#github-cli

#github-cli

The most dangerous assumption in quality engineering right now is that you can validate an autonomous testing agent the same way you validated a deterministic application. When your systems can reason, adapt, and make decisions on their own, that linear validation model collapses.

Agentic browser tools, an experimental feature, provides agents with a set of tools to read and interact with VS Code's integrated browser. When an agent interacts with a web page, it sees updates to page content and any warnings and errors in the console. The tools work out of the box with no need to install extra dependencies.

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.

Over the past decade, software development has been shaped by two closely related transformations. One is the rise of devops and continuous integration and continuous delivery (CI/CD), which brought development and operations teams together around automated, incremental software delivery. The other is the shift from monolithic applications to distributed, cloud-native systems built from microservices and containers, typically managed by orchestration platforms such as Kubernetes.

Central to the GA release is Agentic Chat. This functionality builds on the previously introduced Duo Chat but goes a step further by leveraging context from virtually every part of GitLab. Think of issues, merge requests, CI/CD pipelines, and security findings. Agentic Chat can not only advise, but also actually perform actions on behalf of developers, depending on the rights and approvals that have been set.

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.

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

DBmaestro is a database release automation solution that can blend the database delivery process seamlessly into your current DevOps ecosystem with minimal fuss, and without complex installation or maintenance. Its handy database pipeline builder allows you to package, verify, and deploy, and gives you the ability to pre-run the next release in a provisional environment to detect errors early. You get a zero-friction pipeline, which is often not the case with database delivery process.

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.

Hold on. There's yet another method, one that comes from MacOS. That method is Homebrew. What is Homebrew? Homebrew is a free, open-source package manager for Linux and MacOS that simplifies the installation and management of software. Think of Homebrew as a command-line version of the App Store that allows you to install command-line tools such as Python, Node.js, and more with ease.

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.

Software development used to be simpler, with fewer choices about which platforms and languages to learn. You were either a Java, .NET, or LAMP developer. You focused on AWS, Azure, or Google Cloud. Full-stack developers learned the intricacies of selected JavaScript frameworks, relational databases, and CI/CD tools. In the best of times, developers advanced their technology skills with their employer's funding and time to experiment. They attended conferences, took courses, and learned the low-code development platforms their employers invested in.

Software engineering didn't adopt AI agents faster because engineers are more adventurous, or the use case was better. They adopted them more quickly because they already had Git. Long before AI arrived, software development had normalized version control, branching, structured approvals, reproducibility, and diff-based accountability. These weren't conveniences. They were the infrastructure that made collaboration possible. When AI agents appeared, they fit naturally into a discipline that already knew how to absorb change without losing control.