Hey everyone, welcome back to My Weird Prompts. We are coming to you from a very sunny afternoon here in Jerusalem. It is February eighteenth, twenty twenty-six, and the light hitting the stone walls outside the studio is just incredible today. I am Corn, and I am joined as always by my brother, the man who probably has more browser tabs open than there are stars in the visible sky.
Herman Poppleberry, here and ready to dive into the deep end of the stack. And for the record, Corn, I have actually moved most of my research into a local vector database now, so my tab count is down to a very manageable forty-five.
Only forty-five? You seem especially caffeinated today, Herman. What have you been reading that has you this energized?
Oh, you know, just some light reading on kernel level networking, asynchronous input output patterns, and the latest benchmarks for io_uring on the six point twelve Linux kernel. The usual stuff that keeps a Poppleberry up at night.
That is actually perfect timing, because today's prompt from Daniel is right in that neighborhood. Daniel sent us a message about his automation pipelines. He has been using a platform called Modal to trigger AI agents for his voice-to-text and analysis workflow. He noticed something that felt like a glitch in the matrix. He saw that while the AI part—the actual inference—is obviously super heavy on computation and costs him money, the initial part, the webhook that is just sitting there waiting for a trigger, seems to cost almost nothing. It is always on, always listening, but it is not burning through his credits or resources.
It is a classic architectural question that gets to the heart of how we build the modern web. How do you maintain a persistent presence on the internet without paying a persistent price in terms of electricity and silicon? It feels like it should be expensive to be "always on," right?
Exactly. Daniel was asking how this is achieved at a technical level and what the actual resource requirements are for that first part of an automation instance. Like, if you were to stand this up yourself on a private server, what are you actually "spending" to have that webhook just exist? Is it a tiny flame that never goes out, or is it something else entirely?
I love this because it touches on the fundamental way modern operating systems and networks actually talk to each other. To the average user, it feels like magic. You have a URL, and at any moment, day or night, you can send a packet to it and something happens instantly. It feels like there must be a little engine idling there, burning fuel just in case a request comes by.
Right, that is the intuition. If I want a light to turn on the second I flip a switch, there has to be some tension in the system. But we know that running a simple web server or a listener isn't like keeping a car engine running at three thousand RPMs. So, Herman, let's start with the basics. When we say a webhook is "listening," what is actually happening inside the computer?
Okay, so to understand the "listening" part, we have to look at the concept of a socket. In the world of Unix and Linux, which is where almost all of this infrastructure lives, there is a famous saying that "everything is a file." A network connection is essentially treated like a file that you can read from or write to.
So, when a program wants to hear from the outside world, it tells the operating system, "Hey, I am interested in any data that comes in on port eighty or port four hundred forty-three."
Precisely. The application makes a system call. Usually, it is literally called "listen." But here is the trick that answers Daniel's question: the application itself doesn't just sit there in a loop saying "is there data yet? is there data yet? is there data yet?" That would be polling, and that would indeed consume a lot of CPU cycles because the processor is constantly checking the status of that "file."
That is the "are we there yet" approach to networking. We have all been on that road trip, and it is exhausting for everyone involved.
Right, and it is terribly inefficient for a computer. Instead, what happens is the application says to the operating system kernel, "I am going to sleep. Wake me up only when a packet arrives for this specific address and port."
So the "listening" is actually the state of being asleep? That sounds like my kind of job.
In a very real sense, yes. The process enters what we call a "blocked" state. It is removed from the CPU’s execution queue. It is not taking up any clock cycles. It is just sitting in memory, taking up a tiny bit of space in the process table, waiting for an interrupt. The kernel is the one doing the heavy lifting of keeping track of who is waiting for what.
Okay, so let's talk about that "tiny bit of space." Daniel was asking about the actual resource requirements. If I have a process that is blocked and waiting for a webhook, what is the memory footprint we are talking about in twenty twenty-six?
