's build a complete weather API integration for a travel app. I have been working on this travel planning app called Vonda Plan for a few weeks now. Let me show you what I already have. Here is the basic structure with user authentication, trip planning components and the destination search feature. So we have a basic NexJS setup, a few components like trip card, destination search and some utility functions. The app lets users create trips and search for their destinations. But here is what's missing. It shows the destination dates and basic info. But users keep asking what's the weather going to be like. And this destination search component finds great places to visit. But without with the weather data, it is not very helpful for planning. So here is what I need the agent to do. I'm telling it, I have an existing travel planning app with the trip management and destination search. I need to integrate open weather map API to show weather data in my existing trip card and destination search component. This needs to be production ready with proper error handling, TypeScript support, caching, rate limiting. Please maintain consistency with my existing code styles and component patterns. Watch this. The agent is actually analyzing my existing code. It is looking at my component patterns, my TypeScript interfaces, my styling approach with Tailwind and even my folder structure. This is crucial because it needs to fit seamlessly into what I have already built. Look at how it is breaking this down. It is not just building a weather service in isolation. It is planning how to integrate with my existing trip card component. How to extend my destination search with weather data and it is even suggesting improvements to my existing interfaces to accommodate weather information. Now building the client interface. But notice how it is following the same patterns I established in my other API calls. It is using the same error handling approach I set up for my destination search API and maintaining the same folder structure. The TypeScript interfaces are comprehensive but more importantly they are extending my existing types. Look, it is taking my existing destination interface and adding weather properties to it. And here is a new weather interface that matches the style of my other type definitions. The TypeScript interfaces are comprehensive, but more importantly, they are extending my existing types. Look, it is taking my existing destination interface and adding weather properties to it. And here is a new weather interface that matches the style of my other type definitions. The React integration is beautiful. Instead of creating completely new components, it is extending my existing ones. Look at this. It is adding weather hooks to my existing trip card and destination search components. The use weather hook integrates perfectly with my existing userip and use destination hooks. And here is where the magic happens. My existing components are now enhanced with weather data. The trip card now shows current weather and forecast for the destination. The destination search results now include weather information to help users make better decisions. The testing is comprehensive and considers my existing testing step. It is adding weather related tests to my existing test suites and creating new ones that test the integration with my existing components. Works with my existing cache strategies and the loading states are consistent with the rest of my app. It doesn't feel like a bolted on feature. It feels like it was always a part of the app. So where does this really shine? First, rapid prototyping. I can validate ideas incredibly quickly now. Product managers can sketch out features and developers can review working code within hours. Code review and refactoring. The agent acts like a senior developer identifying issues, suggesting improvements, and even implementing fixes automatically. For learning, it's incredible. As someone who is still developing my coding skills, having an AI assistant that can explain concepts, generate examples, help debug issues, that has been game-changing. It's like having a patient mentor available 24/7. Here's what we accomplished today. A complete twodoor application in under 5 minutes and a production ready weather API integration with comprehensive testing in about 15 minutes. Traditionally, that weather integration would have taken me hours, maybe a full day. But here is the thing. This isn't about replacing developers or data scientists. It's about amplifying what we can do. Especially for those of us who are still learning. The agent handles the boiler plate, the repeated task and the setup work. We focus on understanding the logic, learning best practices, and solving the actual problems. We are at the beginning of a fundamental shift in how software gets built. For those of us just starting our careers, learning to work with AI tools, like this is going to be very essential. I want to know what would you build with your cursor agent? Drop your ideas in the comments below and if you try this out, share your results. I love seeing what the community creates. Thanks for watching and I'll see you in the next one. Keep coding, keep learning, and keep pushing the boundaries of what's possible.