---
type: raw-transcript
title: "I Turned Claude Code Into a Complete Video Generation System (with Archon)"
youtube_id: deeOA6YVfqw
url: https://www.youtube.com/watch?v=deeOA6YVfqw
slug: i-turned-claude-code-into-a-complete-video-generation-system-with-archon
published: 2026-07-12
duration: "0:14:08"
fetched_at: 2026-07-21
immutable: true
---

## Transcript

**[0:00:00]** So over the past eight months, I have been pouring so much time into building my open-source project, Archon. And when I originally created it, I made it just for AI coding, right? It's the open-source harness builder for AI coding. It allows you to create these larger systems where you're connecting

**[0:00:18]** many coding agents together to work on longer tasks autonomously. And so, yeah, that's basically like the 5-second elevator pitch for Archon. I have a lot of content and guides on my channel now when people have been getting their hands on Archon and it's at almost 23,000 stars now. It is crazy

**[0:00:36]** to even be able to say that. So, a lot of people have been trying it out and a lot of people are using it for things other than AI coding. It's an interesting trend that I've started to see surface here where people are using it for any kind of a gentic workflow. It

**[0:00:51]** doesn't have to be just coding. We can have these longer workflows for any kinds of research tasks or content creation. It's fascinating to see how people are using Archon. And so I want to give you an example of that today. I want to show you how we can use Archon

**[0:01:05]** for other things besides coding. Now what I have for you today is not something that's production ready and extremely polished, but just consider this as more of like take a look at what you can do with Archon. It's super cool. All right, so let's get right into what

**[0:01:19]** I have for you today. I have built the AI content factory. This is like the dark factory but for generating a ton of content for the sake of marketing and it's driven entirely by Archon workflows and a platform called Higsfield which is incredible for video generation. We'll get into this in a little bit. So

**[0:01:35]** imagine with me you have a product catalog like this. Super common. All the products here are just still images, which is a fine starting point, but for the sake of marketing and for making our store come to life, we definitely want some videos here instead of just having

**[0:01:51]** still images for literally everything. Now, traditionally, paying for or creating these videos yourself is very painful. So, we definitely want to leverage AI to do this for us. And let me tell you, it's getting to the point now where AI is really good at generating this kind of content. Like

**[0:02:06]** taking a look at this here, a 10-second rendering of this feather scale, which is it's pretty basic, but you could use this for marketing material, even as B-roll. Have this on the product page directly just to make your product come more to life. It's really nice. And then

**[0:02:18]** we have the idea of UGC ads as well. So, I'm not going to play the audio here cuz I want to talk, but yeah, we have audio that goes with this video. This is generated by Higsfield and it looks nice and like the product is rendered perfectly. This is beautiful marketing

**[0:02:32]** material. And so yeah, we are using Higsfield to generate all of our content here, all of the videos. And they wanted to work with me on a video. So I thought like this was the perfect opportunity to showcase an archon workflow doing something other than AI coding. So

**[0:02:46]** combining Archon and Higsfield to build up this content factory. And the reason we need Archon in the first place is because if we have this massive catalog of dozens and dozens of products and we want to generate videos for all of them all at once, that is way too long of a

**[0:03:02]** task for a single coding agent. We need to orchestrate many agents taking items from the queue here and generating the videos. So I'll get into both of the archon workflows that drive the entire process with you. You can see that we have a parallel fan out of agents that

**[0:03:18]** are generating videos to drain the queue of products that need them. And so I have the Archon workflows and everything for this video like the catalog site and some sample videos, the Higsfield skill all in this repo that I'll link to in the description. And before we really

**[0:03:32]** get into that, let's just talk about like the first building block here. How we are using Higsfield to generate videos. And creating a video with Higsfield is as simple as just sending in a prompt for what you want to create. The more detail the better. You can do

**[0:03:45]** it right here in the Higsfield platform once you sign up. But they also have a CLI. We never have to leave our coding agent terminal to generate this content. And that's going to lead into what we're doing with Archon in a bit as well. So they have their own documentation, but

**[0:03:59]** also the two commands you need to install it. I just have right here in the readme. So do that. And then here I got the cloned version of the repo locally here. So we can take a look at the skill. use this when the user wants to generate an image video or a UGC

**[0:04:12]** style video ad. And then these are really just instructions for the agent on how to use the CLI effectively. And so going into my coding agent now, I'll paste in a prompt here. Use the Higsfield CLI to make one 10-second vertical UGC ad. A guy in a kitchen

