VirtusLab's Articles

Scala|Published: Apr 14, 2026

Safe Scala: an introduction

AI agents are powerful: they can execute many day-to-day tasks thanks to their understanding of the surrounding context. That's what sets them apart from ordinary automations. However, they can also go wrong in various ways. That's why giving an agent free access to your computer might not be the best idea. For many reasons: starting with agent incompetence, where a "photo cleanup" request ends up with all of your photos permanently removed, instead of neatly organized into folders. There are several options for restricting the actions an AI agent can perform. Safe Scala provides tools to achieve just that. Let's take a short overview of how Safe Scala works and what the alternatives are.

Artificial Intelligence|Published: Mar 24, 2026

Interview with Artur Skowroński, Head of Application Development and the lead of the VISDOM project

What if the biggest blocker to AI-driven development isn’t the AI itself but everything around it? Artur Skowroński, our Head of Application Development, talks about the most common issues enterprises have stumbled upon over the last few years, and how VirtusLab is working to remove them. This is a sneak peek into VISDOM: a platform designed for a world where AI produces massive amounts of code, and organizations need a way to truly understand, trust, and manage it. Read on to discover how it all comes together.

Scala|Published: Mar 19, 2026

Generating Direct-Style Scala 3 Applications

While being the best language out there, Scala isn't (yet?) the most ubiquitous language for developing business applications. But we're on a mission to change that! Starting with what everybody's doing right now, of course: generating applications using Claude Code or other LLMs. AIs have a strong understanding of the most popular application stacks, such as TypeScript, Java, and Python. But how does an AI agent fare when tasked with writing a Scala 3 application?

Artificial Intelligence|Published: Mar 10, 2026

Agents for Legacy Code migration

The traditional large, legacy system migration can cost multiple times the yearly company revenue and still yield limited versions of the original system functionality. However, with the help of AI, it can change. The migration process might become faster, cheaper, and smoother.

Artificial Intelligence|Published: Mar 6, 2026

How We Got FP16 GPU Tests Running on GitHub Actions - Without a GPU

This post is about the practical challenges of getting GPU-targeting code tested in environments without GPU hardware - specifically GitHub Actions. The project behind it uses Project Babylon / HAT (an experimental OpenJDK fork that compiles Java to GPU kernels), but the lessons apply to anyone doing OpenCL, CUDA, or heterogeneous compute work in CI.

Artificial Intelligence|Published: Feb 11, 2026

GitHub All-Stars #13: Matchlock - Your Agent's Bulletproof Cage (With Room Service)

Today's project dropped on Hacker News frontpage just days ago and instantly sparked one of the most interesting security discussions I've seen in a while. We're looking at Matchlock by Jingkai He - a CLI tool for running AI agents in ephemeral microVMs with network allowlisting and secret injection via MITM proxy. Built to answer a question that every developer running claude --dangerously-skip-permissions should be asking: "What's the worst that could happen?"

Artificial Intelligence|Published: Jan 29, 2026

Do you need an AI Agent?

Agents are LLMs that move in the partially observed environment, interact with it, reason, plan, act, and adapt to the changing environment. They need to use tools to gather information in the iterative cycles. The information required to complete the task is hidden and requires online querying or tool use to obtain it. If your environment is fully observed and determined, tasks are repetitive, and you don’t need to adapt to unplanned changes, a simple LLM pipeline is just enough.

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 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.

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