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In the last two years, generative AI has moved from "cool demo" to become an integral element of IT production. The research proves this trend: according to Fortune Business Insights, global spend on generative AI reached an estimated $67 billion in 2024. By 2032, this spending is expected to pass almost $1 trillion, with a compound annual growth rate of approximately 40%. Moreover, a McKinsey & Company survey finds that roughly two-thirds of companies have already integrated generative AI into their workflows, and 80% use it in its broad sense. This technological transformation, hence, poses a critical new question.

Finding better ways to acquire storage on your Mac is a great idea, and at first it might sound rather difficult to achieve. But realistically, getting more storage on your Mac is actually not extremely difficult. It definitely has its issues, but generally, you will find plenty of methods to supplement your storage. Here are some tips.

In a content-driven world, speed and creativity win. Whether you're building a social post, an explainer video, or a product showcase, video grabs more attention than text or images alone. But what if you could create high-quality videos-fast, flexible, and without editing skills? That's where the Freepik AI Video Generator comes in. Designed for creators, marketers, educators, and hobbyists alike, Freepik's tool uses AI to generate short, cinematic videos in just a few clicks.

When budgets tighten, it's easy to cut expenses across the board, but doing so can overlook the essential need for business continuity. Strategic investment in continuity isn't about spending more - it's about spending wisely to protect core operations and stay resilient against disruptions.

The European hardware development industry is experiencing remarkable growth, with the market reaching $135.43 billion in 2025 and projected to reach $164.56 billion by 2029, representing a 5% compound annual growth rate. This expansion is driven by advancements in IoT, embedded systems, and industrial electronics, making Europe a global hub for hardware innovation.

What comes to mind when you hear the word 'risk' linked to your business? Maybe it's something big and obvious, with alarms going off all over the place and everything suddenly getting chaotic and maybe even falling apart - that's what a lot of people would imagine. But the truth is that real workplace risks aren't always like that (in fact, they're hardly ever like that), and they tend to be much quieter and slower. It'll be something like a missed update or someone making an assumption about something, or perhaps you'll notice an issue and then forget to deal with it because you got caught up in other things.

December brings a flood of Cyber Monday VPS deals, each promising unbeatable savings. The challenge isn't finding deals. It's identifying which ones deliver actual long-term value versus temporary promotional pricing that evaporates after a few billing cycles. This guide evaluates Cyber Monday VPS deals using three core metrics: total cost over realistic usage periods, included features versus add-on fees, and management requirements that impact your team's time investment.

End-to-end testing has always been a double-edged sword - even more so in the world of microservices. On one hand, E2E tests are critical for validating that all services work together seamlessly in real user flows. On the other hand, many experts warn that heavy reliance on end-to-end testing in a microservices architecture can create a "distributed monolith," slowing down deployments and undermining the very agility microservices promise. There's truth to that: if done poorly, E2E tests can become brittle, flaky, and a bottleneck that reduces your deployment frequency.

Since the data lake concept emerged more than a decade ago, data lakes have been pitched as the solution to many of the woes surrounding traditional data management solutions, like databases and data warehouses. Data lakes, we have been told, are more scalable, better able to accommodate widely varying types of data, cheaper to build and so on. Much of that is true, at least theoretically. In reality, the way many organizations go about building data lakes leads to a variety of problems that undercut the value their data lakes are supposed to deliver.