I've had several incarnations of the self-hosted home lab for decades. At one point, I had a small server farm of various machines that were either too old to serve as desktops or that people simply no longer wanted. I'd grab those machines, install Linux on them, and use them for various server purposes. Here are two questions you should ask yourself:
Also note that I often alias ip to ip -br -c as it provides a much prettier output. Compare, before: anarcat@angela:~> ip a 1: lo: <LOOPBACK,UP,LOWER_UP> mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000 link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00 inet 127.0.0.1/8 scope host lo valid_lft forever preferred_lft forever inet6 ::1/128 scope host noprefixroute valid_lft forever preferred_lft forever 2: wlan0: <NO-CARRIER,BROADCAST,MULTICAST,UP> mtu 1500 qdisc noqueue state DOWN group default
Steve Yegge thinks he has the answer. The veteran engineer - 40+ years at Amazon, Google and Sourcegraph - spent the second half of 2025 building Gas Town, an open-source orchestration system that coordinates 20 to 30 Claude Code instances working in parallel on the same codebase. He describes it as "Kubernetes for AI coding agents." The comparison isn't just marketing. It's architecturally accurate.
Support for distributed systems. Check how well the tool handles microservices, serverless, and Kubernetes. Can you follow a request across services, queues, and third-party APIs? Does it understand pods, nodes, clusters, and autoscaling events, or does it treat everything like a static host? Correlation across metrics, logs, and traces. In an incident, you shouldn't be copying IDs between tools. Look for the ability to pivot directly from a slow trace to relevant logs,
Because of that, you need to be very familiar and comfortable with the command line. Or you can install a desktop environment. In my opinion, this is the single easiest way to make Ubuntu Server easier, especially if you're relatively new to Linux. Having a GUI desktop will strip away the fear of having to use the command line, because you'll have plenty of apps to use (such as the file manager, user manager, GUI app store, and much more).
They called out about half a dozen particular instances of what they considered to be bullshit technology. We were too busy laughing sympathetically to whip out a laptop to make notes, but as best as we can recall the sequence, they were: Containers Kubernetes The "Cloud" Anything at all "as a Service" The Blockchain - anything, everything, based on it And now, arguably the biggest and worst of all, "generative AI"
The main advantage of going the Multi-Cloud way is that organizations can "put their eggs in different baskets" and be more versatile in their approach to how they do things. For example, they can mix it up and opt for a cloud-based Platform-as-a-Service (PaaS) solution when it comes to the database, while going the Software-as-a-Service (SaaS) route for their application endeavors.
When I manage infrastructure for major events (whether it is the Olympics, a Premier League match or a season finale) I am dealing with a "thundering herd" problem that few systems ever face. Millions of users log in, browse and hit "play" within the same three-minute window. But this challenge isn't unique to media. It is the same nightmare that keeps e-commerce CTOs awake before Black Friday or financial systems architects up during a market crash. The fundamental problem is always the same: How do you survive when demand exceeds capacity by an order of magnitude?
Percona recently announced OpenEverest, an open-source platform for automated database provisioning and management that supports multiple database technologies. Launched initially as Percona Everest, OpenEverest can be hosted on any Kubernetes infrastructure, in the cloud, or on-premises. The main goal of the project is to avoid vendor lock-in while still providing an automated private DBaaS. Built on top of Kubernetes operators, it aims to avoid complex deployments that depend on a single cloud provider's technology.
Running a global observability platform means one thing above all: your infrastructure must never go down. When you're responsible for monitoring thousands of customers' applications 24/7, network failures aren't just inconvenient, they're existential threats. At New Relic, hundreds of clusters run on multiple clouds, and regions. These clusters depend on a complex web of network connections: regional transit gateways, inter-regional hubs, and cross-cloud links.
I recently wrote about my migration away from VirtualBox to KVM/Virt-Machine for my virtual machine needs. I've found those tools to be far superior (albeit with a bit more of a learning curve) than VirtualBox. Since then, however, I've found another method of working with KVM (the Linux kernel virtual machine technology), one that not only allows me to create and manage virtual machines on my local computer, but also from any machine on my LAN. That tool is Cockpit, which makes managing your Linux machines considerably easier.
