Your interface may look right in a static mockup - but users don’t experience static pages. They see content appear, load, and shift. Perceived performance often matters more than raw performance metrics. A page that loads in 800ms but shows a blank screen feels slower than one that takes 1.2 seconds but shows skeleton placeholders from the first frame.
Open any README in your repository. That flagship one. The one that's 800 lines long with a "Getting Started" section written in 2022. Read it with fresh eyes - as if you were a new developer, or better yet - as an AI agent who's never been to a standup, never seen Slack, never heard the legend of why we don't touch the InvoiceReconciler class in the payment service. Now ask yourself one question: based on this README, can you safely modify anything in this service?
Discover how TypeScript is becoming the language of intent, reshaping engineers into system designers who define contracts while AI handles code.
All the interactions discussed so far need to work across devices - and on mobile, they face a different set of constraints. More than half of all web traffic comes from mobile devices, yet many developer-built interfaces are designed cursor-first and touch-optimized as an afterthought. Mobile isn’t a smaller desktop - it has its own interaction model, constraints, and opportunities.
While API based LLMs are great for rapid, fast, and easy development, they can be less secure and costly in the long-term horizon for load-intensive applications. The solution are Small Language Models (SLM), self-hosted and finetuned on the downstream task. This article presents a case study of a Supervised Fine-Tuning (SFT) of the SLM on the Invoice Processing task. It shows that while SLMs have higher investment costs at start, they are faster, cheaper, and more secure in the long-term, especially for high-load intensive applications.
You've probably heard about the first METR study from July 2025 - it made the rounds at every conference and every newsletter. 16 experienced open-source developers, a proper randomized controlled trial (not a vendor survey), and the result: 19% slower with AI. In this article, Artur argues that the problem lies in the environment, not the model. Read on to find out exactly.
Monorepo keeps coming up in conversations about large-scale software architecture. For some organizations, it’s a way to bring order to a growing ecosystem of applications. For others, it raises a lot of concerns. We spoke with Bartek Sądel, an expert who works with enterprise monorepos, about how this approach works in practice, what questions companies ask, and what the real benefits and challenges are.
Welcome to GitHub All-Stars, our biweekly series where we pick a trending or freshly minted open-source project and put it under the microscope. We focus on new, relatively unknown gems - not another breakdown of the latest React release (because let's be honest, the world has enough of those). This time, we're looking at a project that lives at an unusual intersection of prepper culture, self-hosting enthusiasm, and edge computing philosophy. And it just exploded on Hacker News.
For years, the industry moved away from monorepos toward microservices and smaller repositories. Recently, however, the conversation has started to shift again - and AI may be one of the reasons. As AI tools become part of everyday development workflows, the way engineers interact with large codebases is changing. Piotr Kukiełka shares his perspective on how large codebases work in practice and why AI might influence how companies think about monorepos.
Learn how insurers can secure AI agent platforms through threat modeling frameworks like OWASP, MAESTRO, and MITRE ATLAS. This guide covers prompt injection risks, trust boundaries, and compliance with DORA and EU AI Act.
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?
