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Most read news & articles within last 7 days
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 ...
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 ...
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 ...
Tricentis Preps Wave of Additional AI Testing Capabilities
Tricentis is providing early access to multiple artificial intelligence (AI) capabilities that it is gearing up to roll out later this year via a Tricentis Transform initiative, including an autonomous AI agent, ...
Sonar AI Agent Discovers Vulnerabilities Hidden in Business Logic Workflows
Sonar today made available an artificial intelligence (AI) agent designed to discover vulnerabilities and business logic flaws that pose the greatest risk to an organization should they be exploited. The SonarQube Hunter ...
Harness Unfurls Source Code Repository Alternative to GitHub
Harness today launched a code repository service that is specifically designed for DevOps teams that are relying on artificial intelligence (AI) agents to generate code. Martin Reynolds, Field CTO for Harness, said ...
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 ...
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 ...
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 ...
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 ...
