How I Wrote My First Linux Driver in Rust: Experience from the 'Operating Systems and System Programming' Course on asibiont.com

Introduction: Why System Programming Is Not Scary

Until recently, I perceived operating systems as a black box. I could write web applications in Go and Python, but as soon as I faced low‑level tasks — for example, figuring out how the process scheduler works or writing a simple kernel module — I felt like a complete beginner. The Linux kernel documentation seemed like a labyrinth, and C code with macros and manual memory management left me bewildered.

Everything changed when I decided to take the "Operating Systems and System Programming" course on the asibiont.com platform. This is not a typical online course with pre‑recorded lectures — here, learning is built around AI‑generated personalized lessons that adapt to your level. In this article, I'll share what I learned, how the course is structured, and why this format helped me write a working character device driver in Rust in just a few weeks.

About the Course: Not Just Theory, but Immersion into the Kernel

The course covers all key topics that an engineer working with Linux should know: OS architecture, process and thread management, virtual memory, file systems, inter‑process communication (IPC), and network programming. But the main focus is practice. Each section is reinforced with assignments in C and Rust — from writing your own shell to developing a driver and performance optimization.

Who is this course for?

• Beginner system programmers who already know C or assembly but want to understand the inner workings of Linux.
• Embedded developers who need to write code for bare metal.
• Security engineers wanting to understand kernel vulnerabilities.
• Anyone who wants to stop being afraid of /dev/ and ioctl.

I came with basic knowledge of Rust and nearly zero experience with the kernel — the course turned out to be the perfect entry point.

How Learning Works on asibiont.com: AI Makes Lessons for You

The platform's main feature is AI‑generated lessons. When you start the course, the neural network assesses your level (via a short test or analysis of previous answers) and creates a program that doesn't repeat what you already know and doesn't jump too far ahead. All material is presented in text format — no videos. This may seem unusual, but in practice it gives a huge advantage: you read at your own pace, return to difficult parts, and the AI rephrases the explanation if something is unclear.

For example, when I first encountered the concept of wait_queue in Linux, standard sources explained it through macros and the kernel's internals. The AI lesson on asibiont.com broke the topic down into small steps: first an analogy with a queue in a coffee shop, then pseudocode, then a real example from a driver. I literally "felt" the topic before writing code.

And most importantly: 24/7 access. You are not tied to a webinar schedule. If you want to study at two in the morning — go ahead. The AI will always generate the next piece of material based on your progress.

My Journey: From Hello World to a Character Device Driver

The course follows a "from simple to complex" principle. I started with modules: wrote a minimal kernel module that prints Hello, world to the system log. Then I figured out file_operations — the structure that connects the driver to the open, read, write, ioctl calls.

When I got to Rust, I was initially skeptical: after all, the Linux kernel is traditionally written in C. But the course uses Rust for Linux — a project that lets you write kernel modules in Rust safely, without the risk of memory leaks or data races. The AI explained in detail how to set up the environment, how to use the kernel crate, and which linters check the code.

As a result, I built a simple character device driver that:
- registers a device with alloc_chrdev_region;
- implements open, release, read, and write via the FileOperations trait;
- transfers data between user space and kernel space through a 4096‑byte buffer.

Here's a code snippet in Rust (simplified for understanding):

// from the driver module
struct MyDevice {
    data: Mutex<Vec<u8>>,
}

impl FileOperations for MyDevice {
    fn write(
        &self,
        _file: &kernel::file::File,
        buf: &[u8],
        _offset: u64,
    ) -> Result<usize> {
        let mut data = self.data.lock();
        data.clear();
        data.extend_from_slice(buf);
        Ok(buf.len())
    }

    fn read(
        &self,
        _file: &kernel::file::File,
        buf: &mut [u8],
        _offset: u64,
    ) -> Result<usize> {
        let data = self.data.lock();
        let len = buf.len().min(data.len());
        buf[..len].copy_from_slice(&data[..len]);
        Ok(len)
    }
}

When I first compiled the module and loaded it into a virtual machine, then wrote a string to /dev/mydevice and read it back — I was truly thrilled. This was not a toy exercise, but a real interface working at the kernel level. Thanks to Rust, I wasn't afraid of making a memory error — the compiler caught most issues at build time.

What I Learned New (Beyond Code)

The course isn't only about drivers. Here are a few topics that were eye‑openers for me:

• Virtual memory and page tables. I finally understood how mmap allows mapping files into a process's address space. The AI gave an example with video buffering in a player — it immediately became clear why this is efficient.
• Completely Fair Scheduler (CFS). We analyzed how the kernel achieves fair distribution of CPU time and wrote a small program to observe context switching via perf.
• IPC: pipes, shared memory, semaphores. The course had an assignment: implement a simple chat between processes using POSIX message queues. After that, I stopped fearing multithreading in system programming.
• Networking in the kernel. I wrote a simple module that intercepts packets with netfilter and logs them. A daunting but fascinating experience.

Why AI Learning Works for Complex Topics

Traditional courses often suffer from "fluff" — they just rehash standard documentation. Asibiont.com uses a neural network that adapts the explanation to the specific student. For example, if you're strong in C but weak in assembly, the AI will spend less time on C syntax and more on linking and calling ABI.

Personally, I appreciated the "explain simpler" feature: when a lesson introduced an unfamiliar term (e.g., TLB flush), I could click a button and the AI would rewrite the paragraph using an analogy from the world of transportation. It sounds trivial, but it really helps to absorb abstract concepts.

Who I Recommend This Course To

The course is worth taking if:
1. You want to understand Linux from the inside — not at the level of "how to run a command", but at the level of "how does the open system call work".
2. You develop embedded devices or write code for microkernels.
3. You are preparing for an interview at FAANG or companies that value system programming (Linux Foundation, Red Hat, Canonical).
4. You are interested in Rust in the kernel — this is one of the hottest trends in 2026.

The only caveat: the course is not for complete programming beginners. Basic experience in C or Rust is mandatory. If you're just starting to learn, first master the syntax, then dive into OS.

Conclusion

"Operating Systems and System Programming" on asibiont.com is not just another set of lectures — it's a living, adaptive journey into the world of low‑level development. Thanks to AI‑generated lessons, I was able to master in two months what would have taken me a year to study on my own. And yes, I finally understood how drivers work and even wrote my own in Rust.

If you too want to stop being afraid of /dev/ and feel like a real kernel developer — try the course. You can start right now: Operating Systems and System Programming.