Explore key insights from the Scala Survey 2026, including Scala 3 adoption rates, popular libraries, tooling preferences, and industry trends shaping the future of the Scala ecosystem.
Software supply chain security is a problem which, if ignored, can easily cause anything ranging from a minor bug to a literal disaster. Should we be scared? What can we do to be safe? This article will do its best to answer these questions briefly, while still doing justice to how serious the danger is. As a bonus, I will also mention how a build tool called Bazel can help in the fight.
This practical guide demonstrates how to implement sandboxed LLM coding agents using Agent Sandbox. Learn the complete setup process, from initialization and runtime configuration to managing network policies and handling authentication. Discover advanced patterns for Java projects, IDE integration, and security considerations for safe AI-assisted development workflows.
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.
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.
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?
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.
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.
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?"
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.
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.
