👉 Right now, I’m writing The Go Optimization Guide, helping developers build faster, more efficient Go applications.

🗂️ Looking for older content? My previous blog (from 2010 to 2022, in Russian) is still available here.

Today I learned... git shallow

Sometimes you need to understand why something exists, and instead, you’re staring at a mystery. It feels like magic for a moment. But there is no magic in IT. There is always a reason, and usually it’s painfully concrete.

Today I learned that if git blame suddenly claims I wrote the entire million-line project, it might be lying 🙂

I ran into a situation where my local git blame attributed every line to a single recent commit, while GitLab showed the correct historical authors. At first glance, it looked like history had been rewritten, which is odd and incorrect.

Dealing with ThreadSanitizer Fails on Startup

A typical iconic Kyrgyzstan view.

Usually, you need just a few lines to initialize TSan in your project: you compile with the sanitizer flags, run the tests, and get a clear report of which threads touched which memory locations. On a modern Linux system, that simple expectation can fail in a very non-obvious way.

FATAL: ThreadSanitizer: unexpected memory mapping 0x...

In my case, I attached TSan to a not-so-young C++ codebase and immediately encountered a fatal runtime error from the sanitizer, long before any of the project's code executed. No race report, no helpful stack trace, just a hard abort complaining about an "unexpected memory mapping."

If you can upgrade your toolchain to LLVM 18.1 or newer, this problem effectively disappears, because newer TSan builds know how to recover from the incompatible memory layout. Suppose you are pinned to an older LLVM (by CI images, production constraints, or corporate distro policy). In that case, you are in the same situation I was: you have to understand what the sanitizer is trying to do with the address space, and work around the failure mode yourself.

Cross-Compiling Rust for Raspberry Pi

I just started a new embedded pet project on the Raspberry Pi, and I expect it'll be a pretty big one, so I've been thinking about the technology from the beginning. The overall goal is to create a glass-to-glass video pipeline example. Let's see how it's going. For now, I'm using a USB V4L2 camera while waiting for the native Pi modules to arrive, but it's enough to sketch the capture loop and start testing the build pipeline. The application itself is minimal—open /dev/video0, request YUYV at 1280x720, set up MMAP buffers, and iterate over frames—but the real challenge occurs when v4l triggers bindgen, and the build must cross-compile cleanly for aarch64. 

The language choice immediately becomes part of the equation right away. Go is my favorite and, usually, is not considered as an option by many embedded developers. But it's a good choice for small embedded utilities because its cross-compilation story is nearly effortless. Need an ARM binary? One command and you have it!

GOOS=linux GOARCH=arm64 go build

What Actually Drove the Tech Layoffs

A 10-minute ride and you have such a view from Chon Aryk hills.

I truly enjoy reading Blind and Levels. There is so much internal drama, messy details, and unexpected insights that you almost do not need reality shows anymore. And if you ever feel bored, you can always drop a mildly toxic comment into a thread and watch the whole thing ignite. It fits the overall style of Blind a little too well, but that is part of the fun. And considering that mix of casual toxicity and surprisingly rational takes you see there, you would expect people to look at layoffs with a bit more perspective. But when the topic comes up, the conversation usually drifts to the same explanation. People blame AI. People say their jobs vanished because a model wrote some code. And while I understand the frustration, the logic never sits right with me. Nobody complained during the hiring boom of 2020 and 2021, when companies doubled their headcount like it was nothing. That part gets forgotten. Now that the correction is here, many want a simple villain. AI fits the story, but it does not fit the data.

Identifying Video Streams Using RTP Header Extensions

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Birch Grove near Bishkek. The grove is incredibly popular in the autumn.

When a system manages dozens of cameras or edge devices, packets alone don’t tell you much. An IP and port might change, SSRCs can roll over, and NATs tend to shuffle everything just enough to break simple assumptions. Yet every media packet still needs a clear identity — not for transport, but for logic.

There are many ways to attach that identity: control channels, per-session negotiation, external registries. But the most simple one is already part of RTP itself — the header extension defined by RFC 8285.

How it works

RTP was designed to be extensible. After the fixed header and payload, packets can carry short metadata blocks called header extensions. Each extension has a small numeric ID and a URI describing its purpose.

