Operations | Monitoring | ITSM | DevOps | Cloud

Picking the right VS Code Git extension can shape how you move through your codebase every day. GitLens and Git Graph both add visual Git tools to your editor, but they take different paths to get there. GitLens gives you deep context about every line of code – who wrote it, when, and why. Git Graph focuses on visualizing your commit history in a branching timeline. This article breaks down each extension so you can decide which one fits your workflow.

Merge conflicts waste hours of development time every week. The Git branching strategy you pick directly shapes how often these conflicts appear and how painful they are to fix. GitKraken simplifies conflict resolution with visual tools that help you spot problems before they become blockers. This guide walks you through a step-by-step decision process for selecting between GitFlow and trunk-based development.

GPU infrastructure is expensive and increasingly central to production workloads. Whether you’re running ML training jobs, inference serving, video transcoding, or HPC workloads, understanding what your GPUs are actually doing, and what’s going wrong when performance degrades, is not optional.

Application security & engineering teams are under pressure to move faster, cover more, and reduce the operational drag that often comes with security testing. But in practice, two problems keep slowing teams down and adding friction.

Learn exactly how Amazon Comprehend pricing works, NLP, PII, custom models, endpoints, and how to control costs before they spiral.

Confluent Cloud pricing ranges from $1,000–$200,000+/month based on cluster type, throughput, and add-ons.

Why run Ubuntu on Windows? It’s about getting the best of both worlds. Many organizations rely on Windows applications, enterprise software, and policy configurations; but for developers and system administrators, Ubuntu’s native command-line tools, package managers, and server environments are invaluable. Likewise, with its broad ecosystem of machine learning tools and libraries, and silicon optimizations, Ubuntu is ideally suited for AI workloads.

When you launch a new service in production, you’re working with a lot of unknowns. You don’t yet know how it behaves under real traffic or which incidents are worth waking someone up for. That makes alerting for a new service a little different from what you’re used to with an established one. The goal in the early days isn’t to get everything perfectly configured. It’s to learn enough about the service to get your alerting right.

AI coding tools eliminated the 'writing code' bottleneck. But deploying that code? Still a mess. Here's how Claude Code + Qovery Skill lets you go from idea to production in a single prompt - with enterprise-grade guardrails. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.