Big Idea

The argument in one line.

Giving an AI agent the same handful of file tools (list, read, write, search) but routing them through a swappable backend, local code or a remote API, lets one agent's code scale from a single laptop to dozens of concurrent cloud agents sharing a synced, conflict-resolved codebase.

Who This Is For

Read if. Skip if.

READ IF YOU ARE…

You're building a SaaS or desktop app where an AI agent has to edit files that also need to exist on a server, in a database, or on more than one machine.
You already understand basic Claude/Anthropic tool-calling (system prompts, tool_use blocks) and want to see how a real production file-access layer is built on top of it.
You're running more than one coding agent against the same codebase (cron jobs, background workers, parallel Claude Code sessions) and have hit file conflicts.

SKIP IF…

You run a single Claude Code session locally with no server, no scheduled jobs, and no concurrency; this problem doesn't exist for you yet.
You want finished, installable code. This is a whiteboard architecture walkthrough. No repo or package is shared. The creator asks for comments before writing it.

TL;DR

The full version, fast.

An AI coding agent only ever gets a small set of file tools: list, read, write, search. Locally those tools just run code against the disk. The moment an agent needs to run on a server, in a cron job, or as one of many concurrent sessions, the same tool calls have to be re-pointed at a remote API instead, without the agent ever knowing the difference. The proposed system, phantom-fs, gives every agent session an isolated Docker workspace, keeps a pool of pre-checked-out Git branches so new sessions start instantly instead of waiting on a clone, reconciles changes back to GitHub automatically, and layers a Postgres-backed vector/BM25 search on top so agents can search content, not just grep filenames. The payoff: any number of agents, on any machine, can work on the same live codebase at once without stepping on each other.

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Chapters

Where the time goes.

00:00 – 01:02

01 · The Vision for a Unified Agent File System

Opens with the core problem using his own desktop app, Shockwave: an agent editing local files works fine until the app also needs a server-side agent (for cron jobs) touching the exact same files.

01:02 – 04:02

02 · Why Local File Access Isn't Enough

Extends the problem to any SaaS with a database (using a Descript-style video editor as the example): once an agent needs to edit files that live in a database or on a deployed server, you need a real file system layer, not just local disk access.

04:02 – 07:31

03 · Deconstructing the Agent API Tool Call

Builds a minimal Anthropic API call live in Claude, defining an 'ls' tool and the local exec code that answers it, to show concretely what a tool call actually is underneath a framework like Claude Code.

07:31 – 10:57

04 · Scaling to Multiple Concurrent Agents

Converts the local ls implementation into a call to a remote API instead, the pivot that lets any number of agents on any machine hit the same file backend without the agent code changing.

10:57 – 16:00

05 · Managing State with Git and Pre-Checked Pools

Explains how phantom-fs assigns each agent a session ID, checks out a Git workspace on first use (from a pre-warmed pool for speed), runs each agent's commands inside an isolated Docker container, and reconciles changes back to GitHub with a background 'Git fixer' process.

16:00 – 19:28

06 · Integrating Advanced Vector Search Capabilities

Adds a Postgres-backed vector and BM25 search layer on top of the synced files, then closes by naming comparable projects (Daytona, Vercel Sandbox) and pitching his AI Architects community.

Atomic Insights

Lines worth screenshotting.

An agent's file tools (ls, read, write, search) are just a thin wrapper around your own code; the agent has no idea whether that code touches a local disk or calls a remote API.
Every Claude API call is actually a single, independent request that happens to carry the entire conversation history with it; there is no persistent connection between turns.
Running multiple Claude Code instances in the same folder at once can cause them to overwrite each other's edits, which is a live version of the exact conflict problem this architecture is built to solve.
Checking out a fresh Git repo for every new agent session is too slow at scale, so the fix is a standing pool of already-checked-out branches ready to hand an agent the instant it connects.
Running each agent's file operations inside its own Docker container means an agent can only see its own working directory, so it cannot read or corrupt another agent's files even by accident.
Every workspace also reconciles against GitHub in the background, since local, uncommitted changes are invisible to any other agent or developer who pulls fresh from the remote.
Plain command-line search (ls, grep) is enough for most agent tasks, but finding meaning inside a three-month-old transcript needs vector search, which is why a Postgres-backed index runs alongside the Git-synced files.
The entire file-access layer is meant to ship as a small SDK that plugs into any agent harness, Claude Code, the Vercel AI SDK, or others, so the agent code itself never changes.
The two closest existing systems, Daytona and the Vercel Sandbox, solve pieces of this same problem; this design borrows from both but combines file sync, isolation, and vector search in one layer.

