Build Full-Stack Apps with Deep Agent in 8 Minutes

--- type: transcript date: 2025-08-27 youtube_id: cNyTVprWOwE --- # Transcript: Build Full-Stack Apps with Deep Agent in 8 Minutes In this video, I'm going to be showing you how to build out a full stack SAS application leveraging Deep Agent. Deep Agent is a platform by the team over at Abacus AI. And what it allows you to do is to create full stack applications. What I'm going to do is I'm going to say I want to create a clone of Twitter equipped with the ability to accept payments from Stripe. I'm going to go ahead and kick that off. The cool thing with how they built this is it actually has the ability to detect the intention of what you want to create. What do I mean by that? Basically, as soon as you put in a prompt, it's going to determine different paths that it could potentially take depending on the query within here. Since I asked for a SAS application, what it's going to ask me is for things like what sort of features do I want to have? Do I want to be able to pay per tweet? What's the core functionality in all of this? For this example, I'm going to keep it relatively simple. I'm going to say I want an $8 a month premium tier that allows for users to have a blue check mark. Next, what I'm going to do is I'm going to clarify some of the core features of what I'm going to build out within here. I want basic posting and following and direct messages. And then finally, for user authentication, it's asking me, do I want to sign up with email and password or would I prefer social options? In this case, I'm going to say I want sign up with email and password. And then in terms of the target audience, I want this to be general public use. Now, I'm going to go ahead and kick off that task. Within here, we see that the agent is developing the plan for us. We do see that it's in progress and what it has access to is actually a whole virtual machine. Now, we see it going through and it's beginning to write out the code as well as spin up the instance for us. The really neat thing with deep agent is it has access to a full virtual machine. Effectively, you can connect to this Ubuntu Linux box and it will be able to have access to all of the different features that you typically would on a server or similar to how you would on your own computer. This isn't just spinning up static files within the web browser. It has access to a fully featured backend YUbuntu instance in the cloud. And so what we see within here, we see that it's generating all of the relevant components. We can go and we can look at all of these different files if we'd like or we can go ahead and we can just watch all of these streaming in as all of the different pieces are written out. Now, what's really impressive with the platform is with one relatively simple prompt, it's able to build out all of this scaffolding for me. Now, in terms of the speed of the platform, I'll let you judge by yourself, but it is incredibly fast for inference. Effectively, with just one prompt, it looks like it's going for it in terms of actually trying to build out every aspect of a Twitter clone. Now, you can imagine just 2 years ago before LLMs for you to build out something like this would take a considerable amount of time. There are a ton of moving parts with this. And just the fact that this is able to write this out in a number of minutes, it is incredibly impressive. I actually am not sure in terms of the model that they're using on the back end. It definitely does seem like it's quite a bit faster than something like Sonnet or the Anthropic models, but I could be mistaken. It could very well be an anthropic series of models, but increasingly so. There are a ton of other models on the scene that do run quite quick. It would be interesting to know on the back end what models that they're actually leveraging for this. Now, I'm going to go ahead and let it finish up here and then once it's done, I'm going to circle back and we're going to take a look at what it generated. Now, it does look like it's almost ready to finish. And now, a neat neat thing with how the platform is built is if it does run into any build errors in the process. It will actually just circle back to all of the relevant files that it needs to update and make all of those changes. You don't need to pass in the context of the different errors. It's going to be able to do all of that automatically. And you can just circle back to this once it's actually done creating your application. All right. So, here we go. This is what it generated for us. So, I can go and I can take a look at this full screen. Additionally, within this, I can see that it did actually even test the authentication feature. Within here, we have this test add example email with a password 1 2 3. This is what our application looks like. I have the ability to like things. I have the ability to even retweet things. And within here, I'm going to say this is a demo of Deep Agent. I'm going to go ahead and post this. Now, what I'm going to do is I'm going to go over to my profile. And within here, I can see the post that I just made. Within here, this is a really impressive first start. It definitely looks very close to something like Twitter. I can go over to different users profiles. There was Bob. Here is Alice. And additionally, I have this message interface here. From there, I can check out premium. Here is the $8 a month premium feature that I had asked for. I can click to upgrade here. Now, we haven't set up Stripe or anything like that. So, that would definitely be a next step. Additionally, what you can do is you can select to put it on a subdomain and then you can share this to anyone that you want. And ultimately you can pull this down and you can deploy it on your own infrastructure or do whatever you want with the code. Just to show you some of the other features within here. You have the option to download all of the different codes once you've downloaded it. So if I just unfold this directory here, we can see all of the different relevant files that it generated for us. We can see every little piece of all of the different component pieces of what it has here. We have all the bespoke components that it wrote out for us. We have all of the relevant pages and everything is correctly structured in a coherent manner for a next.js project. For instance, if I go within our API and I go over to users and I just expand that, we have the dynamic path for ID and then we also have the route for follow or post. We have profile. I am honestly genuinely impressed by this. I've used a ton of different platforms, but this is the first one with just one simple prompt that it was able to generate that much code for me as well as resolve all of the different errors. Usually for some of the other platforms that I have leveraged, I at least have to have some sort of intervention at some point to make something work, at least when it's this comprehensive. Additionally, within the platform, there is also the database built right in here. If I go over to the post table, I can see all of the different posts that were created. I can see this is a demo of Deep Agent. This is going to be the content that I created. And now, if I go over to users, I can see that this is the test email. If I go over to retweets, I can see the tweet that I retweeted. Everything is effectively already built in and I can also export all of this different data for each table one by one into a CSV for if you want to add this within an SQL database or a Postgress instance or what have you. You're going to be able to pull down everything that you need to get set up. Next up, I want to demonstrate some of the other capabilities. I'm going to show you how it can make a slideshow based on my YouTube channel. What's really powerful with this is it's going to ask some clarifying questions just like we had in the app development process. But what's really cool with this is before it actually generates the slideshow is it's going to do a thorough research and analysis on all of the different contents of what I had asked of it. Within here, what we'll see that it will gather and summarize all of the different information from all of those different web pages. In just a number of minutes, it generates a fully functional slideshow based on all of the contents on my channel. It's gathered my most viral videos. It's gathered some metrics in terms of growth, how many subscribers I have, how many videos I have, and all of these different data points. It has the average upload rate, and all different interesting insights. If I were to steer this even further, it would probably do very well at gathering different information of what I was looking for. Now, additionally, what I wanted to show you is a demonstration of it researching GPD5. In this case, similar to the previous examples, what it will do is as soon as you ask your query, it will ask some clarifying questions and then once you've clarified your questions, it will kick off the process. So, it's going to go and find all of the different relevant search terms, grab all of the different relevant information from the respective web pages. Once it has all of that, it will take that information and ultimately summarize it in a report. And the nice thing with this is it gives you the flexibility. You can build a web app with it. You can get a slideshow from it. Or in this case, just like I'm demonstrating, you can have a fully featured PDF with sources of all of the different specific information. And in this case, you can see that it's an example of technical details on GPD5. And a similar thing is it will give you all of the relevant and accurate information based on what we had asked for. Otherwise, that's pretty much it for this video. Deep Agent is just a part of a wider suite of tools that comes as a part of the subscription that you get from Abacus AAI. So, I encourage you to check out the platform, but otherwise, if you found this video useful, please comment, share, and subscribe. Otherwise, until the next