Operations | Monitoring | ITSM | DevOps | Cloud

In the traditional automotive world, teams often work in silos: the cybersecurity experts lock down the ports, the quality assurance teams hunt for bugs, and the functional safety engineers track the ISO 26262 compliance. At Canonical, we believe this fragmented workflow causes friction rather than collaboration. You cannot have a safe vehicle that isn’t secure, and you cannot have a secure vehicle running on poor quality code. This friction results in a slow and rigid development process.

This minor update has a one big new feature.

The controls at the top of Explorer are great for a simple question. But as your query grows with more group-bys or a stack of filters, those controls start eating into the vertical space you actually want for your data. Now you have the option to move filters and groupings into a dedicated left side panel, so a complex query has room to scale cleanly. Set it once and CloudZero keeps it that way.

GPT-4 API pricing spans $0.10 to $30.00 per million input tokens across the model family. GPT-4.1 is the current recommended production model at $2.00 input / $8.00 output per million tokens. Legacy GPT-4 still runs at $30.00/$60.00 per million tokens -- 15x more expensive for no meaningful quality gain. For finance and engineering leaders accountable for AI spend, choosing the right GPT-4 variant is the single biggest cost lever on your bill.

A message broker's backup and disaster recovery plan is the last line of defense against scenarios that HA cannot address: a full datacenter outage, catastrophic hardware failure that destroys both primary and secondary nodes, accidental message deletion, or KahaDB corruption that prevents the broker from starting.

The JVM is the runtime foundation of every ActiveMQ deployment. Message throughput, delivery latency, producer flow control triggers, OOM crashes, and GC-induced delivery pauses all trace back to JVM memory configuration. Yet ActiveMQ ships with a 512MB heap and no GC logging, appropriate for a developer laptop, not for an enterprise message broker handling millions of messages a day.

"Hello world, this is your agent speaking!" The agent loop! The LLM is calling tools, the answers are sensible, and the sky's the limit. Now, as you look forward to production, you look for a composable toolset, something that can grow with your use case and system needs. That's what we created with Honeycomb Canvas: a collaborative investigation space where AI agents help you understand, fix, and learn about your system.

Incident response often depends on connecting two kinds of context: what changed in the environment and what the logs say happened next. Through a new integration with Elastic, Anyshift’s AI agent, Annie, can read from a customer’s Elasticsearch deployment to search logs, surface error and warning spikes, and correlate log evidence with infrastructure change history.

Apple’s platform ecosystem is evolving as developers build production applications for watchOS and visionOS. Whether it’s a fitness app on Apple Watch or an immersive spatial computing experience on Apple Vision Pro, these platforms have moved beyond the experimental phase to support real users. Despite this growth in adoption, teams lack visibility into how their apps behave on these devices.

VMware vSphere is the platform that unifies ESXi and vCenter into a complete solution for running and managing virtual machines. VMware vSphere is not a single product, but a full virtualization platform and product suite that includes ESXi, vCenter, and other tools for managing workloads. It provides the foundation for running virtual machines (VMs) on a hypervisor and gives IT teams the ability to centralize management across multiple servers, clusters, and applications.