Takeaway

One tool interface, two backends: local disk or shared API.

WHAT TO LEARN

An AI agent only ever calls a small set of file tools, and swapping what runs behind those tools, local code or a remote API, is what lets the same agent scale from one laptop to dozens of synced, conflict-free cloud sessions.

01The Vision for a Unified Agent File System

The agent editing your local files and the agent editing files on your server are the same code with a different backend; the tool definition ('ls', 'read', 'write') never has to change.
One agent working alone rarely hits this problem; it shows up the moment you need files in two places at once, or more than one agent touching the same codebase.

02Why Local File Access Isn't Enough

A SaaS with a database of user content (video, transcripts) hits the same wall as a desktop app: an agent that needs to edit those files needs a real, segregated file system, not just disk access.

03Deconstructing the Agent API Tool Call

The exact same failure mode already exists in ordinary use: running multiple Claude Code sessions in one folder can cause them to overwrite each other's edits.
Every API call to an LLM is a single, stateless request carrying the full conversation; there's no persistent session on the model's side, only in your own bookkeeping.

04Scaling to Multiple Concurrent Agents

Swapping the tool's implementation from a local filesystem read to a remote API call is the entire trick that lets one agent's code run identically whether it's alone or one of many.

05Managing State with Git and Pre-Checked Pools

Cloning a fresh Git repo for every new agent session doesn't scale, so a pool of pre-checked-out branches, handed out instantly and refilled in the background, is what keeps agent start-up fast.
Isolating each agent's file access inside its own container (Docker in this case) is a structural guarantee against cross-agent interference, not just a convention you hope agents respect.
Uncommitted local changes are invisible to anyone who pulls from GitHub, so a background reconciliation process is needed to keep the shared source of truth current and resolve conflicts.

06Integrating Advanced Vector Search Capabilities

Keyword search (ls, grep) covers most agent tasks, but finding something by meaning rather than exact wording, like an old call transcript, requires a vector search layer on top of the file store.
The intended shape of the whole system is a small SDK dropped into any agent harness (Claude Code, the Vercel AI SDK, others), so the underlying complexity stays invisible to the agent itself.

Glossary

Terms worth knowing.

phantom-fs: The creator's in-progress project name for a file-system API server that gives AI agents shared, synced access to files across local machines, servers, and concurrent sessions.
Tool call: A structured request an LLM emits (e.g. 'call ls with path=.') that your code executes and returns the result of, letting the model interact with things outside its own text generation.
Session ID: A unique identifier the API server assigns to each agent's conversation so it knows whether a workspace already exists for that agent or needs to be created.
Pre-checked-out pool: A standing set of Git branches that have already been cloned and prepared in advance, so a brand-new agent session can be handed a ready workspace instantly instead of waiting for a fresh checkout.
BM25: A classic text-ranking algorithm used by full-text search engines (including Postgres) to score how well documents match a keyword query, as distinct from vector-based semantic search.
Vector search: A search method that finds content by semantic similarity rather than exact keyword match, useful for finding information in things like old call transcripts where the wording won't match your query.

Resources

Things they pointed at.

00:00toolClaude Code

04:05toolAnthropic API

13:00productphantom-fs

18:10toolDaytona

18:15toolVercel Sandbox

18:50toolVercel AI SDK

18:40toolDeepSeek harness

19:10productAI Architects (Skool community) ↗

19:20productContent Academy (free community) ↗

19:20channelTwitch livestream (M-Th 10am Pacific) ↗

Quotables

Lines you could clip.

