Systems | Development | Analytics | API | Testing

Generative AI models are not just tools for producing text, audio or video—they're systems that learn patterns, improvise, and generate unexpected outcomes. When we look at LLMs, we're struck by their capacity to generate surprisingly creative and context-aware results. They can weave coherent narratives, propose novel solutions, mimic human conversation, and even offer nuanced insights across a wide range of topics. While this creativity is their strength, it also introduces variability and risk.

In 2025, QA teams are experiencing real change. Product delivery cycles are becoming shorter, Agile is maturing, and there’s increasing pressure to launch new software quicker and without flaws. There’s also evident talk surrounding AI as the biggest factor for change, and its impact is also increasing. But the truth is AI is just one factor in this story. Other trends - like continuous automation, DevOps integration, and the role of QA - are redefining the way we test and ensure quality.

Retailers must use flexible, scalable architectures to integrate diverse, fragmented data sources and create better customer experiences.

In the field of software quality assurance, there are two types of testing often referenced, smoke testing and regression testing. While they are both vital to software quality, each has its own unique functions and overlaps in the software development cycle. This post explores the differences between smoke testing vs regression testing, and when and how to effectively implement each.

How we architected a responsive, HTTP-aware scale-to-zero mechanism to improve resource efficiency across cloud native workloads in Choreo. Cloud platforms promise elasticity, but achieving true efficiency, especially for idle or sporadically used services, requires more than just horizontal scaling. At Choreo, we set out to rethink how workloads behave when they're not actively serving requests.

Though storage systems and cloud storage services are well known, organizations should consider enhancing a handful of key areas in their storage infrastructure to prepare for the future. While some consider storage systems, management software and services to be commodities, differentiation between offerings can determine the scalability, flexibility, efficiency and resiliency of the data storage infrastructure.

Automation can make cloud migration faster, cheaper, and safer. It reduces migration time by 50%-80%, lowers costs by up to 30%, and minimizes risks like data loss and downtime. Manual migrations often fail due to human error, complexity, and unforeseen expenses. Here's how automation solves these challenges.

Developing high-quality products means carefully defining and tracking both functional and non-functional requirements (NFRs). But what exactly are non-functional requirements and how can you best manage them? This guide will help you understand the difference between functional and non-functional requirements with real-world examples. It also covers best practices for writing them and discusses how to track and manage non-functional requirements for greater efficiency.