Systems | Development | Analytics | API | Testing

Reading a lot of code from very senior engineers is probably one of the best ways to level up as a Ruby on Rails developer. By doing so, we can learn new tips and techniques that we can reuse in our jobs. Thanks to open source, we can read code written by the best developers from all over the world, and for free! However, reading code from a Ruby gem or a Rails engine for the first time without being guided can be daunting. There are so many files; how do we even know where to start?

In part one of this series, we explored how Angular Signals shift the reactive model away from the RxJS-centric approach we’ve relied on for years. We walked through the core API signal(), computed(), and effect() primitives, and demonstrated how they simplify state management by removing the need for subscriptions, teardown logic, and deeply nested observables. We also introduced a minimalist CartService that held cart items in a private signal and exposed a computed total price.

We're very excited to announce Kong Operator 2.0! Kong Operator is an evolution of Kong Ingress Controller (KIC) and Kong Gateway Operator into a single Kubernetes offering to simplify deployment, management, configuration, and upgrades of your Kong Gateway instances on Kubernetes.

Over the years, Node.js developers have relied on countless npm packages to fill gaps in the platform. From HTTP utilities to file system helpers, the ecosystem has always been one of Node’s greatest strengths. But as Node.js continues to evolve, many features that once required third-party packages are now built into the runtime itself. This shift reduces dependency bloat, improves security, and makes applications easier to maintain.

AI coding tools like ChatGPT, GitHub Copilot, and Claude have completely changed how we write software. From humble beginnings where non-AI-enabled code assistants made intelligent code suggestions, like Intellisense, the latest agentic tools can generate entire functions, suggest optimal algorithms, and even scaffold complete applications in minutes. However, as any developer who’s worked with AI-generated code knows, the output isn’t always perfect.

Yes, you’ve heard it all before: the frequency of cyberattacks and their devastating aftermath, organizations’ gaps in protecting sensitive data, and the financial consequences of not complying with GDPR and the likes. I am not here to share any old news. But there is a risk that is not discussed frequently enough in the news. And it should be. How often do you suppose data in non-production environments is compromised or fails compliance audits?

To use synthetic test data or to use test data masking — that is the question. But the answer may not be what you expect. Before we dive into that, what’s happening in today’s business landscape that’s prompting the question around synthetic vs. masking? Delivering high-quality applications at lightning speed is expected in today’s CI/CD world. Fast time-to-market is at odds with security and compliance requirements.

Imagine that you need to know the exact location of the train that you’re about to catch. Trains can be equipped with IoT sensors that can publish location coordinates via Kafka streams in real time. You will be using a mobile application to view a train’s location. Mobile applications and web applications are compatible with WebSocket, a web-friendly protocol, but the location data is received via a Kafka stream. How do we convert the real-time Kafka feed into a real-time WebSocket feed?