05:00

“Each message is actually just one API call, which is always kind of weird to think about. You're not actually having a conversation with an agent. You're sending a single API request that just happened to have the entire conversation.”

reframes a widely-misunderstood mental model of how LLM chat actually works→ TikTok hook↗ Tweet quote

08:10

“This can actually happen in cloud code. If you're running cloud code in multiple terminals on the same folder, multiple cloud codes can be interacting with each other and changing code at the same time.”

names a real, relatable failure mode viewers have likely hit themselves→ IG reel cold open↗ Tweet quote

11:40

“If you have three agents working on the same file, all doing different things, well, there's liable to be conflicts there. Git is actually very natural at helping resolve that and track it.”

crisp statement of why Git, not a custom system, is the right tool here→ newsletter pull-quote↗ Tweet quote

17:30

“By abstracting these tool calls and the whole file system environment from the agent, the agent still feels like it's a local file system, but it's not. This is happening on a server somewhere. It's going to feel like it's just a hard drive in a computer.”

the single sentence that summarizes the entire video's architecture→ TikTok hook↗ Tweet quote

The Script

Word for word.

Read-along

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See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.

metaphoranalogy

What I want to show you guys today is this concept that I've been kind of designing it in my head. And I do have a cloud code session that's like all ready to go. But what I've been building as a result of building all these different agents is a agent file system.

And there's a bunch of other ones that are out there that help you do this in many different ways. And I'm going to go back into some like real basics to kind of show why this is important and why it's interesting. I've been building this for a while.

mostly because what I've realized is that when you are building agents, let's say you're building a SaaS application. In this case, I have Shockwave, which is a desktop application, right? There's a lot of situations that you will find yourself in where the agent and the files that the agent works on need to be in different places at different times.

So in Shockwave, for example, this is an application that runs on my desktop, but I've got an agent off here to the right and I can talk with it. It drew this diagram and created a file that I could actually show you guys today. The agent is working locally in my files.

If I come here and... reveal in the finder. You know, this is an actual directory on my computer and the agent's working in there.

That works fine. But now the problem is, is that this Shockwave app also has a server component. So we've got the desktop here.

This is Shockwave. It's also got a server. And this server is there so that it can run cron jobs.

Like, so if my computer is off and I have cron jobs or scheduled jobs that work on these files and need to do something, create reports in the middle of the night, whatever that is, there also has to be an agent on a server out in the cloud somewhere. that's able to run at any time and it needs the same files. So we're in this situation where we've got files that need to be in multiple places.

And not only do they need to be in multiple places, but they need to be up to date very quickly. Like I can't make an edit here on my desktop and then it takes three hours for the server to get it. The server runs a process and then the file isn't there.

It needs to be almost immediate. So that's just one example, but let's think about like whether, let's say you created a SaaS application. Let's say I created a content creation.

app and so you've got users coming in into your app and you've got a database that maybe is storing all of their videos and descript is probably a good example of this it edited out all this kind of stuff from my live show so that i could create a youtube video all automatically so it edited this right so the files are all in a database but now if you wanted an agent to be able to do that without having to build all the features you could just have an agent that has the ability to create a transcript from a video and then it could use that transcript so let's say your sas is offering this feature that allows them to edit a video it's going to look for dead space or something like that so an agent could just come here, download it, create the transcript.

It's got all the timestamps, and then it could use FFmpeg to edit the video, and then it could come back and update the database. These files need to be edited by an agent, right? And where is that happening, right?

So you have a SaaS software, maybe you deployed something to Vercel or whatever. You need these files in a file system so that the agent can actually work on it. And they have to be segregated from everybody else's files.

So you're in this situation where, yeah, you want to add this agent to your system, but you've got to manage a lot of stuff now. Just deploying an app with a database is pretty simple. But once you have a file system where an agent needs to be able to edit files, it becomes a lot more complex.