It depends on the language and the framework, but at the kernel level, a socket structure is remarkably small. We are talking about maybe a few kilobytes of memory to store the state of that connection—the IP addresses involved, the port numbers, and the buffers. If you are using a very lightweight language like C, Rust, or Go, you could have a listener sitting there using maybe five to ten megabytes of total system RAM, including the entire overhead of the runtime.
Ten megabytes. That is practically nothing in the context of a modern server that might have sixty-four gigabytes or even a hundred twenty-eight gigabytes of RAM.
It is a rounding error. You could literally have thousands, or even tens of thousands, of these listeners on a single modest machine without breaking a sweat. The real cost isn't the "listening"; it is the "responding." But wait, I should clarify something. Even that ten megabytes is mostly the application's own overhead. The actual kernel-level cost of "watching" that port is even smaller.
But wait, if the process is asleep, how does the computer know when the packet arrives? Something has to be awake to see the data coming off the wire. Is the motherboard just constantly scanning?
That is the beauty of the hardware-software interface. This is where the Network Interface Card, or the NIC, comes in. Modern NICs are incredibly smart. When a packet hits your server, the network card sees it. It looks at the header and says, "Oh, this is for port four hundred forty-three." The hardware then triggers what is called a hardware interrupt.
An interrupt. That sounds very dramatic, like someone bursting into a room and shouting "Stop the presses!"
It is exactly like that! It basically tells the CPU, "Stop whatever you are doing for a microsecond. We have data that needs to be moved from the network card into main memory." The CPU then hands control over to the kernel's interrupt handler. The kernel looks at its table of listeners, finds the process that was "sleeping" and waiting for that port, and moves it from the "blocked" queue back to the "run" queue.
So the "listening" is actually being handled by the operating system kernel, which is already running anyway to manage the clock, the disk, and everything else. We aren't adding a new "worker" that is constantly checking; we are just adding an entry to a list that the kernel checks whenever a packet arrives.
Exactly. It is like a concierge at a hotel. The concierge is already at the desk. You don't need to hire a new person to wait for your specific friend to arrive. You just leave a note with the concierge saying, "When my friend shows up, buzz my room." You can go to sleep in your room, consuming no energy, and the concierge, who is already there helping everyone else, handles the notification.
That is a great analogy. But then, let's look at what Daniel mentioned about platforms like Modal or other serverless environments. He is sending a prompt, it hits a webhook, and then a whole pipeline starts. In his case, he is seeing that it is "always on" but he is not being billed for it until it actually does something. How do these platforms manage that at scale? Because they aren't just running one server for Daniel; they are running thousands for thousands of users.
This is where we move from simple sockets to something called "load balancing" and "ingress controllers." In a massive cloud environment, you don't actually have a separate process running for every single user's webhook. That would be a waste of resources, even if it is just ten megabytes per user. Instead, you have a very high-performance "front door."
The "front door" being a massive, shared listener?
Right. Think of something like Nginx, HAProxy, or a specialized ingress controller written in Rust. These tools are built using something called "event loops." Instead of the "one thread per connection" model, which was common twenty years ago and very memory-intensive, they use system calls like "epoll" on Linux or "kqueue" on BSD.
I've heard you mention "epoll" before. That is the secret sauce for high-concurrency, isn't it?
It really is. In the old days, if you wanted to check a thousand connections, you had to ask the kernel about each one individually using a call called "select." It was slow. With "epoll," the application says to the kernel, "Here is a list of ten thousand connections I am interested in. Just give me the list of the ones that actually have new data since the last time I asked."
So the application just gets a list of "active" events. It doesn't have to sift through the silent ones.
Exactly. This allows a single process to handle tens of thousands of simultaneous webhooks with very little CPU usage. So, in a platform like Modal, they likely have a massive fleet of these ingress controllers. When a request hits "daniel-dot-modal-dot-run-slash-webhook," the ingress controller sees it, identifies it as belonging to Daniel's account, and then—this is the clever part—it triggers the "spin up" of his specific code.
So the "always on" part isn't actually his code. It is the platform's routing layer.
