## Intro [00:00] Cursor has a limit on how big your project can be because of the model's context size. Everyday people struggle with things not working or cursor messing up their projects. I found something better. It's called Planex, a command line coding agent built for large-scale projects. In this video, I'll show you how to install it, how it works, and what you can do with it. Let's get started. The special thing ## Overview [00:21] about Planex is that it can handle up to 2 million tokens of context directly, which is a lot. It can also index directories with up to 20 million tokens or more. This is possible because it uses tree sitter project maps, a relatively new feature in code editors that helps you navigate code better. Not only that, it uses multiple models through the open router API key and automatically picks the best one at any given time. This is why they say it's designed to be resilient for large code bases. Let's go ahead and see how to install it. Now, let's talk about the installation options. If you're on Windows, you need WSL or it won't work. There are three ways to run it. First, you can use Planex Cloud where you don't need separate API keys. Everything runs in the cloud and you can get started quickly. The quick start guides are in the GitHub repo and I'll link it below. Second, you can use Planex Cloud with your own API keys. You bring your own keys but still use the cloud service. Third is the self-hosted local mode where you run Planex yourself with Docker and use your own API keys. In this demo, we'll be working with the local mode. Let's set it up locally. ## Local Server [01:26] This is the local mode quick start guide. It's linked in the GitHub repo and I'll also put it in the description below. First, you need to clone the GitHub repo and start the server. As you can see, I have pasted in the command and now my server is running locally for Planex. Before you paste this command, make sure Docker is installed, set up, and running. Otherwise, it will throw an ## Install CLI [01:49] error. The next command goes into a new terminal to install the Planex CLI. You can see I entered the command and now Planex CLI is installed. It will attempt to use pseudo during installation. So you'll need to enter your password as well. After that, in the same terminal, you'll sign into Planex. It will create a user for you because you're running it locally. Just copy and paste the command. After pasting, it will ask how you're using Planex. Remember the three options I mentioned? We're selecting local mode. You'll see a host address. If you look back, the server command is running Planex on a specific port which shows up in the default option. Just press enter. It will create a user sign you in. And now to start Planex, you just use the Planex command. It will spin up a ripple for you in the project directory you want to work in. Now you ## Start Planex [02:34] can see I'm in my desired directory. And I want to initialize Pandex. Before you do that, you need to expose your open router API key and your Open AI API key. That's what Pandex will use. After you have your API keys exposed, you can initialize Planex in any repo you want using this command or a shortened version as well. Both work the same way and will initialize Planex. For more commands, you can check the instructions here. There are also other commands you can use like setting the configuration and changing Planex to auto mode. I'll explain what auto mode is in a moment. Right now, we're in chat mode. To enable mode, which starts writing code, we use this command. There is also a multi-line reading mode, but it is disabled for ## Planex Demo [03:17] now. Planex has actually provided a pretty detailed demo. I will demo it here myself, but let me show you how Plex works through it. This is how Plex works. You start in chat mode and whatever you want to build, you tell it your ideas and brainstorm with it. Even if you don't know anything about the tech stack you're going to use, just flesh out your ideas. You don't need to have everything planned from the start. If you provide a ready-made project, Planex can go through the files and figure out where everything should go thanks to the way it handles large context sizes. When you're ready to code, you can switch to Tel mode. It will automatically ask you and once enabled, it will start the implementation. Just like many newer tools, Planex breaks down the main task into smaller steps, each focused on a single goal, and it works through them one by one. Another thing is that whatever changes or files Planex creates happen inside a sandbox. After every tell mode session, you get a prompt where you can review the changes, apply them or reject them. Both versions of the file stay separate until you approve the changes. This means you can test what you built first. Like cursor, you can also run commands and set things up. Another feature is debugging. If any commands fail after you accept them, you can turn on full auto mode, which will try different fixes by itself. But be aware, full auto mode will use a lot of tokens and burn through your open router and API key credits, which can get expensive. I've opened a Swift app I ## Swift Demo [04:41] made using Gemini 2.5 in a previous video and told Plandex that this is the project. It read everything, recognized that it's a Mac OS menu bar application built with Swift and Swift UI, and laid out the application overview, architecture, key components, and user flow. It now understands how the app works. Let's see if we can make some changes to the app. Here's the prompt I'm going with. I'm asking it to improve the UI for the Swift app, which is usually hard for AI models. If the implementation isn't done step by step, they struggle because they're not deeply trained on Swift and can't retain context well for the code. Let's see how it performs. You can see it reasoned through the project and is now asking to switch to tell mode. Let's do that. Now, it's asking if we want to send another prompt or begin the implementation. Let's begin the implementation. You can ## Implementation [05:37] see it built the plan and presented me with this menu. It didn't change the main files, but created a new script to automatically build the app. I pressed A to apply the changes and now it's asking if I want to execute. I said yes, and now it's executing. It will build the app, get the logs, and make changes based on that. This isn't a big error, but I want to show you something. If a command fails, it gives you a menu where you can choose to debug once or debug in full auto mode. That's what I'm going to use right now. You can see that another plan has been built and now it's applying the changes. Once all the changes are done, I'll show you what it ## Result [06:12] has made. The app was built and I compiled it using Xcode. This is what it was able to build. I can now go into settings, click on the accent color, and adjust it to any color I want. It's using the built-in Mac OS and Swift UI components, which is great. You can now change the accent color to anything like making it blue. I made the original app in a previous video using Gemini 2.5, and that was the only model that could do it. This is probably why the tool succeeded because it uses multiple models from Open Router. Everything works and it looks pretty nice. That's it for this video. If you liked it, please consider subscribing if you want us to keep making these videos. And since my wallet is running a bit empty, please consider donating using the link below. Thanks as always for watching and I'll see you in the next