And this is designed to help with that. Now, in general, again, like if you're building an agent and it's working locally, you don't have to worry about this too much. But what you will find is that when you are building this stuff with software, the problem is gonna come up pretty quickly because you're gonna wanna have files at different places.

You're gonna want multiple agents being able to work on the same set of files on different machines. But let's actually break this down a little bit. I'm going to come up to Cloud.

We're going to actually look at an API call that Cloud Code might make to Anthropic that would allow it to look at your file system, like look at the files and use them. Build me a Anthropic API call that asks the LLM to give me a list of files in the current working directory, and then create a LS tool call that the agent can run to answer that question.

So this is gonna be cool. Cause I'm gonna show you how tool calls work. Like if I just say, you know, show me all the files.

This is basically what I'm replicating here. Show me all the files in this working directory. Now, what I'm gonna show you here is drastically simplified from what Cloud Code has set up because Cloud Code has a ton of tools, not just the ability to look at the files.

So if we look at the API call and try not to get overwhelmed. by what I'm looking at here. Sometimes when you're looking at all this stuff, it can kind of look like Greek, right?

Essentially what happens here is that when we create an agent, that is able to actually look at the file system. Here's the actual API call.

And essentially this is the way most APIs work is that you're basically sending it a message. You're telling it what model to use, how many tokens it can use. And you're giving it a list of tools that it can use.

And then you're sending it the conversation that's going back and forth, right? Each message is actually just one API call, which is always kind of weird to think about. It's like, you're not actually having a conversation with an agent.

You're sending a single API request that just happened to have the entire conversation. And then the API responds back to you with the final message. after that, right?

So every API call is actually independent from any other ones. It's just that you keep sending the entire conversation back and forth over and over again. So now in this situation, back up here, remember, listen, this is where we gave it the tools.

So we come back up here. We gave the agent one tool and it's called LS. And that's equivalent to going into the terminal here and typing LS like this.

And what does LS do? It just gives you a list of the files. We are literally giving the agent a tool and then we're writing the code that's essentially a simulating an ls command from the command line and that's what this is here this is what was missing when i was analyzing the code i was like where's the actual tool call right and so this is running code that is reading the directory and ultimately returning the results so when you ask the agent in this message here that we're sending this api call and that message is hey show me the files that are in the directory What the agent is doing is it's saying, okay, it's asking, the user's asking for me to list the files.

And oh, look, I have a tool. It's called LS. This is the description of this tool says, list the contents of a directory on a local file system.

And so then it knows like how to take that and then send a command to the actual code. And then it gives that back to the agent. And then the agent can then finally respond.

It can say, okay, here's all the files. Now that's what they wanted to know. So now I'm just going to list the files for them.

And so that's for. looking at the files and then you need other things right you need to be able to search the files you need to be able to read files and write files right so a typical agent has tools that do like the typical things that you would do in a command line like looking at files or finding files or searching through files a lot of what an agent is and the basic tools that it usually gets are all just like the basic tools that a human would normally use to interact and work on the computer and this is all local right this code is going to run locally if you're enjoying this video make sure to like and subscribe it tells me what type of content you want more of and so now here's where it gets kind of interesting and gets back to this whole discussion of why i would build this more advanced system this whole system and cloud code.

It works really well when all of the files are local. They're on this computer because I can just write code that executes locally. But now what if I want to have a hundred or let's just say 10 agents running at the same time and they all needed to work on the same list of files.

So now these are all on different computers. We want these agents to be able to interact with those files. And then when it changes it, we want those changes everywhere else as well.

Because if agent three changes. The files here, well, agent one and two need those files very quickly. This is no longer gonna work because this is only working on files that are local.

Well, this is where it gets kind of interesting. This ls command and the code here, the agent doesn't know anything about it. It just runs it and gets the result.

So whatever happens here doesn't really have to be a local file system. Instead of that code that we wrote in the agent, just looking at the local file system.

