DevClass

Arm, logo Discussions on AI infrastructure performance tend to focus on accelerators: tensor cores, GPU counts, and peak FLOPS. Those metrics matter. But in production environments, accelerator throughput rarely operates in isolation. Data needs to be ingested, staged, transformed, secured, scheduled, and moved across memory and network fabrics before a single training job completes.

How Arm is redefining cloud performance — and how developers are already building on it If you thought that Arm was just for mobile applications, it’s time to take another look. The power-efficient, performant architecture that led the evolution of mobile devices is driving a strategic shift in the cloud, and processors built around the Arm Neoverse platform are taking on some seriously heavy workloads for all major cloud hyperscalers.

CloudBees has opened up a preview of an MCP Server for its nascent Unify platform to allow it to manage the AI agents that are working their way into DevOps workflows. CloudBees’ Unify was only launched last month, as a platform for managing multiple DevOps platforms and tool chains, such as CloudBees-backed Jenkins and other CI/CD environments, as well as Git-based platforms such as GitHub and GitLab.

AI accounts for over half of the code produced in some organizations, but much of this going into production with little to no oversight. According to Cloudsmith’s Artifact Management Report 2025, “AI is now writing code at scale.” Of those developers using AI, 42 percent said at least half their code is AI generated. Breaking those numbers down further, 16.6 percent attributed the majority of their code to AI, and 3.6 percent said all their code was machine generated.

The Cloud Native Computing Foundation is scrambling to put in place a new collaboration platform for its vast community after Salesforce unexpectedly pulled the plug on its free enterprise Slack account.

Sysdig researchers have warned that developers and maintainers could be leaving their repos open to hijacking through inadequately secured workflows on GitHub. Researchers at the container and cloud native security firm highlighted potential abuse of the pull_request_target trigger event in GitHub Actions.

Now AI agents have identity, too. Here’s how to handle it The rise of agentic AI systems is rewriting the rules of cybersecurity. Unlike generative AI, which relies on predefined instructions or prompts, AI agents operate autonomously, learn continuously, and act with minimal oversight. They collaborate across systems and adapt to dynamic environments. As enterprises scale their AI deployments, identity security must evolve in lockstep to preserve control, mitigate risk, and enforce trust.

GitLab says it is on track to launch its agentic AI iteration of the Duo assistance technology this winter. CEO William Staples gave the update on GitLab Duo Workflow during its first quarter earnings call this week. The firm took its first run at incorporating AI into its platform in 2023, when it unwrapped Duo.

Databricks kicked off its user conference this week with a slew of announcements that put (agentic) AI at the center of its strategy, and fleshed out its plans for Postgres following its recent $1 billion purchase of Neon. On the agentic AI front, it announced a public preview of Lakebase, a “fully managed Postgres database” built for AI, and itself built on the technology it acquired with its Neon buy. Databricks said the approach brings “operational data to the Lakehouse”.

New Relic has added Model Context Protocol support to its existing AI monitoring portfolio, giving AI teams at least some insight into interactions between their applications and language model agents. MCP was only open sourced by Anthropic last year.