Version managers, in general, are hacks. But they are useful hacks, as they solve a real problem: we all work on multiple projects at the same time, and each project needs a different version of tooling like Go, Python, or Ruby. It’s not enough to install a single version system-wide and hope for the best. Even if we can get away with using a single version across all projects, it would only be temporarily and accidentally working.

APIs are vital in today’s digital transformation, but their seamless delivery is equally crucial. However, many organizations still fragment their API development and UI testing processes, leading to delayed issues detection, longer release times, and even missed bugs. This approach is unsustainable in a fast-paced environment where time-to-market and user experience are key. A unified strategy that integrates API management with UI testing is essential.

The recently published Independent Investigation of the National Health Service (NHS) in England offers valuable insights into the current challenges faced by one of the world’s largest healthcare systems. Among the many findings, the report highlights significant issues with patient access, quality of care and system performance.

For the last 10+ years, Docker has been one of the leading technology tools for helping developers build, run, update, and manage containers. It is one of the most widely used containerization tools available, able to work with Linux, Microsoft Windows, Apple MacOS, and all of the leading cloud service providers. With over 20 million monthly users, you’ve likely used Docker before, whether for your own projects or as part of a larger team.

Ukraine is a leading choice for IT outsourcing, thanks to its skilled talent and strong cultural alignment with Western clients. The country offers a deep pool of tech experts, a solid STEM (Science, Technology, Engineering, and Mathematics) foundation, and a thriving tech industry, making it a valuable option for businesses. When you work with Ukrainian firms, you get reliable software, smooth collaboration, and a shared drive for innovation.

The journey from a great idea for a Generative AI use case to deploying it in a production environment often resembles navigating a maze. Every turn presents new challenges—whether it’s technical hurdles, security concerns, or shifting priorities—that can stall progress or even force you to start over.

Computational chemistry relies on large volumes of complex data in order to provide insights into new applications, whether it’s for electric vehicles or new battery development. With the emergence of generative AI (GenAI), the rapid, scalable processing of this data has become possible and critical to investigate previously unexplored areas in catalysis and materials science.

Self-service kiosks have rapidly become a staple in the retail landscape, symbolizing the growing trend toward automation in customer interactions. In 2023, the global self-checkout market was valued at approximately $4.5 billion, with estimates suggesting it could reach between $5.2 and $6.0 billion by the end of 2024. This surge is driven by increasing consumer demand for convenience, technological advancements, and the pursuit of more seamless shopping experiences.

How are AI governance and data governance related? Better still, what’s more important for an organization to focus on, AI-powered data governance or AI data governance? These are important questions, but before we answer these, let’s understand how AI and data governance are related to each other.

In this Digital era, getting your own hands into every operational activity is not the right thing to do. There are IT transformation consulting experts whose experience and skill set can be leveraged to reduce time, effort, and cost for your organization. In that context, today’s organizations are delivering more and more into cloud-based infrastructure.