**[0:04:28]** holding the branded Campbell Tumblr and use the branded product image as the reference, which I have that right here. You can send images and other reference material into Higsfield so that things are branded as you need. We don't want generic logos and things like that. And so there we go. So now it loaded the

**[0:04:44]** Higsfield skill and it's about to use a CLI. I'm already authenticated and everything to generate the video. So I'll come back once we have that. All right, our video is generated. Let's take a look and I'll actually play the audio this time so you get the full experience here. Okay, so I actually

**[0:04:57]** kind of love this thing. poured coffee in this morning and it's like lunch now and it's still hot. Yeah, this one's a keeper. Okay, I mean that's actually pretty good. So, I mean there's a couple of things that are a little cheesy there and it's only a 10-second clip, but

**[0:05:13]** yeah, I mean like this is just a starting point. I'm just trying to show you what is possible versus spending a ton of time like super refining a video that I make here. So, we just made this in a single prompt with Higsfield using the CLI. So, that's cool. And Higsfield

**[0:05:26]** really is the best platform I've seen for video generation. But how do we scale this? Right? Like what we did right there, prompting it a single time to create one video. I don't want to do that repeatedly. And we definitely can't have it try to generate a ton of videos

**[0:05:40]** all at once in a single coding agent session cuz it's going to get totally overwhelmed by that task. And so we need a larger orchestrator here. That is where Archon comes in because we need the workflow where we are fanning out to different workers operating in parallel. They're each taking a single video one

**[0:05:57]** at a time and the orchestrator is also going to validate everything before we just hand it back to us with the finished videos. And back to the catalog here, another thing we have to consider is that we want to sort of like validate the idea for the video before we

**[0:06:12]** generate the video because we don't want to spend the credits creating it until we are confident it's going to be a good product representation. And so we want a process of image generation, validate, then generate the video. We also have that as a part of the archon workflow.

**[0:06:26]** So it's a lot more than just like call the Higsfield CLI for each one of the videos. We have a full system here. So let's get into that. There are two workflows that guide the entire process. We have this first workflow to explore our different products and generate

**[0:06:39]** ideas. And then if I go back to the logs here, we also have the render workflow. So this one, it looks very similar because it's really just like orchestrator with our worker agents, but this one is taking the approved ideas and then creating the videos from that. And both of these workflows are using

**[0:06:56]** the Higsfield CLI to drive everything. And so yeah, let me just show you the workflow really quick. I don't need to get too much into the weeds of the YAML files for these Archon workflows. And I will link to a Archon guide right here if you really want to understand how to

**[0:07:09]** create and run these things, but let's just take a look at a high level how this works. And the cool part about this workflow and the other one I'll show you is that they're both just Ralph loops just for content creation instead of AI coding. Because a Ralph loop is a

**[0:07:23]** harness, we can build any harness in Archon. We're building a Ralph loop here for content. It's just that instead of our larger task list being a bunch of things to do in a codebase, it's the individual products we want to generate content for, generate the videos for with Higsfield. And so we look at the

**[0:07:38]** first worker here. We have this prompt for how to handle a single video. So we tell it to look at the next item in our queue. And then if there's nothing that comes back, that means that we're done. So we're just going to output complete to exit the loop. That's exactly how you

**[0:07:53]** do it with the Ralph loop. And then for all the other workers here, it's taking the exact same prompts. We just run five of the same workers at a time. So if we have 20 products, for example, it's going to run this four times, right? 5 * 4 is 20. If it's 13 products, then we're

**[0:08:09]** going to run five and then five and then three. And so it's as simple as, yeah, just take from the queue, generate the content. And the other part of this here is it doesn't just generate the video right away. We can see here that it's going to validate things by generating

**[0:08:24]** an image of the idea first and then when we approve the image. So we have our review cue right here. When we approve the image, then it's going to generate the video. And so the agent is going to do its own scoring and give recommendations to us and automatically

**[0:08:39]** scrap any images that are just really bad. And then it gives just the good ones to us to say here are the ones we want to generate into full videos, whether that's a rendering or a UGC style ad. And so then for the ones that are approved, we have the approve Q. So

**[0:08:54]** human in the loop, these are the ideas that are scored high and we deemed good enough. And so then that brings us into the content factory render, the second workflow that again is just a Ralph loop. And so this time we have a prompt here. You're one worker on the render

**[0:09:11]** line. Each run you animate one approved ad then stop right the RL loop continues with the next video in the next agent. So you claim your next approved concept. I just have a couple of Python scripts here to grab from the these markdown documents. Then same kind of thing. If

