So, Herman, I was just looking at our storage server earlier today—the one we affectionately call "The Beast"—and it is an absolute disaster zone. I was trying to find that one specific take from our interview last month, and I realized we have about fourteen different versions of the same two-hour recording, plus all the raw video from that 4K project we did, and then all the scripts, the JSON metadata, and the AI-generated transcripts. It’s a digital landfill, Herman.
Herman Poppleberry, reporting for duty! And yes, I saw you poking around in there. I could hear the hard drives spinning from the other room. It’s funny you mention this, because it’s exactly what our housemate Daniel was talking about in the prompt he sent over this morning. He’s hitting on this massive, growing pain point: the collision of traditional code and these massive creative assets.
It really is a collision. Daniel’s prompt was specifically about how AI agents are starting to bridge that gap. He mentioned using agents to run F-F-M-P-E-G commands via natural language to automate video editing, which sounds like magic until you realize you’re generating hundreds of gigabytes of intermediate files every hour. As soon as you start treating a creative project like a software project, the infrastructure we’ve used for twenty years just starts to buckle.
It doesn't just buckle, Corn; it snaps. You see, we usually think of version control as this geeky, behind-the-scenes thing for software developers—you know, the guys in hoodies writing C-plus-plus. But as soon as you start using AI agents to edit video or generate high-fidelity assets, that line between a "creative project" and a "software project" just... evaporates. You’re writing code to manipulate pixels, and those pixels need to be versioned just as strictly as the code that moved them.
Exactly. Daniel’s big question was: where does that output go? If you try to put a four-gigabyte video file into a standard Git repository, you can almost hear the servers screaming in the distance. I tried it once back in two thousand eighteen and I think I’m still banned from that specific hosting provider.
Oh, they aren’t just screaming, they’re basically giving up. Look, Git is fantastic. It’s the industry standard for a reason. But we have to remember its origin story. It was designed by Linus Torvalds in two thousand five to manage the Linux kernel. The Linux kernel is millions of lines of tiny text files. Git is built on the architectural idea of a directed acyclic graph where every single contributor has a full copy of the entire history of the project on their local machine.
Right, the "distributed" part of Distributed Version Control.
Exactly. That works beautifully when your entire project history is a few hundred megabytes of text. But when that history includes fifty versions of a high-definition video file or a massive three-D model for a game, the whole system grinds to a halt. Every time a new person joins the project and does a "git pull," they are essentially forced to download every single version of every large file that has ever been committed since the dawn of time. It’s a recipe for a three-day download and a very angry I-T department.
So, today we’re diving deep into the world of version control systems that actually handle the heavy lifting. We’re moving beyond the standard Git-and-GitHub workflow to look at what the pros use—the people building AAA games, high-end visual effects for movies, and now, these AI-driven media pipelines.
And this is where it gets really interesting, because the solutions often involve going back to architectural ideas that Git actually tried to move away from. It turns out that for large binaries, some of the "old ways" were actually better, or at least more pragmatic.
Let’s start with the most common "fix" people try. If you search for "Git and large files," the first result is always Git L-F-S, which stands for Large File Storage. It’s the standard recommendation. But Daniel mentioned in his prompt that L-F-S feels like a band-aid. Why is that, Herman? What is the fundamental limitation there?
Well, Git L-F-S is essentially a clever hack. Instead of storing the actual large file—let’s say a ten-gigabyte video—in your Git repository, it replaces that file with a tiny "pointer" file. It’s just a text file with a unique hash. The actual big file gets pushed to a separate, specialized server. When you "check out" a specific branch, the L-F-S extension looks at those pointers and goes, "Oh, I need these five big files," and it fetches only those specific versions.
On paper, that sounds like the perfect solution. You keep the Git workflow but offload the heavy stuff. So where does it fall apart?
In practice, it’s incredibly fragile. Because L-F-S is an extension, not a core part of Git, the synchronization is prone to breaking. If your L-F-S server and your Git server get out of sync, or if a new team member forgets to run "git lfs install" on their machine, they just end up with these useless little text pointers and a broken project. I’ve seen entire production pipelines stall for a day because someone pushed a pointer but the actual binary upload failed.
And then there’s the cost. Most hosted services like GitHub or GitLab charge a significant premium for L-F-S storage and bandwidth. If you’re an AI agent generating thousands of iterations of a video, your L-F-S bill is going to look like a mortgage payment by the end of the month.
