News and Tech Update

Internal developer platforms have become a tangled web of orchestration tools, CI runners and deployment systems that rarely speak the same language. Every new integration adds another translation layer, and as AI-driven automation starts to plug into those pipelines, the lack of a shared vocabulary for what is actually happening across the software delivery lifecycle becomes a real bottleneck. Without a consistent way to describe build, test and deploy events, both humans and agents are left stitching together logs and webhooks from systems that were never designed to interoperate. Dadisi Sanyika of the Continuous Delivery Foundation sat down with Mike Vizard to walk through how CDEvents is tackling that fragmentation. CDEvents defines a common event specification — essentially a standardized set of receipts that tools like Jenkins, Tekton and other delivery systems can emit as work moves through the pipeline. That shared semantic layer gives platform teams a reliable way to wire h...

Imagine handing the same master key to every contractor who works on your building. No names, no records, no way to know who came and went. If the key gets copied, passed around or lost, you’d have no idea. You’d only find out something went wrong after the damage had been done.   That’s essentially what API keys do for your AI agents , and for prototypes, that’s fine.    However, the moment your agent moves into production, accessing real data, taking real actions and operating inside real systems, that master key becomes a liability you can’t afford.   The Risks and Benefits of API Keys   Developers are under a huge amount of pressure to build faster. Every organization wants to benefit from agentic AI, and devs play an integral role in making that happen.   Given this, it’s easy to see the appeal of API keys: They’re simple to use and can get you to a proof of concept almost instantly. The problem is that they’re severely lacking from a security standpoint.   API keys work by ...

Turning continuous integration and continuous delivery (CI/CD) pipelines into emissions‑controlled workloads is a strategy businesses adopt to reduce the carbon footprint of their pipelines. This article examines how a Carbon-Aware DevOps strategy can help optimize CI/CD pipelines for sustainability by reducing their carbon footprint while also decreasing costs and meeting the increasing demand for innovative, eco-friendly solutions.   Understanding the Problem   Today’s organizations take advantage of CI/CD pipelines to streamline and accelerate their software delivery. However, these CI/CD pipelines can become a hindrance to sustainability because of the resources required to create, build, test and ultimately deploy software into production.   Typically, CI/CD pipelines run tens of thousands of automated builds, tests and deployments daily, thereby consuming significant compute and energy resources. If you don’t take the steps necessary to reduce the emissions generated by these ...

GitLab today, at its Transcend 2026 conference, launched a bevy of updates that collectively optimize its integrated DevOps platform for the volume of code being generated by artificial intelligence (AI) coding tools. At the core of that effort is a Next Generation Source Code Management, a re-vamped implementation of the Git protocol that is based on a distributed architecture that makes it possible to scale DevOps workflows in a way that enables AI agents to complete tasks 50 times faster Manav Khurana, chief product and marketing officer for GitLab, said this implementation of the Git protocol takes advantage of distributed processing and storage engines to meet the requirements of DevOps workflows that are increasingly being driven by AI coding tools and associated agents. Additionally, GitLab is also now able to limit server-side queries to only what the actual task requires. At the same time, GitLab is adding a context graph, dubbed GitLab Orbit, that maps code, work items, p...

Why micro teams and rotation reshape culture, not just throughput, in modern SRE. Most SRE leaders design teams around the systems they own. We designed ours around movement. We introduced micro teams expecting a throughput story: smaller groups, tighter scope, faster work. Some of that arrived. What we had not budgeted for was how much it changed the way people worked with each other. We are a 34-person group running four enterprise platforms across two continents, and at that size the thing you fight is fragmentation. People settle into corners: a database specialist who has only ever touched one product, a platform engineer who hasn’t talked to the other side of the stack in months. Knowledge turns territorial, and the team becomes a set of people who happen to share a Jira board. Micro teams broke that through proximity, not policy. What a Micro Team Actually Is A micro tea›m is a small cell pulled together for a fixed window around one outcome. Not a permanent team, and ...

Microsoft released Visual Studio Code 1.123 on June 3, expanding its AI-assisted development capabilities with features that reflect the editor’s direction: toward a more agentic, context-aware workflow. The update is relatively focused, but several additions stand out to developers who rely on Copilot and other AI models day to day. A Million-Token Context Window One of the headline changes is support for 1-million-token context windows for compatible models from Anthropic and OpenAI, including Claude Opus 4.7 and GPT-5.5. That’s a meaningful jump. Larger context lets you work with larger codebases and longer conversations without the model losing track of what came before. There’s a trade-off. Microsoft notes that larger context windows consume more tokens per interaction, which matters for teams on usage-based billing. But for developers working on complex projects, the ability to keep more code in context without manual truncation is a practical productivity ga...