Right. His code is "cold." It is sitting on a disk somewhere as a container image or a specialized microVM. The routing layer is the only thing that is "hot." And because that routing layer is shared across thousands of users, the cost per user is effectively zero. They only charge him when they have to "provision" the actual compute to run his AI logic.
That makes a lot of sense. But Daniel also mentioned the idea of a "persistent automation instance" that he owns. If he were to move away from a serverless platform and just run his own Linux box, say a small virtual private server, he would be paying for that server twenty-four seven anyway. But he is curious about the actual resource draw. If he has a Python script running a basic Flask or FastAPI server just to catch that webhook, is that going to "wear out" his CPU or use significant RAM?
Not at all. If you run a basic FastAPI app in twenty twenty-six, it might use sixty to eighty megabytes of RAM while idling. And the CPU usage will be essentially zero percent. I mean, it might show up as zero-point-one percent in your task manager just because of the background overhead of the operating system and the Python interpreter doing its own housekeeping.
So, for someone like Daniel, the "cost" of the webhook is really just the cost of the smallest virtual machine you can buy. Which these days is what, five dollars a month?
Or even less. You can get "nano" instances for three dollars a month, or even use "free tier" offerings from the major clouds that give you a small ARM-based instance for nothing. But here is where it gets interesting: the "resource requirement" isn't just about the idle time. It is about the "burst." When that webhook hits, your server has to go from zero to sixty instantly.
Right, it's the "wake up" cost.
Exactly. If you're running on your own server, the process is already in RAM, so the wake-up is instantaneous. We call this a "hot" listener. If you're using a serverless platform, you might hit a "cold start" where the platform has to find a machine, pull your container, and start it up. In twenty twenty-six, platforms like Modal have gotten this down to sub-one-hundred milliseconds using things like Firecracker microVMs and memory snapshotting.
Memory snapshotting? That sounds like you're just taking a picture of the computer's brain and reloading it.
That is exactly what it is! They use a technology called CRIU—Checkpoint/Restore in Userspace. They start your app once, take a snapshot of its entire memory state, and save it to disk. When a webhook comes in, they just "resume" that memory state. It is much faster than starting a fresh Python process.
That is wild. So the "always on" feeling is actually just an "extremely fast resume" feeling.
Precisely. Now, for Daniel's question about the "actual resource requirements," let's get granular. If he ran this himself on a Raspberry Pi or a small VPS, he is looking at maybe two to three watts of power for the whole device. The actual "listening" part of the software is using a fraction of a milliwatt. It is the most efficient part of the entire stack.
I want to go back to the technical mechanism for a second. You mentioned "epoll" and "kqueue." For our more technically minded listeners, why are these so much more efficient than the old way? What was the "old way" exactly?
The old way was a system call called "select." With "select," you would give the kernel a list of file descriptors—those "files" that represent your connections. The kernel would then have to walk through that entire list, one by one, to see if any of them had data. It was an O of N operation, meaning if you had ten thousand connections, it took ten thousand times longer than one connection.
That sounds like it would get very slow as you added more listeners.
It was a massive bottleneck. It was called the C-ten-K problem—how to handle ten thousand concurrent connections. "epoll" changed the game because it made the kernel responsible for keeping track of the "ready" list. The application doesn't have to provide the list every time; it just registers the interest once, and then the kernel pushes events to a special queue. It turned an O of N problem into an O of one problem.
It’s the difference between a teacher calling out every student's name to see if they have a question, versus the students just raising their hands when they have one. The teacher only has to look at the hands.
Exactly! And in twenty twenty-six, we have moved even beyond that with something called io_uring. This is a relatively new interface in Linux that allows the application and the kernel to share a "ring buffer." The application puts a request in the buffer, and the kernel picks it up without even needing a full system call, which saves even more CPU cycles. It is the absolute peak of efficiency.
So, thinking about Daniel’s setup, he’s using Modal, which is a serverless GPU platform. He mentioned it’s more affordable than using APIs like OpenAI directly for everything. That’s an interesting point. He’s essentially running his own "worker" nodes that only exist when they are needed. But the "listener" is the entry point.
