Test suites have grown in size and complexity. What used to be a quick validation step has become a bottleneck that can significantly impact development velocity. The solution is to distribute test execution across multiple machines, running different test groups in parallel.
I’ve always claimed there’s no better way to learn anything than by building something yourself… and the second-best way is reviewing someone else’s code. From now on, every Wednesday we’ll pick one trending repo from the previous week and give it some attention: a tutorial, an article, or a code review - learning directly from its authors.
As we go full-AI mode, we want to inspire fellow developers with all the cool projects and experiments our devs are running with AI tools and agents. This is why we are launching the new series: “This Month We AIed” initiated by the Scala expert and SoftwareMill co-founder, Adam Warski.
This first edition curates in-depth, hype-free AI insights for engineers, from the pitfalls of multi-agent systems to the rise of AI-powered browsers. Expect contrarian takes, practical engineering tips, and real-world case studies over fleeting trends.
Explore how tool invocation works in LLMs like Claude and ChatGPT, blending prompt design with infrastructure. Understand the role of MCP in standardizing external tool integration for seamless AI-agent interactions.
Explore how MCP transforms JVM tools like WildFly and Ghidra into LLM-driven operations, diagnostics, and reverse engineering servers. Discover how agents can now run diagnostics, decompile malware, and even query applications—all via chat.
If you are using AI coding assistants or agents, they will only be as helpful as their knowledge of the libraries you use. LLMs know a lot about the world from their training set, but that might not include a library's documentation at all or include an older version. Agents can browse the Internet looking for more recent information, but this might not be precise enough. For instance, the web might be littered with outdated examples, and the latest information might only be available in the official docs. Hence, if you are a library author, how do you prepare your documentation for consumption by coding agents?
GenAI and LLMs are distinct AI models, but have so many commonalities that some users struggle to tell them apart. To find the right application for them in a business environment, we will discuss the differences between the two.
Read on to find out what they are and what makes them a valid storage option for solutions built around edge reference architecture. You will also learn how the combination of data lakes and edge computing applies in real life.
Java has come a long way on ARM. Runtime improvements, smarter garbage collectors, and better vector performance now make it a solid fit. CI/CD pipelines work without extra effort. This guide walks through: - real-world benchmarks - examples from AWS and Google Cloud - gives hands-on advice for tooling, deployment, and compatibility If you're weighing cost, speed, or sustainability, this will help you judge whether a move to ARM makes sense—and how to pull it off cleanly.
Developers often find themselves in a dilemma: how can they ensure their software runs well for the foreseeable future? The mantra "You Build It, You Run It" emerged as a natural answer. Having your development team manage the product throughout its lifecycle offers certain benefits, but it's not a one-size-fits-all solution. Fortunately, there are alternatives.
We’re excited to announce that VirtusLab will be attending the Data Innovation Summit 2025 as a Venue Partner! Whether you're deep into machine learning, curious about data maturity, or just looking for some good conversation (and maybe a cup of tea), we’d love to meet you there.
