DEVOPSdigest

GenAI excels at handling repetitive coding tasks, but it still relies on developers to guide the work through smart prompting, critical judgment and contextual oversight to ensure outputs meet real-world needs. In fact, even top-performing large language models (LLMs) like Claude 3.5 could only solve fewer than half of real-world engineering tasks. This evolution makes human expertise more important than ever. It's a call to rethink the role of developers — not in terms of what the industry is giving up to AI, but what the industry can gain by working alongside it ...

Gartner Inc. predicts that organizations will develop 80% of Generative AI (GenAI) business applications on their existing data management platforms by 2028 ...

As the European Accessibility Act (EAA) deadline draws closer, my organization, Applause, just released the results of our fifth annual State of Digital Quality in Accessibility survey ... Let's start with the good news. Digital accessibility awareness has steadily grown over the past four years, with the majority of organizations considering it a priority ...

Over the past two years, code assistants based on generative AI have transformed software coding, accelerating the generation of code on an unprecedented level. Developers are deploying more code than ever, but at a cost: exponential growth in security vulnerabilities. New research points to a 3X increase in repositories containing Personally Identifiable Information (PII) and payment data, a 10X increase in APIs without authorization and input validation, and more sensitive API endpoints exposed, all threats proliferated by AI-generated code. Though AI code assistants boost productivity, they possess no understanding of organizational risk, compliance policies, or security best practices, leaving companies more exposed ...

More than three-quarters (77%) of engineering leaders identify building AI capabilities into applications to improve features and functionality as a significant or moderate pain point, according to a survey by Gartner. The survey also found that the use of AI tools to augment software engineering workflows was the second largest pain point ...

Kubernetes was not initially designed with AI's vast resource variability in mind, and the rapid rise of AI has exposed Kubernetes limitations, particularly when it comes to cost and resource efficiency. Indeed, AI workloads differ from traditional applications in that they require a staggering amount and variety of compute resources, and their consumption is far less consistent than traditional workloads ... Considering the speed of AI innovation, teams cannot afford to be bogged down by these constant infrastructure concerns. A solution is needed ...

While nearly two in three organizations (63%) claim architecture is integrated throughout development (from design to deployment and beyond), more than half (56%) have documentation that doesn't match the architecture in production, according to the 2025 Architecture in Software Development study from vFunction.