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.
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.
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.
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.
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.
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.
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 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.
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”.
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.
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.
This month, we used AI to stress-test three repeatable patterns: we ran a full Java 8 to 21 migration to compare Claude Code with Cursor, scaled agents on a large codebase by right-sizing context with Cline’s Focus Chains, and we put two specialized agents into a review loop. We also did a rapid proof of concept with Cursor in agentic mode.
