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Your AI Coding Budget Is Becoming a Variable Cloud Bill
Carla Castillo | | agentic coding, AI coding assistants, AI coding costs, AI cost management, AI credits, AI developer tools, AI Governance, cloud economics, cost per developer, developer productivity, devops, engineering spend, FinOps for AI, GitHub Copilot, model mix, platform engineering, premium models, SaaS budgeting, token billing, usage based billingAI coding assistants are becoming a variable, usage-based engineering cost, forcing platform and DevOps teams to apply FinOps practices to models, credits, utilization and multi-vendor spend ...
Is Java Enterprise Ready for AI? Absolutely
AI is transforming software engineering. For enterprise Java developers, the key question is whether Java and Jakarta EE are prepared to integrate AI into enterprise applications. The answer is yes. Java and ...
CI/CD for AI-Enabled Applications: Why Traditional Deployment Pipelines Need to Evolve
Prem Kumar Gadhanki | | artificial intelligence, CI/CD, continuous delivery, devops, MLOps, software deliveryTraditional CI/CD pipelines are optimized around a familiar assumption: source code changes, automated tests validate the change, a build artifact is produced, and the application is promoted through environments. AI-enabled applications complicate ...
The Blind Spot Between Back‐End Metrics and User Experience
Engineering teams often face a frustrating scenario when application performance monitoring (APM) shows perfect Apdex scores and negligible error rates, yet user retention drops due to poor front‐end performance. The solution requires ...
Why DNS, DHCP, and IPAM Can No Longer Live in Separate Silos
Ask a network engineer where their DNS records live, and the answer is rarely straightforward. Some zones reside on Windows DNS servers, others are hosted in cloud-managed services, while DHCP scopes are ...
The AI Agent Race Is On. But Are We Watching the Right Race?
Claude Code, OpenAI Codex, GitHub Copilot, Cursor and a growing field of challengers are competing to define the future of software development. A new Techstrong special report examines who is ahead, how ...
Report Shines Spotlight on 91 Vulnerabilities Fixed in Latest Update to Spring Framework
Mike Vizard | | AI cybersecurity, AI vulnerabilities, application security, Broadcom, CVEs, devsecops, Java security, open source security, open source vulnerabilities, patch management, security automation, software components, Software Supply Chain, sonatype, Spring framework, Spring vulnerabilities, technical debt, virtual patching, vulnerability managementSonatype says 91 Spring vulnerabilities affecting more than 209,000 software components highlight how AI is accelerating vulnerability discovery and creating a new patching challenge for DevSecOps teams ...
Why “Tokenmaxxing” Was Always the Wrong Way for Developers to Measure AI Productivity
Drew Thompson | | ai, AI Development, AI security, application security, developer productivity, software developmentThe term "tokenmaxxing" left the developer lexicon just as quickly as it arrived, and like most viral technology concepts, it means different things depending on who's using it. In practice, the term ...
Ten Great DevOps Job Opportunities
DevOps.com is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience. Our goal in these ...
Beyond Log Search: What We Learned Building a RAG-Based Incident Diagnosis System
Abhimanyu Garg | | AI for SRE, AI hallucinations, AI incident diagnosis, AIOps, devops, devops automation, FAISS, incident management, incident remediation, incident response, knowledge management, knowledge retrieval, LLM grounding, LLM incident response, observability, postmortems, production AI, RAG AIOps, re-ranking, retrieval-augmented generation, root cause analysis, Runbooks, semantic search, SREA RAG-based AIOps framework can cut incident diagnosis time by grounding LLM reasoning in real runbooks, tickets and postmortems, improving root-cause accuracy while giving SREs source-backed answers they can trust ...
Scalable Jenkins Management: Empowering Enterprises With Centralized Control for 150+ Instances
Thrivikram Eskala | | ansible, ArgoCD, AWS, azure, CI/CD Automation, developer platform, devops automation, ELK stack, gitops, grafana, Helm, Jenkins, Jenkins administration, Jenkins backups, Jenkins control plane, Jenkins fleet, Jenkins management, Jenkins observability, Jenkins rollback, Jenkins upgrades, kubernetes, multi-cloud CI/CD, platform engineering, Prometheus, self-service platformA centralized Jenkins control plane can reduce fleet sprawl, automate upgrades and backups, improve observability, and give application teams safe self-service across large multi-cloud CI/CD estates ...
Preparing Infrastructure for the Next Phase of Agentic AI
Gretchen Stewart | | agent orchestration, agentic AI, AI agents, AI data architecture, AI infrastructure, AI modernization, AI observability, AI resource management, AI workflows, AI workload management, autonomous agents, cloud infrastructure, data movement, federal agencies, government AI, government IT, human-in-the-loop, hybrid infrastructure, infrastructure modernization, multi-agent systems, on-premises AI, public sector AIAgentic AI is changing government infrastructure requirements, pushing agencies to rethink workflows, observability, data movement and resource prioritization before investing in new hardware ...
