#github

#github

The Swift for Visual Studio Code extension adds first-class language support for projects built with Swift Package Manager, enabling seamless cross-platform development on macOS, Linux, and Windows.

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.

No matter how inevitable the AI-takes-all scenario may sound, as long as there is a person in the world who still wants to own their means of computation, we will be here to build the hardware that enables it.

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.

Akismet launched in 2005 as a web service to classify user-generated content as spam or not, quickly gaining popularity among bloggers facing spam issues.

Meta is working on two proprietary frontier models: Avocado, a large language model, and Mango, a multimedia file generator. The open-source variants are expected to be made available at a later date.

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.

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.

GitHub engineers recently traced user reports of unexpected "Too Many Requests" errors to abuse-mitigation rules that had accidentally remained active long after the incidents that prompted them. According to GitHub, the affected users were not generating high-volume traffic; they were "making a handful of normal requests" that still tripped protections. The investigation found that older incident rules were based on traffic patterns that were strongly associated with abuse at the time, but later began matching some legitimate, logged-out requests.

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.

GitHub is exploring what already seems like a controversial idea that would allow maintainers of repositories or projects to delete pull requests (PRs) or turn off the ability to receive pull requests as a way to address an influx of low-quality, often AI-generated contributions that many open-source projects are struggling to manage.

"We've been hearing from you that you're dedicating substantial time to reviewing contributions that do not meet project quality standards for a number of reasons - they fail to follow project guidelines, are frequently abandoned shortly after submission, and are often AI-generated," Moraes wrote. "As AI continues to reshape software development workflows and the nature of open source collaboration, I want you to know that we are actively investigating this problem and developing both immediate and longer-term strategic solutions."

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.

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.