Multilingual benchmarking project => Bazel for advanced engineering

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In her one year, Molly saw many more exciting places than I did until I was about 28. She does pretty well :-D

When working on performance experiments across C++ and Go, you obviously need a multilingual project structure. There were two paths forward: create separate build systems under a shared repository, or consolidate everything under a single, coherent framework. Bazel made that decision easy.

Using Bazel to unify builds isn’t just convenient—it should be the default choice for any serious engineering effort that involves multiple languages. It eliminates the friction of managing isolated tools, brings deterministic builds, and handles dependencies, benchmarking, and cross-language coordination with minimal ceremony.

Here’s why Bazel makes sense for performance-critical, multilingual projects like this one—no fragile tooling, no redundant setups, just clean integration that scales.

Functional Programming style in C++

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Teskey-Torpok Pass is a lovely pass leading you to Song-Kol Lake, Naryn region.

I’ve been on a bit of a Leetcode streak lately, poking at problems from companies I secretly admire. To keep things interesting (and to avoid nodding off in front of the screen), I challenged myself to solve the same task three ways: good old C++, its shiny modern C++20 cousin, and Elixir. It turns out that staring at a problem through a functional programming lens is like putting on X-ray specs—you see the same lines of code, but suddenly, there’s a weird beauty in that filter chain.<

My first pass was as traditional as it gets—a simple C++ class with an array and a loop. No magic here, just pushing timestamps and scanning them one by one. The implementation is pretty naive, but considering the constraints provided by the Design a hit counter challenge, even an unscalable approach is acceptable. So, we will push all new timestamps into the std::queue std::vector and then simply count.

Fixing C++ lacks switches on strings

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Birch Grove is just 40 minutes from Bishkek. Although I was concerned about the strong, foggy weather, it was an amazing opportunity for photography!

C++ is a powerful language, and I genuinely love it, but sometimes, even in modern versions, it lacks some surprisingly simple features. One such missing feature is switch on std::string. You’d think that by now, we could use a switch statement on strings just like we do with integers or enums—after all, Go has it! But no, C++ keeps us on our toes.

Why Doesn’t C++ Support switch on strings? Because "you only pay for what you use," which is the standard C++ mantra. The switch statement in C++ relies on integral types. Under the hood, it works by converting the case values into jump table indices for efficient execution. But std::string (or even std::string_view) is not an integral type—it’s a more complex data structure. That’s why you can’t simply do:

switch (msg->get_value<std::string>()) { // Nope, not possible :-(
    case "topology":
        // Handle network topology
        break;
    case "broadcast":
        // Handle network broadcast
        break;
}
  • in Go, AI
  • 4 min read

AI, Software Engineering, and the Evolution of Code Generation

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From a weekend trip to a nearby gorge. Why is it here? Because I love the mountains of Kyrgyzstan!

Mark Zuckerberg recently made a bold statement: AI will soon take over the work of mid-level engineers (Forbes). While this may sound like another tech CEO hyping AI, my latest experience with OpenAI’s o3-mini-high model suggests he might not be too far off.

Thanks to DeepSeek, OpenAI was compelled to make o3-mini-high available in a regular ChatGPT subscription instead of locking it behind a steep $200 paywall. I would never pay original $200 for a model, but since I already have the regular ChatGPT subscription, it was an obvious choice to try it out. With this in mind, I decided to experiment: Could o3-mini-high generate a functional Go codebase for my GFSM library?

The experiment

For context, GFSM is my Go Finite State Machine library, and I needed a new generator to extract and save state machines in formats like PlantUML and Mermaid. Writing such a generator requires a solid understanding of Go’s Abstract Syntax Tree (AST) package, something I hadn’t used in years.

How to compile C++ in 2025. Bazel or CMake?

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Today, we’re examining two modern build systems for C++: CMake, the industry favorite, and Bazel, a powerful alternative. While CMake is often the default choice, I believe that approach warrants a bit more scrutiny—after all, we’re focusing on modern tools here (yep, not counting Make, right?). To explore this, I’ve created a practical demo project showcasing how both systems manage a real-world scenario.

Using the maelstrom-challenges project as a starting point, I’ve extracted a C++ library called maelstrom-node. This library has been set up to work seamlessly with both Bazel and CMake, giving us a hands-on comparison of their approaches, strengths, and quirks.

The Project Structure

Here’s what the final directory layout for maelstrom-node looks like: