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The Green Side of Observability: Why Less Data Can Mean More Insight
Observability can generate massive data volumes. Learn how sustainable observability reduces telemetry waste, lowers costs and improves insight ...
Five Great DevOps Job Opportunities
Mike Vizard | | career advancement, Cloud Security, devops engineer, devops jobs, hiring trends, job roundup, remote-friendly, Salaries, senior roles, SRE, tech hiring, weekly jobs reportWeekly DevOps jobs roundup, this week highlighting top roles in Massachusetts, New Jersey, Chicago, Charlotte and Seattle, with pay ranges and hiring trends to help DevOps pros advance careers ...
Survey: AI Coding Exacerbates Existing DevOps Workflow Issues
A global survey of 700 software engineering practices published this week finds that thanks to increased reliance on artificial intelligence (AI) coding tools, well over a third (35%) are either achieving daily ...
Low-Code’s New Frontier: Tailored Solutions for Each Industry
JJ McGuigan | | application development platforms, DevOps pipelines, digital transformation, enterprise devops, low-code development, regulated industriesFor years, most low-code platforms have focused on one primary challenge: efficiency. The goal was to help teams build applications faster and with less effort, reducing manual coding, speeding up iterations, empowering ...
The Risk Profile of AI-Driven Development
Ilkka Turunen | | AI-generated code, AIBOM, autonomous development, autonomous security, challenge bottleneck, CI CD gating, cloud-native security, Dependency Management, GRC engineering, hallucinations, IDE controls, license compliance, machine speed enforcement, OpenSSF Gemara, Outdated Dependencies, policy based dependency selection, prompt level governance, review bottleneck, runtime transparency, SBoM, secure by default, shift left security, Software Supply Chain, threat modeling, transitive dependencies, trusted registries, typosquattingAnalysis arguing that AI-driven code generation accelerates dependency decisions and expands supply-chain risk, requiring shift-left governance, prompt-level controls, automated SBOM/AIBOM visibility, threat-modeling as engineering, and autonomous security to match autonomous development ...
How eBPF and OpenTelemetry Have Simplified the Observability Function
Morgan McLean | | auto instrumentation, ebpf, endpoint visibility, Grafana Labs, instrumentation, kernel level, kubernetes, legacy applications, linux, metrics, monitoring, network observability, OBI, observability, observability adoption, observability practice., OpenTelemetry, OpenTelemetry Injector, RED metrics, SDKs, security concerns, Splunk, supply chain, telemetry, third party software, traces, vendor neutral, zero code instrumentationOverview arguing that OpenTelemetry eBPF Instrumentation (OBI) — combined with OpenTelemetry Injector — removes barriers to full observability by enabling zero-code, kernel-level telemetry for Kubernetes and Linux environments, solving language, legacy, and ...
Sorry, Charlie, StarKist Wants AI With Good Taste
Alan Shimel | | Accelerate, accountability, AI alignment, AI Governance, behavioral generalization, blameless postmortems, character in AI, constitutional alignment, Culture Change, devops, emergent misalignment, ethical AI, guardrails, incentive design, incentives, insecure code, interdisciplinary alignment, model disposition, model governance, moral behavior, safety engineering, site reliability engineering, training data culture, training pipelines, trustworthiness, virtuesA surprising AI experiment showed that feeding a model sloppy code didn’t just produce bad programming, it produced bad behavior. The result points to something philosophers and DevOps engineers have long understood: ...
Zero Downtime Multicloud Migrations for Observability Control Planes
Deven Chawla | | control planes, distributed systems, multicloud, observability, platform engineeringMost platform teams aren’t deciding whether they’ll run across multiple clouds. They already are, or they’ll be soon. The real question is how to migrate critical systems without turning on-call into a ...
JetBrains Launches Air and Junie CLI to Blend Traditional IDE with AI Agents
JetBrains has launched a new “agentic” tooling stack that pairs a multi‑agent development environment, Air, with a standalone, LLM‑agnostic coding agent, Junie CLI. If you know JetBrains, you probably know it for ...
The Observability Bill is Coming Due – and AI Wrote Most of It
Alan Shimel | | agentic telemetry, AI in development, AIOps, APM (Application Performance Management), CI/CD (Continuous Integration/Continuous Deployment), data management, data quality, devops, instrumentation, Logging Standards, observability, Sawmills, Signal-to-Noise Ratio, software engineering, technology innovation, telemetry management, Telemetry OptimizationObservability has always had a data quality problem. AI coding agents just made it catastrophically worse. Shimmy sits down with the co-founders of Sawmills to dig into why unmanaged telemetry is the ...
Gemini Code Assist Gets Agent Auto-Approve, Inline Diffs, and Custom Commands to Speed Up the Core Coding Loop
Tom Smith | | Agent Mode, AI coding agents, AI-assisted development, Auto Approve, code review, Context Drawer, custom commands, developer productivity, devops, Finish Changes, Gemini Code Assist, Google Gemini, inline diff, IntelliJ, Outlines, software lifecycle, VS CodeGoogle’s latest Gemini Code Assist updates focus on workflow, not hype: Agent Mode with Auto Approve, inline diffs, a Context Drawer, custom commands, Finish Changes, and Outlines streamline how developers review, trust, ...
Microsoft Azure Skills Plugin Gives AI Coding Agents a Playbook for Cloud Deployment
Tom Smith | | agent orchestration, AI coding agents, AI in software delivery, Azure automation, Azure cost optimization, Azure Skills Plugin, Claude Code, cloud deployment, devops, Foundry MCP, GitHub Copilot, guardrails, infrastructure as code, institutional knowledge, MCP Server, Microsoft Azure, platform engineering, VS Code agent pluginsMicrosoft’s new Azure Skills Plugin closes the gap between AI-written code and production deployments by packaging expert Azure knowledge as executable skills, backed by MCP servers for real infrastructure actions and AI ...