**[0:09:25]** it printed none then there's nothing left to render. So just output the promise to end the Ralph loop. And then we just have three workers working in parallel on this one just like you saw in the archon UI with the graph view of the workflow. And so this one I wanted

**[0:09:40]** to run not as many in parallel because it does take a lot more credits to render the actual videos. That's why we do the image then video uh because we get to save a lot of credits in doing so. So that is that workflow. Both of these together is all we need to go from

**[0:09:55]** idea all the way to approved and rendered video. And here I ran a live demo for you just so you can see what the process looks like when we have our coding agent run the archon workflows. It just uses the Archon CLI which in turn uses the Higsfield CLI. So I

**[0:10:10]** created a second version of the site. I just wanted to uh do a couple of concepts here just to keep it fast and simple. So it loads the Archon skill and then it kicks off the Archon workflow. So right here the workflow is launched and then our coding agent is going to

**[0:10:24]** monitor. That's how Archon workflows work is your primary coding agent like Cloud Code, Codeex, whatever is the orchestrator for the entire thing. So, it's checking in on the workflow over time, making sure that it's working well. And then finally, we get to the point where it creates the concepts, and

**[0:10:38]** then we have a call to Gemini that validates and scores the images as well. So, at this point, now we have images generated for both of our products. And it's our turn to approve things. And once we do approve things, we have that second marked document of approved

**[0:10:52]** ideas, that's when we go into the next workflow to render the videos. And then for our second workflow here, I approve some things off camera. So now we're running the content factory render. And I'll show you here in the archon logs. We can see that this workflow is

**[0:11:06]** currently running. So we can click into this to see that right now it looks like we're just waiting for worker one to finish. And then we'll be done generating all of the ideas here. So same kind of thing where it loads the archon skill. It kicks off the workflow

**[0:11:18]** after it makes sure that we have some approved ideas to run. And so orchestrator with our workers and then we'll scroll all the way to the bottom here. And yep, looks like it is in the middle of rendering our videos. And so I'll just come back once it is done with

**[0:11:31]** our workflow. All right. So let's see what was actually generated now. And and by the way, going back to the archon UI, we can see the full workflow. Like here's the flow that we just went through. And then we can look at the logs as the different workers are

**[0:11:42]** operating. So we can do this both for the explore and render workflows. And then going to our catalog here, we went from having just images to now having videos for everything. Same catalog that I showed earlier, but this time we are seeing the archon workflow run live. And

**[0:11:56]** so let's go to the Onyx kettle. This one has a new video, so let me go ahead and play this. I'll actually do the audio here. Let me do that. All right, there we go. >> This kettle made my pourover so much better. The gooseeneck gives you a

**[0:12:07]** perfect slow, controlled pour. If you're serious about coffee, get it. >> All right, that's pretty good. Yeah. And then for this one specifically, I did generate another version off camera that I thought was even better. Let me play this one here. >> This kettle made my pourover so much

**[0:12:21]** better. The gooseeneck gives you a perfect slow, controlled pour. If you're serious about coffee, get it. >> Okay, that is pretty good. You can't really tell that isn't a real person. I'm just really impressed with what Hicksfield is able to do here and then Archon orchestrating everything. Now,

**[0:12:38]** obviously, for a real product catalog, you're not just going to have like the videos in line with the images here. I mean, this isn't like a real product listing. You're definitely not going to have these ads as well, but you can think of this sort of like an admin

**[0:12:48]** view. Like, here are all of the videos you approved and generated, and now you can take these forward, creating real product pages and real ads. So, I hope you found this use case interesting. Just taking ideas from AI coding and harness engineering and the Ralph loop and applying it to something else using

**[0:13:04]** Archon and Higsfield for content marketing. And feel free to play around with everything here in the repo that I have linked in the description. I have the Archon workflows. I have some examples of how we have our explore queue. These are ideas to turn into images and then we review and approve

**[0:13:20]** things here. And then you have the approved cue that goes into the second Archon workflow to generate everything. And oh, one thing I didn't show, let me actually show this really fast. I actually have it built right into the user interface here where when the first workflow runs, it gives those ideas that

**[0:13:36]** it vetted itself. You can then approve those images directly in the UI here. So that then becomes the input into the next workflow to generate the video. So when you click approve, it updates that markdown document locally. And yeah, I mean the whole catalog here is just for

**[0:13:51]** a demonstration, but yeah, feel free to play around with this. The whole repo is at your disposal. Hope you found this interesting. And so if you appreciated this video, you're looking forward to more things on harness engineering and AI coding, I would really appreciate a like and a subscribe. And with that, I

**[0:14:05]** will see you in the next
