Our AI journey began with SoftPro because we are currently assisting a significant number of clients of all sizes in transitioning from legacy TPS platforms to SoftPro. These clients have already benefited from our RezeCore TPS data migration suite to search, migrate and retain their historical TPS data. Building on that success, it was a natural next step to continue supporting them by providing this advanced and intelligent AI solution.
To dislodge that, OpenAI would need to deliver a platform that is meaningfully AI native rather than AI augmented. That means the repository itself becomes a living system that continuously understands the codebase, its intent, and its risks, rather than a passive store of files.
The Harness Resilience Testing platform extends the scope of the tests provided to include application load and disaster recovery (DR) testing tools that will enable DevOps teams to further streamline workflows.
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.
Blue/green deployments on Amazon Elastic Container Service (Amazon ECS) have long been a go-to pattern for shipping zero-downtime deployments. Historically, the recommended approach in the AWS Cloud Development Kit (AWS CDK) was to wire ECS to AWS CodeDeploy for traffic shifting, lifecycle hooks, and tight integration with AWS CodePipeline. In July 2025, Amazon ECS launched built-in blue/green deployments. This allows you to operate directly within the ECS service, without requiring the use of Amazon CodeDeploy.
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.
I once transitioned from a SaaS CTO role to become a business unit CIO at a Fortune 100 enterprise that aimed to bring startup development processes, technology, and culture into the organization. The executives recognized the importance of developing customer-facing applications, game-changing analytics capabilities, and more automated workflows. Let's just say my team and I did a lot of teaching on agile development and nimble architectures.
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.
Docker builds images in layers, caching each one.When you rebuild, Docker reuses unchanged layers to avoid re-executing steps - this is build caching. So the order of your instructions and the size of your build context have huge impact on speed and image size. Here are the quick tips to optimize and achieve 2 times faster speed building images: 1. Place least-changing instructions at the top
If you've ever struggled with running multiple docker run commands for a complex application, Docker Compose is your solution. It's a tool that allows you to define and manage multi-container Docker applications using a single, declarative configuration file. Instead of a long list of commands, you describe all your services, networks, and volumes in a docker-compose.yml file. With one command, you can spin up your entire application stack.