Instead, it's gonna make an API call to a remote server saying, hey, I'm on this workspace, give me a list of the files, but it's calling an API instead. And then we'll get into like these other features and why they're there and why they need to be there and what makes doing what we're doing. kind of complex because there's performance related issues, right?

Like now we're asking an API for the files. And so now the API has to have those files. And there's some complications that we're going to get into that will be interesting.

So now what it did, if you look here now that LS command, it's no longer actually looking for the files locally. Now it's building an API request and it's going to look out to another server to get that back, but then still display it as if it was just a simple. ls call this whole idea of getting a list of files it's all abstracted what actually happens here in this function that gives you back those files could be anything and that's what's kind of interesting is that the agent has no idea where these files actually are it just calls the tool that says that it's going to give them that so now if you install the phantom sf library it's going to give you an api server with all of this functionality behind it it's going to expose the basic tools that the agent wants read files search for files search within files all of those basic things very standard at this point with ai agents okay but then it's going to facilitate this more complex thing on the back end right because now what this is illustrating is that we might have multiple agents here so this agent here that's running we might have 10 of them okay dot dot dot

and they're all going to be accessing the same api server if you've ever built an api or you've done anything like that or you've used one you know that they expose different functions right slash ls slash right so we're going to expose these functions and then as they come in here then the server can kind of work on this and make it work well Now, the thing is, is that if we have a bunch of different agents, they can't all be working on the same folders, right?

Because this one could be doing, you know, job one, and this one's doing job two, and they might be totally different. And if they're modifying the files on each other, then they can get in fights. It's like one is not expecting certain things to happen.

This can actually happen in cloud code. If you're running cloud code in multiple terminals on the same folder, multiple cloud codes can be interacting with each other and changing code at the same time, which cloud code is pretty good at noticing that that's happening and recovering. not really actually a good scenario so what ends up happening with phantom fs is that as these api calls are coming in it's giving each agent its own session id and then based on that it will say hey is this the beginning of a conversation right so Let's say that an agent asked for LS.

Say, where are all my files? Well, if this is the first call, the first tool call in a conversation, well, we haven't built the workspace yet. We don't have the files.

So very quickly, what needs to happen is we need to get those files, right? And the way Phantom FS syncs these files and keeps track of them is through Git. And that's pretty common on all these project files.

Even Shockwave itself uses Git to basically store and keep track of all of the different files that the agent is using. And Git's really good because... You can keep track of them.

If you go to a Git repository, I'm just going to go to Shockwave. If I go to the commits here. So when an agent is working on files, Git is nice because it's showing you a reflection of how everything changes.

And it's also very good at handling conflicts. And if you have three agents working on the same file, all doing different things, well, there's liable to be conflicts there. And so Git is actually very natural at helping resolve that and track it.

And an agent comes along and says, okay, I need, I'm going to run an LS. Comes in this way. Well.

This is a brand new session. So we don't really have the files yet. So what could we do?

So at this moment, we could check out the repository. If I start a new conversation on Shockwave over here, come over here. If a cron job dubs, it can just check out that code and it has it.

And it's being kept up to date. But the problem from a performance standpoint is that that checkout process can actually take a bit of time. And the more files...

that you have in here the longer that's going to take when a new session is created you don't want this process to like take a minute right right like the agent's like hey show me all the files and then a minute later it's like oh here it is that's like that's just pretty slow so what ends up happening is that the the file system ends up creating a pool of like ready to go checked out branches so that when a new request comes in it can just pull that in it knows it's current and then it can start working on it and then once the session is created it can continue to use that folder from that point on So a new chat is created by an agent.

The API server figures out what command it was. Was it like, do I need to list the files? Do I need to write a file, read a file?

If that workspace doesn't exist because it's a brand new chat, well, it has ready to go get folders. So what do I mean by that? Like it literally checked this out three times.

There's a pool of pre -checked out repositories ready to go so that when an agent starts a session, it's not like downloading it. Now, like this particular repo is pretty small. So this would only take like a couple of seconds.

but when you're talking about agent experience and keeping things quick then it becomes a bigger deal let's just say what if it was a gigabyte well that's going to take a substantial amount of time to download and get ready now when we actually execute the command like the agent that says hey give me the files with an ls the session didn't get wasn't there so it created it it took one of these pulled it in now we've got the file system and now it actually has to run the ls Well, that happens here inside of a Docker container.