But the real, deep-seated issue is that L-F-S doesn’t solve the fundamental problem of how Git handles merging and branching with binaries. You can’t "merge" two versions of a video file. You can’t see a "diff" of two J-P-E-G images to see what changed. So, you’re using this incredibly complex, distributed system for a type of data that doesn’t actually benefit from the distributed nature of Git.
That’s a key insight. In Git, everyone has everything. But with large assets, you actually want something more centralized, right? You want a single source of truth where you only grab exactly what you need for the task at hand.
Precisely. And that leads us to the heavy hitter in this space: Perforce Helix Core. If you look at companies like Epic Games—the people behind Unreal Engine—or Sony, or any major movie studio like Disney or Warner Brothers, they aren’t using Git for their main projects. They are using Perforce.
I’ve heard about Perforce for years, but it always seemed like this mysterious, expensive enterprise tool that only people in suits use. What makes it so different from Git when it comes to these hybrid projects Daniel is talking about?
It’s built from the ground up to be centralized. It uses a client-server architecture. Instead of having the whole history on your laptop, you have a "workspace" that maps to the server. When you want to work on a file, you "check it out" from the server. And here is the killer feature for creative work: file locking.
Ah, the classic "checkout-and-lock." I know developers usually hate that because it stops multiple people from working on the same code file at once, but for a video editor, that sounds like a lifesaver.
It’s more than a lifesaver; it’s a necessity. If I’m editing a forty-minute sequence in Adobe Premiere or DaVinci Resolve, I do not want you opening that same project file and making changes at the same time. Why? Because we cannot "merge" those changes later. One of us would lose all our work. Perforce lets me put a lock on that file so the whole team knows, "Herman is working on this, don't touch it." Plus, because it’s centralized, you don’t have to download the ten-year history of the project just to fix one typo in a script. You only sync the files you actually need.
So, if Daniel is building this AI-driven creative workflow, Perforce sounds like a very strong candidate. But isn’t it incredibly difficult to set up? I mean, we’re just three guys living in Jerusalem, not a massive studio with a dedicated I-T department.
That’s the common misconception. Perforce actually has a very generous free tier for small teams. It’s free for up to twenty users and twenty workspaces. Now, setting it up does require a bit more server-side knowledge—you’re usually running a Linux box or a cloud instance—but for the performance gains you get when handling terabytes of data, it’s often worth the learning curve. In two thousand twenty-six, there are also a lot more "one-click" cloud deployments for Perforce than there used to be.
What about the middle ground? I remember we looked at something called Plastic S-C-M a while back. I think it’s officially called Unity Version Control now?
Yes, Unity bought them out a few years ago. Plastic S-C-M is fascinating because it tries to be the best of both worlds. It has a distributed mode that feels like Git, but it also has a centralized mode that feels like Perforce. It was designed specifically for game development, so it handles large binaries natively without needing a clunky extension like L-F-S.
And the visual tools are much better for non-technical creators, right? I remember seeing a branching diagram in Plastic that actually looked... readable. Not like the "spaghetti monster" diagrams you see in Git.
Oh, it’s beautiful. They have this thing called the Branch Explorer. It gives you a high-level visual map of your entire project history. For a creative director or a producer who might be intimidated by a terminal window, it’s a game changer. And they have a dedicated interface for artists called Gluon. Gluon basically hides all the complex branching and merging logic and just lets an artist see the files, check them out, and check them back in. It’s very "low friction."
That sounds like exactly what’s needed when you have this blend of code and assets. You might have a developer writing the AI scripts in one branch, and an artist or a video editor working on the assets in another, and they need a way to stay in sync without stepping on each other's toes.
And this brings up a really interesting second-order effect of what Daniel was talking about with AI agents. Think about the sheer volume of data an AI agent can generate. If you have an agent that’s constantly iterating on video clips—maybe it’s testing different color grades or different F-F-M-P-E-G compression settings—you aren’t just managing a project anymore; you’re managing a data deluge.
That’s a great point. If the AI is the one doing the work, the bottleneck isn’t the human’s ability to click "Save," it’s the system’s ability to ingest and version that output. I’ve seen some people in the machine learning space moving toward something called D-V-C, or Data Version Control. Have you looked into that for creative work?
I have! D-V-C is really cool because it’s designed to sit right on top of Git. It handles the data side of things while Git handles the code. It’s very popular in the machine learning world for tracking datasets and models. It works similarly to L-F-S in that it uses pointers, but it’s much more flexible about where the data actually lives. You can store your big files on Amazon S-three, Google Cloud Storage, or even just a local network drive in your office.
