Operations | Monitoring | ITSM | DevOps | Cloud

AI coding assistants accelerate development but can rapidly introduce vulnerable, malicious, or non-compliant open-source dependencies into your codebase. Harness Artifact Registry's Dependency Firewall acts as a registry-level control point, evaluating and blocking risky external packages before they enter your CI/CD pipeline—essential protection against modern npm-style supply chain attacks.

The Harness MCP Server now connects directly inside Google Antigravity. Developers can link Harness in under two minutes and give the agent structured, real-time access to their pipelines, execution history, services, environments, and policies, without leaving the editor. What makes it reliable isn't the connection itself. It's the Harness Software Delivery Knowledge Graph underneath, which gives the agent the context to act accurately, fast, and within your guardrails. ‍

Production failures don't announce themselves cleanly. They arrive at 2 AM, buried inside 40 million log lines, spread across a dozen microservices, and disguised as something that looks entirely unrelated to the actual root cause. For years, engineering teams absorbed this pain through process: runbooks, on-call rotations, dashboards, and a deep institutional knowledge that lived in the heads of their most senior engineers.

How to track expenses for a business: categorize expense types (operating, software, cloud, travel, capital), choose a tracking method (spreadsheet, accounting software, expense management tool, or cost intelligence platform), connect data sources (bank feeds, cloud billing APIs, SaaS invoices), assign ownership per cost center, set a reporting schedule, and audit quarterly.

CloudZero now splits an Amazon Bedrock line item into spend by model and by the team, user, or project behind each call, straight from your AWS bill.

Last Christmas, after everyone had gone quiet for the holidays, I sat down with a pen and some paper and started drawing Spike. Not the Spike we actually had, but the Spike I wanted, the one I had been carrying around in my head for a long time without ever really putting it down anywhere. A little while later I brought a few of those screens into Figma and showed them to the team over coffee one afternoon.

Upsun is included in IDC ProductScape guide to worldwide cloud deployment-centric platform capabilities. Building and scaling applications has never been more complex or more critical. Engineering teams are under constant pressure to ship faster, manage increasingly complex infrastructure, and adapt to the rapid rise of agent and AI-powered development. Choosing the right platform to support these demands is an important decision for technology organizations.

Most hybrid cloud strategies fail not at launch but at scale. The architecture works fine for the first year. The team's workloads are modest, the integration points are limited, and the operational overhead is manageable. Then the business grows. Workloads multiply, data volumes climb, the team expands, and the seams between public cloud and private infrastructure start showing.

AI's environmental cost is real, and it's growing. Training a large language model can consume the electricity of hundreds of households for weeks. Inference at production scale runs continuously, with GPU clusters drawing power around the clock. The data centers that house all of this are some of the most concentrated energy consumers in the modern technology stack.