Right. And what’s cool about Modal specifically is how they’ve optimized that "spin up" time. They use a lot of clever tricks with shared filesystems so that your code doesn't even have to be "downloaded" to the worker; it is just mounted instantly. But at the end of the day, the "listening" part is still just a very efficient routing layer waiting for an HTTP POST request.
Let's talk about the "HTTP" part of this. A webhook is usually just a POST request, right? Why POST?
Almost always. It’s a standard web request where the "payload" is the data Daniel wants to process—in this case, his voice prompt or the metadata about it. POST is used because it allows for a large body of data to be sent, unlike a GET request which is usually just for fetching information.
So, if I'm building my own webhook listener, I'm basically just building a tiny web server that only has one route.
That’s it. You don't need a database, you don't need a complex frontend. You just need a tiny bit of code that says, "When you get a POST request on this URL, take the body of that request and do X with it." In Python, using FastAPI, that is literally five lines of code.
And "X" could be "start a heavy AI process."
Right. And this is where the "resource management" gets tricky. If you are running your own server, you have to make sure that when "X" starts, it doesn't kill the "listener." If your AI model suddenly grabs all the RAM and all the CPU, your web server might stop responding to new webhooks. This is the "noisy neighbor" problem, but inside your own server.
Oh, that's a good point! The "listener" is cheap when it's idle, but if it's the same process that then does the heavy lifting, you could accidentally lock yourself out. You'd be so busy thinking that you'd forget to listen.
Exactly. This is why in professional setups, we usually decouple them. You have a "producer" and a "consumer." The webhook listener is the producer. It receives the data and puts it into a "queue"—something like Redis, RabbitMQ, or even a simple disk-based queue. Then, a separate "worker" process watches that queue and does the heavy AI work.
So the webhook listener stays "light" and "responsive" no matter how busy the workers are. It just says "Got it!" and goes back to sleep.
Right. The listener's only job is to say, "I got it, it's in the queue, here's a two hundred OK status code. Bye!" It takes milliseconds. This keeps the "listening" part of the pipeline incredibly stable and low-resource. If Daniel is doing this on Modal, they handle all of that queueing and scaling for him behind the scenes.
This actually leads into a misconception I think a lot of people have. They think that "being online" is expensive. But "being online" is just having an IP address and a port open. It's the "processing" that's expensive.
It’s the difference between having a phone in your pocket and actually being on a call. Having the phone in your pocket, waiting for a ring, uses a tiny bit of battery to stay connected to the tower. But the second you start talking, the screen turns on, the radio starts transmitting at full power, the processor is encoding your voice—that's when the battery starts to drain.
That's a perfect analogy. And just like a phone, modern servers are designed to be "always on" in that low-power state.
Totally. In fact, modern CPUs have different "C-states." When the CPU is idle—like when our webhook listener is blocked and waiting—the CPU can actually power down parts of itself. It can lower its voltage and its frequency to save energy. It’s "on," but it’s in a deep sleep. In twenty twenty-six, server-grade CPUs are incredibly good at "parking" cores that aren't being used.
So even at the hardware level, the "listening" is optimized to be as close to "off" as possible while still being able to wake up in microseconds.
Exactly. We’ve had decades of engineering dedicated to making this "idle-but-ready" state incredibly efficient. It is what allows your phone to last all day even though it is technically "listening" for notifications from a dozen different apps.
So, if Daniel wanted to get really granular—let’s say he wanted to calculate the cost of just the "listener" part of his instance for a month. We’re talking about what, pennies?
If you look at the actual electricity used by a single idle process on a shared server? It’s probably a fraction of a cent per month. The only reason we pay five dollars a month for a VPS is for the "guaranteed" slice of resources, the IP address, and the physical space in the data center. The "work" of listening is virtually free.
That’s fascinating. It really changes how you think about "persistent" infrastructure. It’s not a heavy thing you’re dragging along; it’s more like a very thin thread you’ve left hanging in the air.