So, it’s like Git L-F-S but more agnostic and powerful?
Exactly. And it’s great for reproducibility. If Daniel’s AI agent generates a video, D-V-C can track exactly which version of the script and which version of the raw input produced that specific output. That’s vital when you’re trying to debug an AI workflow. You need to be able to go back in time and say, "Okay, why did the agent produce this weird glitch in this specific render on Tuesday?" With D-V-C, you can jump back to that exact state perfectly.
I love that. It turns the version control system into a laboratory notebook. But let’s talk about the friction. One thing Daniel mentioned is the feeling of using the "wrong tool." Even if you use L-F-S or D-V-C, you’re still fundamentally interacting with Git’s command line and its mental model. Is there a world where we just stop using Git for these types of projects entirely?
Honestly? For some projects, yes. And I’m going to say something that might get me kicked out of the "cool developer" club. There’s a reason why Subversion, or S-V-N, is still alive and kicking in certain industries in two thousand twenty-six.
Wait, you’re recommending S-V-N? That feels like telling someone to use a flip phone or a typewriter.
I know, I know! But hear me out. S-V-N is old, it’s centralized, and it’s definitely not flashy. But it handles large binary files and folder-level permissions much better than Git ever will. If your project is ninety percent assets and ten percent code, and you don’t need complex, multi-threaded branching and merging, S-V-N is rock solid. It’s easy to understand—you just "update" and "commit." Sometimes the old tools are the right tools because they don't have the overhead of a distributed system that you don't actually need.
Fair enough. But I think for someone like Daniel, who is clearly comfortable with modern tech and AI agents, he probably wants something a bit more... forward-looking. What about the newer players? I’ve seen things like Diversion or some of these "cloud-native" version control systems popping up lately.
Diversion is a really interesting one to watch. They’re trying to build a version control system specifically for this modern era of huge files and remote, distributed teams. It’s cloud-native, so you don’t have to manage a server like you do with Perforce, but it’s designed to handle binaries as first-class citizens. No pointers, no hacks, no separate L-F-S extensions—just a unified system where code and assets live together in harmony.
And that’s the dream, isn’t it? A single source of truth where a one-kilobyte Python script and a ten-gigabyte Pro-Res video file live side-by-side and are treated with equal respect by the system.
It really is. And as we see more of these AI-to-video workflows, the need for this is only going to grow. Think about the latency, too. If you’re in Jerusalem and you’re working with a collaborator in New York on a massive video file, Git is going to be a nightmare. You need a system that supports things like edge caching or partial syncing.
You mentioned edge caching earlier. How does that actually work in a version control context? Is it like a C-D-N for your files?
Sort of! Imagine you have a local "proxy" server in your office or your house. When you sync a large file for the first time, it’s pulled from the main cloud server once and cached on that local box. Then, when I want to sync that same file on my computer, I get it at gigabit speeds from our local network instead of downloading it over the internet again. Perforce has this down to a science with their "commit and edge" server architecture. It’s why big studios can have offices in London, L-A, and Tokyo all working on the same movie without losing their minds.
That sounds like exactly what we need for our house, actually. We’re always fighting for bandwidth when we’re both trying to upload or download large project files for the show.
We really should look into setting up a local Perforce edge server. It would save us so much frustration. But back to Daniel’s point about AI agents. There’s another layer here, which is how these systems integrate with the tools themselves. If you’re using an AI agent to edit video via F-F-M-P-E-G, that agent needs to be able to talk to the version control system directly.
Right, it needs a robust A-P-I. Git has a great ecosystem there, obviously, but what about the others? Can an AI agent "check out" a file in Perforce?
Absolutely. Perforce has a very mature A-P-I, and Plastic S-C-M has a pretty good one too. But this is where the complexity starts to ramp up. You have to write the logic that tells the AI, "Okay, before you start editing this clip, check out the file and lock it so Corn doesn't try to edit it at the same time. When you’re done with the F-F-M-P-E-G render, commit it with a descriptive message about what you changed."
Can you imagine an AI agent writing commit messages? That might actually be the greatest contribution of AI to software engineering in history. No more commit messages that just say "fixed stuff" or "update" or "asdfghjkl."
Ha! True. The agent could actually analyze the video "diff" and write a message like: "Adjusted the color grade on the second scene to increase the warmth by fifteen percent and cropped the frame to a sixteen-by-nine aspect ratio to match the master delivery spec." That would be incredibly useful for a human supervisor to read.
