Systems | Development | Analytics | API | Testing

You run your test suite, and a test fails. You re-run it—now it passes. No code changes, just random failures. Annoying, right? But here’s the scary part: flaky tests don’t just waste time—they slowly break trust in your entire testing process. Flaky tests show inconsistent results across runs. They create unreliable outcomes, hurting test accuracy. These tests may report false positives or negatives. False negatives flag non-existent defects, wasting time.

Supply chain disruption. Pandemics and medical crises. Climate change. Events that were once “black swans” are now near-everyday occurrences for citizens worldwide—and governments are facing growing pressure to provide effective public services in response. In the past, the ability to access, process, and apply data to make informed decisions about complex issues was crucial to creating effective public services.

People are tired of talking about artificial intelligence (AI). They want action. Since the launch of ChatGPT, the financial services industry has been looking for ways to drive value with AI, but it's been a struggle to get real value out of AI experiments and pilot projects. The banking industry prefers to avoid potential risks, so how can financial sector leaders move from AI experimentation to AI value while being mindful of risk tolerance?

Have you ever released a feature only to see it break spectacularly in production? If you’re a developer or an SDET (Software Development Engineer in Test), you’ve likely been there. That’s where Alpha and Beta Testing come in – your last line of defense before your users turn into bug reporters! But what exactly are Alpha and Beta tests? How do they differ? And why should you, as a developer or SDET, care about them?

In the fast-paced world of API development, understanding the distinction between stateful and stateless applications can be a game-changer. Did you know that stateless REST APIs are the backbone of scalable web applications, handling millions of requests with ease? Yet, for many developers, the nuances of these architectures remain a mystery. As the demand for efficient and scalable systems grows, grasping the fundamentals of stateful vs. stateless applications becomes increasingly vital.

Exceptions in C# are like fire alarms – they're loud, disruptive, and absolutely essential. And much like fire alarms, most beginners either ignore them or disconnect the batteries. Let me explain what we're dealing with here. In the C# world, exceptions are specialized objects that capture all the details when something goes wrong in your code. They're not just error messages – they're fully-fledged objects that inherit from the System.Exception class.

Concurrency is the ability of an app to perform multiple tasks at once, and it’s a crucial concept for apps that need to perform multiple tasks at once in an efficient, usable way. Thankfully Swift has made great strides with concurrency, and now provides simple tools for writing robust apps that are responsive and enjoyable to use. In this article we’ll explore two main ways of using threads for concurrency models.

Confluent has been instrumental in enabling customers from various industries to develop real-time stream processing solutions using Apache Kafka. While many of these use cases demand low-latency and real-time processing, stream processing is also increasingly being utilized for ingesting logging and telemetry data. This type of data typically features a high ingest rate, but allows for a higher tolerance for end-to-end processing time.

The global financial services industry is a complex landscape: a patchwork quilt of regulatory frameworks, technologies, and markets with very different customer needs. To be the most profitable, the most competitive, and the most efficient it can be, a financial services provider must find a way to navigate that complexity, and sales and finance teams hold pieces of the puzzle. And putting the pieces together isn’t as easy as it may look at first glance.