#progression-lock

#progression-lock

Microsoft did not send me any emails or prior warnings. I have received no explanation for the termination and their message indicates that no appeal is possible. I have tried to contact Microsoft through various channels but I have only received automated replies and bots. I was unable to reach a human.

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.

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.

After streamlining our development and delivery process, we'll ship a new Stable release every week. Endgame will now be folded into our weekly activities. This acceleration is enabled by AI automation, including a one-click experience for creating test plans from feature request issues, reducing manual steps previously required.

LinkedIn has redesigned its static application security testing pipeline (SAST) to provide consistent, enforceable code scanning across a GitHub-based, multi-repository development environment. The initiative was a result of the company's shift-left strategy by delivering fast, reliable, and actionable security feedback directly in pull requests, strengthening the security of LinkedIn's code and infrastructure and helping protect members and customers.

The real cost of poor observability isn't just downtime; it's lost trust, wasted engineering hours, and the strain of constant firefighting. But most teams are still working across fragmented monitoring tools, juggling endless alerts, dashboards, and escalation systems that barely talk to one another, which acts like chaos disguised as control. The result is alert storms without context, slow incident response times, and engineers burned out from reacting instead of improving.

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.

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.

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.

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.