Systems | Development | Analytics | API | Testing

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.

Marketing should be the hub of go-to-market strategy – not the internal agency that just says “yes” to everything. But what does it take to actually shift that perception and function?

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.

Modern data architectures rely on purpose-built analytics data platforms, stakeholder buy-in, and flexible, automated data integration.

Common Vulnerability and Exposures (CVE) collects known cybersecurity vulnerabilities and exposures to help you to better safeguard your embedded software. This framework is central to managing security threats effectively. Here, we explain what is CVE, unpack the role of CVE identifiers, examine the differences of CVE vs. CWE, expand on the CVE list, and outline how identifying vulnerabilities early in software development can be achieved with the right static analysis tools.

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.

You’ve translated the app and maybe even hired native speakers. It passes all your internal checks, but users in new markets are still dropping. The problem often isn’t obvious. Putu Kusumawardhani , Director, Client Impact,

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.

Software development isn’t what it used to be, and that’s a good thing. What was once a linear code-and-release process has evolved into a dynamic, AI‑powered, security-first ecosystem spanning cloud, edge, and compliance workflows. In 2024, the global software development market reached $403.6 billion and is expected to climb further, driven by AI, cloud-native architectures, and embedded automation.