Operations | Monitoring | ITSM | DevOps | Cloud

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.

7 Ways Digital Protection Services Are Safeguarding High-Risk Individuals in 2026

In today's hyper-connected world, personal security no longer begins and ends with physical protection. For executives, entrepreneurs, public figures, journalists, activists, and other high-risk individuals, digital threats have become just as significant as real-world risks. A single exposed piece of personal information can open the door to identity theft, financial fraud, online harassment, reputational damage, or even physical safety concerns.

Which AI-Powered Observability Tools Accelerate Root Cause Analysis (RCA)?

TL;DR Choosing the right AI-powered observability platform isn’t about who has the most AI features. It’s about which platform helps your team identify root causes faster and spend less time investigating incidents. Here’s the short version: Logz.io + OrionIQ: Autonomous AI agents investigate incidents, perform root cause analysis, and surface next steps. Open standards, Kubernetes-ready, and deploys in as little as a week.

Why There's Never Been a Better Time to Leave Heroku (Especially in the AI Era)

Salesforce just put Heroku into maintenance mode and ended Enterprise sales for new customers. The AI era also flipped the migration math: what used to be a six-month project is now a one-prompt, agent-driven move to your own cloud. Here's why now is the best time to leave Heroku. Julien is a Senior Product Manager at Qovery. He bridges engineering and product, writing about deployment workflows, environment management, and DevOps patterns.

How to build sustainable AI infrastructure on GPU cloud

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.