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VirtusLab's Articles

Scala|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.

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Artificial Intelligence|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.

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Artificial Intelligence|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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Artificial Intelligence|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.

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Artificial Intelligence|Sep 18, 2025

This Month We AIed #2

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.

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Artificial Intelligence|Sep 17, 2025

GitHub All-Stars #4 - CopilotKit: Solving the “Last Mile” Problem for AI Agents

In this edition of GitHub All-Stars, we look at a key building block of agentic applications - UI- and how Human-in-the-Loop can be implemented in practice. Let’s be honest: despite the marketing, no agent solution is perfect, and there will always be moments when we, the protein-based organisms, are needed. That’s why any application that aims to solve the problem realistically has to face this challenge head-on.

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Artificial Intelligence|Sep 12, 2025

AI glossary: Key terms to help you understand artificial intelligence

To stay up to date in the Artificial Intelligence solutions era, it's critical to become acquainted with the fundamental terms, concepts, and jargon that characterize this fast-developing discipline. This article will help you discover the foundational concepts of neural networks, deep learning, natural language processing, and beyond.

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Artificial Intelligence|Sep 10, 2025

GitHub All-Stars #3: LangExtract – How Google Turns Chaos into Data with Gemini

Every Wednesday, we’ll pick one trending repository from the previous week and give it attention, preparing a tutorial, article, or code review—learning from its creators along the way. Today, we’re taking a fresh project from Google engineers to the bench: Google/langextract.

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Artificial Intelligence|Sep 3, 2025

GitHub All-Stars #2: Mem0 - Creating memory for stateless AI minds

As part of our GitHub All-Stars series, where we examine open-source gems, I stumbled upon a project that strikes at the heart of one of the most fundamental problems in modern AI. After analyzing deepagents and its system-level approach to reasoning, the natural next step was to examine how we solve the problem… of memory.

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Artificial Intelligence|Aug 27, 2025

GitHub All-Stars #1: deepagents - Architecture of Deep Reasoning for Agentic AI

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.

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Scala|Aug 27, 2025

Distributing Test Execution with SBT: A Complete Guide to Parallel CI/CD

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.

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Artificial Intelligence|Jul 14, 2025

Providing library documentation to AI coding assistants

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?

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