Around 66,000 CVEs are expected in 2026. Most won't affect your code, but which ones will? A practical look at layered, AI-supported vulnerability triage.
For a bank, introducing autonomous coding agents is an architecture, security and governance problem. Here is how Visdom addresses data residency, machine-enforced boundaries, credential security, AI tracing, audit integrity, human oversight, code quality, test verification, organizational context and cost governance.
Most of the AI-generated code already in your codebase arrived through unsanctioned tools on personal accounts. This is Shadow AI — and like shadow banking before 2008, the risk is invisible until it isn't. Here's how to govern it with risk-based triage, traceability, and Continuous Modernization.
AI tools work. Adoption is above 80%. And yet measurable business value remains stubbornly low. The bottleneck is not the model — it's the software delivery system around it.
A coding agent is not an AI-native SDLC. Visdom provides the control plane — context, orchestration, sandboxing, and auditability — required to turn AI-generated code into software enterprises can trust in production.
Kotlin's DeepRecursiveFunction turns deep recursion into a heap-allocated trampoline while keeping direct-style code, built on suspend functions. This post explains why that works, then builds a Scala 3 macro that does the same trick with no coroutines and no Project Loom, by rewriting a recursive method into a TailRec state machine at compile time.
Daml is a functional language for writing smart contracts on Canton Network — a distributed ledger built for regulated, multi-party workflows. This guide covers Daml syntax, templates, choices, testing with Daml Script, and local debugging tools.
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.
