Systems | Development | Analytics | API | Testing

No one starts their work day thinking “Let’s investigate the memory allocations of my Gradle build”, but sometimes life happens (usually at the worst possible time): Every Gradle project faces memory allocation problems eventually, as the codebase grows. So, instead of blindly applying JVM flags from Stack Overflow until it’s resolved, why not take a deeper look?

Choosing the right IoT protocol for edge microservices can save energy, reduce latency, and improve efficiency. Here's a quick guide: MQTT: Lightweight and energy-efficient, ideal for resource-constrained devices. AMQP: Reliable and secure, great for mission-critical applications but resource-intensive. CoAP: Best for low-power, low-bandwidth devices with minimal energy needs. HTTP/REST: Widely compatible but consumes more energy and bandwidth.

It's not even 9 AM, and the day's already started. A customer can't reset their password. The product team just found a bug in the app. A partner bank needs data synced ASAP. Oh, and the CEO wants to know when the mobile app can do instant loans. Sounds about right? Yeah, for banking leaders, this is just another Tuesday. Customers want everything to be easy and digital. Stakeholders want things done well, safely, and by the book. And nobody has time for old-school stuff.

APIs have become the backbone of communication between applications in today’s software-oriented world. Digital systems are increasingly complex due to the proliferation of microservices and distributed systems, making API testing both efficient and crucial. A recent study projects the API testing industry to grow from USD 4.92 billion in 2025 to USD 27.38 billion by 2034, reflecting a compound annual growth rate (CAGR) of 20.99% during the 2025-2034 timeline. Source: Market Research Future.

AI coding assistants are transforming the way developers approach software development by automating routine tasks and enhancing code quality. These tools leverage artificial intelligence and machine learning to provide real-time code suggestions, auto-complete functions, and even debug existing code, making the development process faster and more accurate.

As LLMs become central to AI-driven products like copilots and customer support chatbots, data science teams need to ensure the LLM performs well for the use case. The process of LLM evaluation ensures reliability, safety and performance in production AI systems. In this guide, we explore how to approach evaluations across development and production lifecycles, what frameworks to use, and how the integration between open-source MLRun and Evidently AI enables more scalable, structured testing.

Playlists for every mood and occasion. Media recommendations grouped by the most niche theme from your watch history. Sophisticated ad algorithms that optimize pay-per-click ads for the customer experience. Whether you call them digital-native, disruptors, or just tech giants, the likes of Spotify, Netflix, and Amazon have long made uncannily personal experiences a key part of their differentiation or business models.

Coding isn’t just a chore anymore – it is an experience. With the rise and advancement of AI-driven coding tools, developers are now able to enjoy what Andrej Karpathy calls "vibe coding" – a state where we turn off our control and let the tools vibe with us. In this blog post we are going to discuss how we can tap into vibe coding to ship faster, feel creative, and stay in flow.