It really would. It turns the version control history into a readable log of the creative process. So, if we were to give Daniel a roadmap, where should he start? If he’s hitting the limits of Git L-F-S today, what’s the next logical step for a small-scale, AI-driven creative project?
I think the first question Daniel needs to ask is: how much do I care about branching? If he’s doing complex, multi-threaded development where he needs to merge code constantly, he might want to stick with a Git-based workflow but move to a more robust backend. Maybe look at a dedicated L-F-S provider like Xet-Data or even D-V-C if he’s doing a lot of AI training.
But if the assets are the star of the show? If it's mostly video and audio?
Then I’d say jump straight to Unity Version Control—the artist formerly known as Plastic S-C-M. It’s probably the most user-friendly transition from Git. It feels modern, it handles large files natively, and as we mentioned, the visual tools are fantastic for creative work. It’s also very well integrated with the major game engines and creative suites like Unity and Unreal.
And if he’s looking for something truly professional-grade and doesn’t mind a bit of a learning curve to get that "industry standard" power?
Then it's Perforce Helix Core. It’s the gold standard for a reason. Once you get past the initial setup, the performance and the peace of mind you get from file locking and centralized management are hard to beat. Especially if you’re working with those massive F-F-M-P-E-G outputs Daniel mentioned. It can handle millions of files and petabytes of data without breaking a sweat.
I’m curious about the cost aspect again. For a solo creator or a tiny team of two or three people, are these really viable? Or are we talking thousands of dollars a month?
For a lot of them, they are surprisingly affordable. As I mentioned, Perforce has that free tier for up to twenty users. Unity Version Control has a free tier for small projects too. The real cost often ends up being the storage itself. Whether you’re on GitHub, Perforce Cloud, or Amazon S-three, someone has to pay for those terabytes. But honestly, Corn, the time you save by not fighting with your version control system is worth the few dollars a month in storage fees.
That’s a great point. We often forget that our time is the most expensive part of the project. If I spend two hours trying to fix a corrupted Git repository because of a large binary merge conflict, I’ve already "spent" more than a year’s worth of storage fees in lost productivity.
Exactly! And that’s the hidden trap of trying to force Git to do something it wasn’t meant to do. It feels "free" because GitHub is free for basic use, but the technical debt you accrue—and the stress of wondering if your files are actually safe—is very real.
Let’s talk a bit more about the AI side of this. Daniel’s mention of F-F-M-P-E-G is really specific. F-F-M-P-E-G is this incredibly powerful command-line tool that can do almost anything with video, but it’s notoriously difficult to learn. The syntax is like a secret language. Using an AI agent to bridge that gap is brilliant. But it also means you’re generating a lot of intermediate files, right? You might run a command, see the result, tweak it, and run it again.
Right, and in a traditional Git workflow, you’d probably just ignore those intermediate files. You’d put them in your "git-ignore" file. But with an AI workflow, those intermediates might be the most important part! You might want to see the evolution of the AI’s "thought process," or go back to a version that was almost right but had one specific glitch you liked.
So, you need a version control system that’s fast enough and cheap enough that you don’t feel "guilty" about committing everything.
Exactly. And that’s where the centralized systems shine. Because they only store the "deltas"—the changes—or the specific versions you ask for, they can be much more efficient. Also, some of these newer systems are looking at block-level deduplication.
Explain that for the non-engineers. That sounds like something that would be incredibly useful for video.
Okay, so imagine you have a ten-gigabyte video file. You make a tiny change—maybe you just change the metadata or add a small watermark in the corner. In a basic system, that’s a whole new ten-gigabyte file. But with block-level deduplication, the system looks at the actual chunks of data. It realizes that nine-point-nine gigabytes of the file are identical to the previous version. It only stores the small chunk that actually changed.
Wait, does any version control system actually do that for video files effectively? I thought most video formats were compressed in a way that even a small change would ripple through the whole file and change everything.
It depends heavily on the format. If you’re using uncompressed or lightly compressed formats like Pro-Res or D-N-x-H-R, deduplication can be very effective. For highly compressed formats like H-two-six-four or H-two-six-five, you’re right, a small change can change a lot of the subsequent data. But even then, some advanced systems can find similarities. This is a huge area of research right now, especially as we move toward AI-generated content where we might have millions of slightly different versions of the same asset.
This is where the future of this stuff gets really wild. If the AI is generating the content from a script, maybe the version control system doesn’t need to store the video file at all? Maybe it just stores the prompt and the seed?