I like that. A "thin thread." And as long as nobody pulls on it, it doesn't weigh anything. But the moment someone pulls on it—the moment that webhook is hit—it can trigger a whole cascade of events.
Okay, let's talk about some of the "downstream" implications of this. If webhooks are so cheap and efficient, why don't we use them for everything? Like, why do we still have "polling" at all? I still see apps that seem to refresh every thirty seconds.
Well, sometimes you don't have control over the "sender." If you're waiting for a website to change, and that website doesn't offer a webhook, you have no choice but to poll. You have to keep checking, "Did it change? Did it change?" It is a "pull" versus "push" problem.
Right, because you can't force them to "buzz your room" if they don't have a concierge.
Exactly. Webhooks require a "push" architecture. Both sides have to agree on the protocol. And there are some security implications, too. When you open a webhook, you are essentially opening a door to your server. You have to make sure that only the "right" people can walk through it.
That’s a great point. Let’s talk about that. If I have this "lightweight" listener, how do I make sure it’s not being abused? Because if it’s "always on," that means it’s always a target for scanners and bots. Does the "security" part add a lot of resource overhead?
This is a real concern. Even if your listener is "cheap" to run, if a botnet starts hitting it with a million requests a second, it’s going to get very expensive very fast, either in terms of bandwidth or CPU usage trying to reject those requests. This is where "Edge" security comes in.
Edge security? Like living on the edge?
Sort of! It means moving the security check as close to the user as possible. Platforms like Cloudflare or Fastly sit in front of your server. They have massive capacity to absorb attacks. They check the requests, and if they look like garbage, they drop them before they ever reach your "thin thread."
And if I'm doing it myself? What is the "low resource" way to secure a webhook?
The most common way is using "HMAC signatures." The sender—say, Daniel’s voice form—takes the data, mixes it with a "secret key" that only he and the server know, and creates a unique signature. The listener’s first job, before it does anything else, is to run that same math. If the signatures match, the request is real.
And is that "checking" expensive?
It’s a very fast mathematical operation. It takes a few microseconds. If the signature doesn't match, the listener just drops the connection immediately. It doesn't even "wake up" the rest of the application or start the AI pipeline.
So you can maintain your "low resource" profile even under attack, as long as you can quickly identify the bad actors.
Right. And in twenty twenty-six, we also use things like eBPF—Extended Berkeley Packet Filter. This is a way to run tiny, sandboxed programs inside the Linux kernel itself. You can actually write a security rule that drops unauthorized webhook requests before they even reach the "socket" layer. It is the ultimate in efficiency.
It’s like having a security guard at the front gate of the hotel who checks IDs before they even get to the concierge.
Precisely. It’s all about layers. The deeper the request gets into your system, the more expensive it is to handle.
So, to summarize the technical side for Daniel: the "always on" nature of webhooks is possible because of the way modern operating systems handle network sockets. By using "blocked" processes and kernel-level event notifications like "epoll" and "io_uring," we can have thousands of listeners that consume virtually no CPU and very little RAM. The actual cost is just the "state" of the connection in memory, which is tiny.
And the "magic" of it being free on serverless platforms is just a result of shared infrastructure. You aren't paying for your own "waiter"; you're sharing a very efficient "front door" with everyone else. The platform only charges you when they have to do the "real work" of running your code.
That is such a clear way to look at it. Now, what about the practical side? If Daniel, or any of our listeners, wants to set up their own persistent automation instance in twenty twenty-six, what are some "pro tips" for keeping it efficient?
Number one: use a lightweight language. If you're just catching webhooks, you don't need a heavy Java or Ruby environment. Go and Rust are the kings of this right now because they compile to small binaries and have incredible concurrency models. But even Python with FastAPI is fine for most people.
And avoid the "one thread per connection" model?
Absolutely. That is the old school way. Make sure your server is using an "asynchronous" or "event-driven" architecture. In Python, that means using async and await. That’s what allows the process to "sleep" efficiently while waiting for I/O.
What about "keep-alives"? I’ve heard that term in networking. Does that affect the resource usage of a webhook?