Linux has a tool for everything. Sometimes those tools come in the form of an easy-to-use GUI, and other times a command is necessary. For monitoring network traffic, your best bet is the command line. Once you dive down the rabbit hole of possible commands for this task, you could become overwhelmed with choices -- and with the complexity of some of those commands.
Lead without authority. You may not have direct reports, yet you shape architecture, quality and the roadmap. Your leverage comes from artifacts, reviews and clear standards, not from title.I started by publishing a lightweight architecture template and a rollout checklist that the team could copy. That reduced ambiguity during design and cut review cycles by nearly 30 percent
As organizations increasingly adopt cloud-native architectures, managing communication between microservices becomes a critical challenge. Modern applications are often distributed across multiple Kubernetes pods and ensuring secure, reliable and observable interactions between these services is essential. This is where Istio and Envoy sidecars come into play. Together they form a service mesh solution that abstracts networking complexities, enforces security policies and provides deep observability - all without requiring changes to application code.
Hakboian describes a pattern in which specialised agents: one for logs, one for metrics, one for runbooks and so on, are coordinated by a supervisor layer that decides who works on what and in what order. The aim, the author explains, is to reduce the cognitive load on the engineer by proposing hypotheses, drafting queries, and curating relevant context, rather than replacing the human entirely.
What do we do at Fly? We are a developer-focused cloud platform. That means we make it easy for developers to get their apps deployed, up and running. Something I think that really differentiates us is that we make it easy to deploy your apps in different regions over the world. We are available in 40 different regions. It's basically like a CDN, but for your apps.
Automation is transforming IT service management (ITSM), moving service desks from reactive, manual workflows toward systems that can intelligently route, prioritize, and resolve issues with minimal human intervention. Recent research from Freshworks found that IT professionals lose nearly seven hours every week-almost a full workday-to fragmented tools and overly complicated work processes. Implementing ITSM automation reduces manual effort, accelerates resolution, improves consistency and accuracy, enables proactive issue prevention, and delivers faster, more reliable service that measurably improves employee and end-user satisfaction.
Charlie Marsh announced the Beta release of ty on Dec 16 "designed as an alternative to tools like mypy, Pyright, and Pylance." Extremely fast even from first run Successive runs are incremental, only rerunning necessary computations as a user edits a file or function. This allows live updates.
Unified observability signifies the possession of one consistent view over all the parts of a distributed system - applications, infrastructure, and services. With modern systems being spread over several clouds, microservices, and APIs, the only way to have such visibility is through open standards and distributed tracing. Standards such as OpenTelemetry not only make it easier to collect but also to correlate the telemetry data (logs, metrics, and traces) from various components.
For more than a decade, many considered cloud outages a theoretical risk, something to address on a whiteboard and then quietly deprioritize during cost cuts. In 2025, this risk became real. A major Google Cloud outage in June caused hours-long disruptions to popular consumer and enterprise services, with ripple effects into providers that depend on Google's infrastructure. Microsoft 365 and Outlook also faced code failures and notable outages, as did collaboration platforms like Slack and Zoom. Even security platforms and enterprise backbones suffered extended downtime.
When a system is overwhelmed with more requests than it can effectively process, a cascade of problems can ensue, significantly undermining its performance and reliability. One of the most immediate and noticeable consequences is the degradation of performance. In such scenarios, users may face frustratingly slow response times or complete timeouts in more severe cases. This not only hampers the user experience but can also erode trust in the system's reliability.
Identity and authentication services company Authress shared its strategy to stay operational during major cloud infrastructure outages like the massive October 2025 AWS outage that disrupted many major services. The company's resilience architecture relies on strategies like multi-region deployment and minimizing reliance on AWS control plane services, Authress CTO Warren Parad explains. Parad says the AWS October 20 incident was the worst seen in a decade. Even so, Authress maintained its SLA reliability commitments thanks to a reliability-first design centered on a failover routing strategy.
Real-Time Visibility: Instantly monitor test progress and quality trends to catch regressions before they impact your release. Comprehensive Analytics: Dive into historical data with built-in dashboards that break down results by outcome, priority, configuration, and failure type. Effortless Management: Use powerful filters such as timeline, run type, pipeline, and more, to find exactly what you need. Customize your view with persistent search and column visibility settings.
