#command-line

#command-line

In order to use agents or in order to use AI in IT operations, all of your systems need to be interconnected and what interconnects all of your systems is an automation platform. Interconnecting systems is only a piece of the puzzle though. There is also some well-founded concern about the autonomous AI systems we are moving towards. AI agents may make decisions and inferences, but enterprises remain hesitant to allow direct execution on production systems.

The software industry is collectively hallucinating a familiar fantasy. We visited versions of it in the 2000s with offshoring and again in the 2010s with microservices. Each time, the dream was identical: a silver bullet for developer productivity, a lever managers can pull to make delivery faster, cheaper, and better. Today, that lever is generative AI, and the pitch is seductively simple: If shipping is bottlenecked by writing code, and large language models can write code instantly, then using an LLM means velocity should explode.

Dependabot sounded the alarm on a large scale. Thousands of repositories automatically received pull requests and warnings, including a high vulnerability score and signals about possible compatibility issues. According to Valsorda, this shows that the tool mainly checks whether a dependency is present, without analyzing whether the vulnerable code is actually accessible within a project.

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.

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:

Bash scripts are a great way to automate all sorts of repetitive tasks -- you can run backups, clear temporary files/logs, rename or batch-rename files, install or update software, and much more. Although writing such scripts isn't nearly as hard as you might think, it does take some time to learn the ins and outs of bash scripting. Also: 6 hidden Android features that are seriously useful (and how they made my life easier) Good news: If you have an Android device, you can enable the Linux terminal, which means you can create or practice your bash scripting on the go.

For the longest time, Linux was considered to be geared specifically for developers and computer scientists. Modern distributions are far more general purpose now -- but that doesn't mean there aren't certain distros that are also ideal platforms for developers. What makes a distribution right for developers? Although I consider app compatibility, stability, and flexibility to be essential attributes for most any Linux distribution, developers also need the right tools

The reason for this is Snap - a Linux application packaging format - creates a local Trash folder for each VS Code version, one that's separate from the system-managed Trash, according to a VS Code bug report dating back to November 11, 2024. Not only that, but Snap keeps older versions of VS Code after updates, potentially multiplying the number of local Trash folders and the trashed-but-not-deleted files therein. Emptying the system Trash folder doesn't affect the local instances.

Industry professionals are realizing what's coming next, and it's well captured in a recent LinkedIn thread that says AI is moving on from being just a helper to a full-fledged co-developer - generating code, automating testing, managing whole workflows and even taking charge of every part of the CI/CD pipeline. Put simply, AI is transforming DevOps into a living ecosystem, one driven by close collaboration between human judgment and machine intelligence.

One thing I always do when I prompt a coding agent is to tell it to ask me any questions that it might have about what I've asked it to do. (I need to add this to my default system prompt...) And, holy mackerel, if it doesn't ask good questions. It almost always asks me things that I should have thought of myself.

Now available in technical preview on GitHub, the GitHub Copilot SDK lets developers embed the same engine that powers GitHub Copilot CLI into their own apps, making it easier to build agentic workflows. This makes it possible to integrate Copilot into any environment. You can build GUIs that use AI workflows, create personal tools that level up your productivity, or run custom internal agents in your enterprise workflows.