Software

As the cloud native landscape continues to evolve, a key question is emerging: can Kubernetes serve as a unified platform for both virtual machines (VMs) and containers? This move reflects broader industry trends towards consolidation and efficiency, but it’s not without its challenges. At the heart of this discussion is the ability to manage diverse workloads on a single platform, streamlining operations and reducing costs. The idea of running VMs and containers on the same platform is enticing, but it requires a fundamental shift in skills, expectations, and migration strategies. VM operators, accustomed to working with VMware, Hyper-V, or Nutanix, must adapt to Kubernetes’ ephemeral pods, policy-driven networking, and abstracted storage. This skills gap is being addressed by open source projects like KubeVirt, which extends Kubernetes to manage VMs in a familiar way, and Red Hat’s OpenShift Virtualization, which provides a standalone license for hosting VMs on Kubernetes. ...

As companies navigate the complex landscape of digital transformation, they often encounter two types of debt that can hinder their progress: technical debt and architecture debt. While technical debt is a well-known concept, architecture debt is a more insidious and hidden threat that can silently sabotage AI, cloud, and transformation initiatives. Technical debt refers to the shortcuts taken by developers to deliver software faster, which can lead to delays, instability, and rising maintenance costs. In contrast, architecture debt is a more systemic issue that arises from flaws in the overall structure of systems, integrations, and processes. It can manifest in duplicate platforms, fragile integrations, and outdated governance models, making it a more challenging problem to diagnose and fix. ...

The Delicate Balance of Autonomy and Trust in AI As AI systems become increasingly autonomous, the need to balance autonomy with trustworthiness has become a critical concern. This move reflects broader industry trends towards more responsible and transparent AI development. The lack of clear responsibility in AI decision-making can create an accountability vacuum, eroding public trust and leading organizations into ethical and legal trouble. To navigate this complex issue, it’s essential to understand the spectrum of autonomy in AI systems. On one end, human-in-the-loop systems provide passive assistance, while on the other end, autonomous systems operate independently with minimal human intervention. The six pillars of trustworthy AI - algorithmic fairness, transparency, reliability, accountability, data safety, and human centricity - serve as the foundation for designing and deploying AI systems that balance autonomy with trust. ...

As the world becomes increasingly reliant on high-performance networks, a recent test by Deutsche Telekom has exposed significant limitations in IPv6, the next-generation Internet protocol. This move reflects broader industry trends towards large-scale networking and the need for more efficient protocols. In a bid to simulate dynamic satellite networks, Deutsche Telekom pushed the limits of Kubernetes, containers, and networks, ultimately revealing that IPv6 is not yet ready for large-scale deployments. ...

The Ripple Effect of a Single Misconfiguration On October 20th, 2025, Amazon Web Services (AWS) experienced a significant outage in its US-EAST-1 Region, affecting numerous cloud services, including AWS Lambda, Amazon API Gateway, and Amazon Appflow. The incident serves as a reminder of the potential consequences of a single misconfiguration, which can quickly escalate into a cascade of failures. The issue began with a misconfigured DNS, which soon affected EC2 launches, causing errors and disruptions to various services. Despite initial confidence in resolving the problem, the situation worsened, with the Lambda service experiencing significant recovery issues. The outage had a profound impact on major online businesses, including Snapchat, Reddit, Venmo, and Apple Music, which rely heavily on AWS. ...

As organizations rapidly adopt the Model Context Protocol (MCP) to connect services and data to AI models through AI agents, they’re encountering familiar challenges: securing access to MCP servers and tools while providing routing, rate limiting, observability, and developer portals. This move reflects broader industry trends towards cloud-native technologies and the need for more sophisticated API management. The question on everyone’s mind is: can we just use our existing API gateway for MCP? The short answer is “maybe,” but the real question is, should you? API gateways were not built for MCP use cases, and eventually, most API gateway vendors will build dedicated MCP gateways. ...

As organizations embark on their AI journeys, they often overlook a critical component: data hygiene and governance. This oversight can lead to stalled AI initiatives, despite the presence of advanced models. The root of the problem lies in the fact that AI is only as good as the data that feeds it. In this article, we’ll explore why data hygiene, governance, and experimentation are essential for unlocking AI potential. The importance of data access for AI cannot be overstated. Without strong data access, models are unable to utilize the data they need, resulting in technological headaches and stalled projects. This is where data federation comes into play, providing a solution to the data access problem. By making distributed data sets accessible wherever they live, data federation enables governance and fine-grained access controls, solving the data access issue in an elegant and sophisticated manner. ...

As the AI landscape continues to evolve, the need for secure and confidential computing has become a top priority. This move reflects broader industry trends towards prioritizing data protection and security in cloud computing. At the OpenInfra Summit Europe 2025, NVIDIA emphasized the importance of combining Kata Containers with Confidential Computing to preserve bare-metal GPU performance while preventing cloud operators from inspecting sensitive model and data. Kata Containers, an open-source project, provides lightweight VMs for containers, using hardware virtualization technology to launch a separate VM for each container. This approach offers the performance benefits of containers along with the security and workload isolation of VMs. Confidential Computing, on the other hand, brings in-memory data and application encryption, ensuring that even the cloud provider cannot access sensitive information. ...

AIOZ Stream launches as creator-first alternative to centralized streaming giants AIOZ Network unveiled AIOZ Stream on September 15, 2025—a decentralized peer-to-peer streaming protocol that promises to disrupt the $670+ billion video streaming industry by putting creators back in control of their content and revenue. Built on a global network of over 200,000 edge nodes, the platform delivers video content through blockchain-verified transactions while offering transparent, token-native monetization that stands in stark contrast to the opaque revenue-sharing mechanics of YouTube and other Web2 platforms. The launch represents a significant milestone for decentralized infrastructure, combining high-performance content delivery with creator ownership and verifiable on-chain payouts that could reshape how the internet handles streaming media. ...