Operations | Monitoring | ITSM | DevOps | Cloud

The AI bill arrived. Now what?

There was a time when “Opus” meant a classical composition and “Sonnet” was fourteen lines of Shakespeare you definitely did not read before the test. Now they’re model tiers, and every new release rewrites the economics of your engineering org whether you’re ready or not. Currently, your monthly total hides the crucial information you need to control and justify AI spend.

How Coding Agents are Changing the Traditional Software Development Lifecycle

AI coding assistants are rapidly evolving from passive copilots into active, agentic collaborators capable of planning, executing, and iterating on complex software tasks. This shift has huge ramifications onthe software development lifecycle (SDLC), developer productivity, and even the structure of engineering teams.

Progressing AI Beyond Scaling and Into Deep Reasoning

The breakthroughs in AI today aren’t just coming from bigger datasets and more compute; Reinforcement Learning (RL) has quietly become one of the most powerful forces in modern AI development. RL is teaching models to reason and self-correct, enabling capabilities that make AGI feel less like science fiction and more like an inevitable future.

How to track business expenses in 2026: methods, tools, and AI spend

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.

How AI Is Transforming Production Issue Investigation for Modern DevOps Teams?

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.

AI Coding Security Risks Demand Dependency Firewalls | Harness Blog

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.

An introduction to Zebra's AI for the Frontline | Zebra

Zebra Technologies is at the forefront of AI innovation for frontline workers. In this video Daniel Park discusses how Zebra is integrating AI across devices and tools to guide employees to the "next best action" within their workflows, improving real-time efficiency and decision-making on the frontline. We explore how our brand-new fleet of mobile computers—including the TC501 and TC701—are designed from the ground up for on-device AI. Equipped with advanced memory and dedicated Neural Processing Units (NPUs), these devices process data locally at lightning-fast speeds.

Zebra AI for the Frontline: Core Components | Zebra

Zebra Technologies is at the forefront of edge artificial intelligence, built specifically to empower those on the frontline. In this video, we break down the three core components of Zebra's Frontline AI solution and explore how they accelerate development, streamline specific workflows, and assist workers in real time. We examine the three pillars of our AI architecture.