I didn't have to use this. So the API server could technically just do an LS itself. And if you think back to our discussion over here, remember we had that code.

Here's that code. So remember we created the local LS that worked locally and then we created the API version, right? If you look here, the API is now gonna be running the real code that gets the LS.

Now, the reason why I'm using a Docker container is because it's really more for privacy. If every command is run through a Docker container, well, then I can essentially hide the working files that each agent has from other agents. So I don't really have to have this.

The API server could have the files and it could just ls and work on the file directly but what that would allow an agent to do is it would allow one agent to peek into other agents work there might be a good good reason why you want all of your agents to be able to interact with all the other files of another agent and who knows maybe i i could actually make this an option i could say hey work in the docker container or don't but by having the docker container what this does allow me to do is that it can serve the ls through the docker container and then when the docker container looks at the files it can literally only see its own working directory it can't unless it becomes a hacker it can't go into other directories and do anything like that and so that's all it is right so ls commands come in you could have 20 different agents across those different agents they could um they could have like 40 different chats open and so it's just like creating those working spaces and at some point down the line like if an agent isn't working on that file anymore or that working directory basically you know it's kind of like with with clot right you create all these different chats

and then at some point for whatever reason your chats kind of just go to the bottom you're done with it at some point you can clean those up just for for disk space the one final piece that this needs as the agent is changing files here they're all local they're all checked out in the local repository right a continuation of just like how git works when we are in a git repo here this is on github when we check it out onto our computer and we make a bunch of changes here on our computer these are not on github so if another programmer or another agent comes and downloads the the source from github this right here these changes that we just made won't be there.

What I have found out, and this just kind of took a little bit of time for me to build, is that I have this little Git fixer. It's like this little agent that runs on each working directory, and it's just monitoring the changes. And it's comparing that back with like the true source, right?

GitHub is the true source. So you've got this little Git fixer that kind of monitors this and just make sure that this is up to date and a very speedy, clean. way and then when there are conflicts again because when you have 20 agents working on the same files it's possible that they both modify the same file and there is something to resolve there so that's what this is doing here but it also gives us some abilities to do some other things as well there's also a postgres server in this api in phantom fs that does some cool stuff as well so we've got all the files right so we know agents are really good at using files in cloud code or a para system in a second brain and how you organize yourself agents are really good at going into those and searching and finding things and keeping things up to date and linking things, all that kind of stuff.

It still doesn't necessarily have like a vector search. Remember like a year ago or whatever with N8N, everyone was talking about the vector searches. There's really a lot of different ways to search your data.

There's a BM25. Being able to access your files and data, using the typical command line things like Cloud Code does, it works really well. But there are other times where you might want to do a vector search where you're trying to find some information from some transcript and some Zoom call that was three months ago or something.

That's where you can use a vector search where you can search the database. What's also happening here too is that for every project that you have, there's also this other process that is basically looking at this GitHub repository and it's maintaining a database version. of the same data we can also expose other tools to the agent that gives us access to the file system but in a different way obviously we can have all of the other tool calls like ls which is like list files grep which is like search in files read and write the basic ones right but now imagine you can just like there's another tool called vector search a full index search like just like you know Databases have their own search.

It's just like an index. It's just looking for files or it's just looking for strings inside of a database, right? So what this portion of the process does is that it essentially goes through every single file as it's changed and it's keeping a database of all the files as well.

So the agent will have tools like vector search. And when they call that to the API, instead of it going here and actually doing that, it will come to the database. It will do that type of search there and it will respond back.

this way. So by abstracting these tool calls and the whole file system environment from the agent, the agent still feels like it's a local file system, but it's not. This is happening on a server somewhere.

but it's going to feel like it's just a hard drive in a computer. And not only do I have the advantage of having the files and having them all up to date and having them all synced automatically, being able to run 25 agents all at the same time on the same file system without having to worry about any type of conflict, I can also take advantage of all the other cool ways of searching files for indexing and, you know, like doing a vector search or doing just like a BM25 search, a typical search that Postgres would offer you.