"Keep-alives" are more for when you have a connection that stays open, like a WebSocket or a database connection. For a standard webhook, it’s usually a "one-and-done" request. The connection opens, the data is sent, and the connection closes. This is actually more efficient for the "listener" because it doesn't have to maintain any long-term state.
So it’s even "lighter" than a persistent connection.
Much lighter. It’s like a letter being dropped in a mailbox versus a phone call where you both stay on the line. Once the letter is in the box, the mailman is gone, and you can check the box whenever you want.
Okay, let's talk about the "what if" scenario. What if Daniel wanted to run a million webhooks? Like, he becomes the next big automation platform. What changes then? Does the "thin thread" start to break?
Then you start hitting the limits of the operating system's "file descriptor" table. Every socket is a file, and Linux has a limit on how many files a single process can have open. You have to start tuning the kernel parameters—things like "ulimit"—to allow for millions of connections. This is the C-ten-M problem—ten million concurrent connections.
So the bottleneck isn't the CPU or the RAM, it's literally just the "index" of files the computer can keep track of?
At that scale, yes. You also have to worry about the "epoll" set size and the amount of kernel memory used for the TCP stack. But we are talking about extreme scale here. For an individual or a small business, you will never hit those limits. Your five-dollar VPS could probably handle every webhook Daniel will ever need in his entire life.
That’s a very empowering thought. The technology is so efficient that it’s almost "free" at the human scale. We often worry about the "cost" of tech, but in this one specific area, we've really won.
It really is. It’s one of the few areas in computing where we’ve actually reached a sort of "peak efficiency." We’ve gotten so good at waiting for data that the "waiting" part is basically solved. The only thing left to optimize is the "doing" part—which is why things like Modal and specialized AI hardware are so popular right now.
It’s funny, we spend so much time talking about how to make AI faster or how to process big data, but the "waiting for data" part is this quiet, unsung hero of the internet. It's the silent foundation.
Without it, the "real-time" web simply wouldn't exist. We’d all be stuck in the "polling" dark ages, and our batteries would all be dead by noon. Imagine if your phone had to manually check every thirty seconds if you had a new WhatsApp message. It would be a disaster.
I think that's a great place to wrap up the technical deep dive. Daniel, I hope that sheds some light on why your automation pipeline feels so "magic" and why it's so cheap to keep that first part of the instance running. It’s all about the kernel doing the heavy lifting while your code gets to take a nap.
And if you ever do find yourself paying more than a few cents for a "listener," you might want to check if you've accidentally left a "while true" loop running somewhere! That is the fastest way to turn a "thin thread" into a "burning fuse."
Good advice, Herman. So, before we sign off, I want to give a quick shout-out to everyone who’s been listening. We’ve been doing this for over six hundred episodes now, and it’s still just as fun as it was on day one.
Maybe even more fun now that the tech is getting so weird. AI agents, serverless GPUs, persistent automation—it’s a great time to be a nerd. I mean, we are literally talking about kernel interrupts on a sunny afternoon in Jerusalem. It doesn't get better than this.
It really doesn't. And hey, if you are enjoying the show, we’d really appreciate a quick review on your podcast app. It genuinely helps other people find us, and we love reading your feedback. It’s like a little webhook of positivity hitting our server.
Yeah, it makes a huge difference. We're on Spotify, Apple Podcasts, and pretty much everywhere you get your audio fix. We even have a decentralized feed now for the Web Three crowd.
You can also find us at myweirdprompts-dot-com. We’ve got the full archive there, plus a contact form if you want to send us a prompt like Daniel did. Or you can just email us directly at show-at-myweirdprompts-dot-com. We read every single one.
We’re always looking for new rabbit holes to dive into. The weirder, the better.
Alright, that’s it for today. Thanks for joining us on this little journey into the world of webhooks, kernel interrupts, and the efficiency of doing nothing.
This has been My Weird Prompts. Until next time, keep your sockets open and your interrupts ready.
Bye everyone!
Goodbye!