Now you’re talking! That’s the ultimate version control for the AI era. Instead of a ten-gigabyte file, you store a hundred-byte text string. As long as the AI model is deterministic—meaning it produces the exact same output for the same input every time—you can recreate that exact file whenever you need it.
But that’s the catch, isn’t it? Determinism. As these models get updated or if you change the hardware—like moving from an Nvidia card to a different chip—the output might change slightly. So, you still need to cache the actual asset somewhere if you want to be one hundred percent sure you have it.
Right. It’s the classic trade-off between "compute" and "storage." Do you spend the electricity and time to re-generate the file, or the money to store it? In two thousand twenty-six, storage is still relatively cheap, but the environmental and time cost of massive AI compute is becoming a real factor.
It’s a fascinating balance. I think for Daniel’s current workflow, he’s in that middle ground where he’s using AI to manipulate existing assets. So, he still needs to manage the raw files and the outputs.
Definitely. And I think there’s one more thing we should mention, which is the cultural shift. If you’re coming from a pure coding background, moving to something like Perforce can feel restrictive. It feels "corporate." If you’re coming from a creative background, moving to Git can feel like learning a foreign language where every word is a trap. The best teams are the ones that find a way to bridge that gap, often by using multiple tools.
You mean like using Git for the code and something else for the assets? How do you keep them in sync? That sounds like it would double the work.
It’s called a "hybrid workflow," and it’s actually very common. You might have your Python scripts and your F-F-M-P-E-G logic in a GitHub repository because you want to use GitHub Actions for your automation. But then you have a separate Perforce "depot" for your raw video and your high-res renders. You use a simple naming convention or a small script to link the two. For example, a commit message in Git might reference a specific "change-list number" in Perforce.
That sounds like it could get messy if you aren’t disciplined. I can barely remember to name my layers in Photoshop, Herman.
It definitely requires discipline! But it’s how almost every major game is made. The programmers live in the Git world, the artists live in the Perforce world, and they meet at the "build server" which pulls from both. For a solo creator like Daniel, it might be overkill, but as soon as you add a second or third person to the team, you need that structure.
It’s funny, we started this talking about Daniel’s project, but I’m realizing this applies to almost everything we do now. Even our podcast. We have the show notes, which are basically text code, and then we have these massive multi-track audio files. Maybe we should be practicing what we preach and moving our own workflow off of just basic cloud storage.
I’ve been saying that for months, Corn! We should set up a Unity Version Control repository for the podcast. It would make it so much easier to track the different edits and the raw takes. Plus, I really want to play with that Branch Explorer and see our "creative journey" visualized.
Alright, alright, you win. We’ll look into it this weekend. But back to the prompt—I think we’ve given a good overview of the landscape. It’s not just Git anymore. There’s a whole world of tools designed for this exact problem of "code meets pixels."
And I think the most important takeaway for Daniel, or anyone in this position, is to not be afraid of the specialized tools. Git is amazing, it changed the world, but it’s not a religion. It’s okay to use something else if it fits your data better. If you’re building the future of AI-driven media, you need a foundation that won't crumble under the weight of your own creations.
Well said. And hey, if any of you listening have found a perfect way to manage your hybrid projects—maybe you’ve found a way to make Git L-F-S work perfectly, or you’ve discovered a new tool we didn't mention—we’d love to hear about it. Or if you’ve tried one of these tools and had a total nightmare experience, that’s also good to know. We love a good horror story.
Oh, I’m sure there are some Perforce horror stories out there. It’s a powerful tool, but like any power tool, you can definitely take a digital finger off if you aren’t careful with your workspace mappings.
Speaking of people listening, we’ve been doing this for over five hundred episodes now, which is just wild to think about. It’s always amazing to see the community that’s grown around these weird prompts. If you’ve been enjoying the show, we’d really appreciate it if you could leave us a quick review on your podcast app or over on Spotify. It really does help other curious people find us in the vast ocean of content out there.
It genuinely does. We check those reviews every week, and it’s always great to see what’s resonating with you all. And if you want to reach out or see the archives of our past five hundred plus episodes, you can find everything at my-weird-prompts-dot-com.
We’ve got the full R-S-S feed there, and a contact form if you want to send us a prompt like Daniel did. We’re always looking for new rabbits to chase down, especially the technical ones.
Especially if they involve technical deep dives and a bit of brotherly disagreement over server architecture.
Exactly. Well, I think that covers it for today. This has been My Weird Prompts. I’m Corn.
And I’m Herman Poppleberry. Thanks for listening, and we’ll catch you in the next one.
Bye everyone.
Goodbye!