So I can get at these files. and pull data in a lot of other ways. And this is just like an add -on bonus that's just there automatically because I've set all this up.

So again, there's some other projects that you might want to take a look at. There's Daytona, there's the Vercel Sandbox. Ultimately, why I built this and why it's just a little bit different is because there's elements of this that are pulled from and shared with these other platforms, but I've just never seen it put together exactly like this, which is exactly how I want it.

And so the way this would actually work is if you wanted to use Phantom FS, there would be a server to install, which would be... everything here. And then there would be a very small SDK that you would give your agent that would basically give it all of these abilities.

So if you were using the deep seek harness, it would be very easy to insert these new tools. I have to create a tool so that when you do an LS, it hits the API and it knows how to do the authentication and it knows what to send the API so that it gets the right response. There's also gonna be a vector tool.

There's also gonna be a search tool, all of those different things. Or if I were to use the AI SDK, these are all different. Agent SDKs, right?

So we have the deep seek harness and it would basically just be a small library of code tools You would basically just be able to plug in and then this whole thing would just be kind of automatic But once you have this then you can pretty much build any software all of the agents that you might have run can all access the same file system, the same files from any machine.

Plus you have additional stuff like vector search, all that kind of stuff, all built in without having to think about it. It just automatically works. And that's pretty valuable because setting this up and getting it to work is not easy.

There's a lot of little things. to hash out. Now you've probably noticed the endless stream of new AI content that keeps you overwhelmed.

If you wanna step off that treadmill and learn to build with someone that didn't just pick up cloud code last week, someone who's been coding for over 30 years, the AI Architects is for you. It's a community of builders shipping real things. We do a weekly round table like a think tank.

I do live builds in there and there's a course that walks you through everything end to end. I'd love to see you inside the community. I hope you enjoyed this video and I'll see you on the next one.

The Hook

The bait, then the rug-pull.

He's built a handful of agent-powered apps and kept running into the same wall: the files an agent needs to touch never stay in one place. So he sketched a file system made for agents instead of humans, and walks through the whole thing on a whiteboard before a line of it ships.

Frameworks

Named ideas worth stealing.

05:00concept

Swappable tool backend (local exec vs. remote API)

The agent is only ever given a tool name and description (e.g. 'ls'); the function that actually answers it can run local disk code or call out to a remote server, and the agent can't tell the difference either way.

Steal forany product where an AI agent needs to work identically whether it's running on a laptop or in the cloud

12:30concept

Pre-checked-out Git pool

Rather than cloning a fresh repo every time a new agent session starts, which gets slow as repos grow, the server keeps several branches already checked out and ready, handing one over instantly and refilling the pool in the background.

Steal forany multi-agent or multi-tenant system where session start-up latency matters

13:20concept

Docker-per-session isolation

Every agent's file commands run inside their own Docker container scoped to their working directory, so one agent literally cannot see or touch another agent's files, by design rather than convention.

Steal formulti-agent platforms where you can't trust every agent to stay in its own lane

CTA Breakdown

How they asked for the click.

VERBAL ASK

19:10product

“If you wanna step off that treadmill and learn to build with someone that didn't just pick up cloud code last week... the AI Architects is for you.”

Soft, single pitch placed after the technical content is fully delivered, not before it; frames the paid community as taught by someone with 30+ years of coding experience, backed by a free lower-tier community and a public Twitch stream as trust-builders.

MENTIONED ON CAMERA

19:10productAI Architects (Skool community) ↗

19:20productContent Academy (free community) ↗

19:20channelTwitch livestream (M-Th 10am Pacific) ↗

FROM THE DESCRIPTION

PRIMARY CTAWhere the creator wants you to go next.

OTHER LINKSAlso linked in the description.