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Artificial Intelligence|Published: Nov 12, 2025

Understanding How Claude Code Works: A Deep Dive into LLM-Powered Developer Tools

If you're a developer exploring AI coding assistants, you might have encountered Claude Code and wondered how it actually works under the hood. What's the relationship between the command-line tool you install and the large language model that powers it? How does the AI decide when to read your files or run commands? And how do those CLAUDE.md instruction files actually get interpreted? This article will walk you through these questions by clarifying a fundamental distinction that often gets overlooked when people first encounter Claude Code.

Artificial Intelligence|Published: Oct 29, 2025

GitHub All-Stars #8: toon - Cutting LLM costs in the protocol layer

In our “GitHub All-Stars” series, we take “new or little-known” open-source gems that solve real engineering problems and put them under the microscope. Today, we’re looking at toon —a tool that directly tackles the financial and performance overhead of data serialization in the age of AI.

Scala|Published: Oct 28, 2025

Bringing Scala to Server-Side Wasm with WASI & Component Model

WebAssembly (Wasm) is a binary instruction format and low-level language that runs in various environments, including browsers. While it was originally designed for browser applications, its adoption is expanding beyond browsers into cloud and edge computing environments, due to Wasm's features. This article explains the advantages of compiling Scala to Wasm and future prospects.

Scala|Published: Oct 27, 2025

Rethinking Gatling: A Scala CLI and Containerisation Approach to Performance Testing

Gatling proved to be an excellent tool for performance testing - especially in monolithic environments. It integrated well into traditional CI pipelines, and its DSL made writing tests both expressive and maintainable. But as our architecture evolved toward microservices and cloud-native deployments, performance testing became significantly more complex. In this article, we will look at an alternative approach to Gatling, one that overcomes its out-of-the-box limitations.

Artificial Intelligence|Published: Oct 23, 2025

Running a Pragmatic AI Hackathon

To see how code assistants work in reality, our engineers ran a focused, half-day AI hackathon inside an active commercial project - a large-scale logistic platform built in Scala and deployed on Kubernetes. Their goal was to see how AI can be used responsibly in a real, mature system and explore its applications and possibilities directly in our project domain, making sure the outcomes were practical, safe, and genuinely valuable. Read on to learn about their results.

Artificial Intelligence|Published: Oct 15, 2025

GitHub All-Stars #7: Paper2Agent - The alchemy that transforms research papers into a working code

This week, we’re diving into a project that tackles one of the most fundamental problems in the world of science and engineering: the frustrating gap between the theory described in a research paper and its practical implementation. Anyone who’s ever tried to reproduce results from a paper knows the pain. The code, if it’s even available, is often a tangled mess of one-off scripts and Jupyter notebooks - making reproducibility, the cornerstone of science, more of an art than a craft.

Artificial Intelligence|Published: Oct 13, 2025

What top engineers read about AI in September 2025

This newsletter is a monthly, noise-free roundup of AI developments that truly matter to engineers and tech leaders — practical, skeptical, and ready for implementation. Instead of chasing every new model or “top 50 tools” list, we focus on what will stand the test of time and genuinely change how we build software. This month, we’re diving into agents — a concept that’s finally moving beyond experimentation and maturing into a true engineering discipline.

Artificial Intelligence|Published: Oct 10, 2025

This Month We AIed #3

We’ve been putting AI to the test. Not in theory, but in controlled experiments. In this edition, you will learn when to use AI to get things done (not to plan them); how to guide it with the right structure, and where the “AI as a teammate” metaphor breaks down today.

Data Engineering|Published: Oct 7, 2025

What is data orchestration?

Data orchestration can be defined either as an automation or as a data management process. In this article, we will look closely at both of these definitions, how they are different from each other. We will also talk about some of the most popular tools used by engineers worldwide.

Artificial Intelligence|Published: Oct 1, 2025

GitHub All-Stars #6: Dayflow - Your Day’s Private “Git Log”

There is a fundamental gap in understanding our own productivity, for those who feel an inner need to close it, creates an opportunity for Dayflow - a project that aims to redefine how we perceive and analyze our screen time - for the good or the bad. Dayflow is not yet another time tracker - it’s an ambitious attempt to build a “semantic timeline,” or, to use the project’s own metaphor, “a git log for your day”.

Artificial Intelligence|Published: Sep 24, 2025

GitHub All-Stars #5: Spec-Kit – How GitHub tames AI-coding chaos with Spec-Driven Development

In this article, we’ll break spec-kit project down to its core components. We’ll explore its philosophy, architecture, and the powerful engineering patterns behind it to understand how GitHub is transforming chaotic “vibe coding” into a structured software development process.

Backend Engineering|Published: Sep 24, 2025

Gradle vs. Bazel for Android projects: A deep dive

Discover how Gradle and Bazel compare for Android builds, from IDE support to scalability. Learn which tool suits your team’s size and project goals.

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