AI
Eclipse Foundation Extends Scope and Reach of Open VSX Registry
Mike Vizard | | AI coding tools, application development, AWS, developer tools, digital sovereignty, encryption, exposed credentials, extension name spoofing, funding model, malicious pattern detection, multi-region architecture, namespace impersonation, rate limiting, security infrastructure, software security, Software Supply Chain, trusted environments.The Eclipse Foundation launches a new framework for the Open VSX Registry, enhancing security features and transitioning to a hybrid architecture. With support from AI tool provider Cursor, this initiative aims to ...
Context Engineering is the Key to Unlocking AI Agents in DevOps
Abhijit Ubale | | AI agents, automation, context engineering, context management, data governance, devops, incident management, kubernetes, memory architectures., Model Context Protocol, observability, operational complexity, performance optimization, production-grade agents, Productivity, scaling AI, selective context injection, structured memory, tool integration, workflow efficiencyExplore how context engineering is essential for transforming AI agents from experimental prototypes to reliable production tools in DevOps. Understand its impact on automation workflows, accuracy, and scalability ...
Microsoft Executives Warn AI Could Limit the Developer Talent Pipeline
Generative AI is transforming software development at remarkable speed. But in a new paper written by two Microsoft executives, Azure CTO Mark Russinovich and Scott Hanselman, VP of the developer community, argue ...
Google ADK Opens the Door to AI Agents That Work Inside Your DevOps Toolchain
Tom Smith | | Agile workflows, AI agents, AI-Driven Development, AI/ML ecosystem, CI/CD, code management, database queries, devops, engineering workflows, GitHub integration, Google ADK, MPL-based integrations, observability, observability-native frameworks, Pinecone, project management tools, software development, system performance, telemetery, third-party integrations, workflow automationGoogle ADK adds integrations for GitHub, GitLab, Jira, MongoDB, and seven observability tools. AI agents can now work inside your DevOps toolchain ...
AI-Fueled Development Pushes Open-Source Risk to Extremes: Report
Artificial intelligence has shortened the timeline for software development from months to days. But according to new research, that acceleration is creating significant risks for security and compliance issues. Black Duck’s 2026 ...
Security Flaws in Anthropic’s Claude Code Risk Stolen Data, System Takeover
Jeff Burt | | AI API Security, AI coding risks, AI coding tools, AI developer tools, Anthropic Claude Code, Check Point, MCP protocol, Palo Alto Unit 42Three critical vulnerabilities found in Anthropic’s Claude Code agentic AI developer tool could be exploited simply by cloning and opening an untrusted project and lead to system takeover, stolen API keys, and ...
The Future of AI in Software Quality: How Autonomous Platforms are Transforming DevOps
Igor Kirilenko | | agentic AI, CI/CD pipelines, devops automation, software quality, static analysisAgentic AI is transforming DevOps by embedding autonomous quality checks into CI/CD pipelines, improving code security, coverage and speed ...
AWS Extends Agentic AI Capabilities of Kiro Developer Tool to Improve Code Quality
Amazon Web Services (AWS) has added two additional capabilities to its Kiro artificial intelligence (AI) coding tool that promise to make it simpler to both create higher quality software in the first ...
Survey: Adoption of AI Software Testing Slowed by Trust Issues
A new global survey from Leapwork underscores a growing tension in software development: while AI is widely viewed as essential to the future of testing, many teams remain hesitant to rely on it for ...
AWS Just Turned “Deploy to AWS” Into a Five-Word Prompt
AWS Agent Plugins give coding agents the skills to architect, estimate costs, and deploy to AWS. Type "deploy to AWS" and the agent handles the rest ...
Why AI Makes Requirements a Runtime Artifact
Tom Hollowell | | AI behavior, AI Governance, AI requirements, continuous verification, MLOps, model drift, production monitoring, requirements traceability, runtime constraintsIn traditional software, requirements are static design-time artifacts. In AI-enabled systems, they must be continuously observed and enforced in production. Learn how AI collapses the boundary between design-time and runtime, shifting requirements ...
What to do About AI’s Forced Rethink of Reliability in Modern DevOps
Leo Vasiliou | | Agentic Workflows, AI monitoring, AI observability, AI reliability, AI. analytics, business impact, CDNs, customer churn, devops, digital experience, experience-driven resilience, incident response, internet stack, KPIs, latency, LLM applications, model drift, monitoring tools, NPS, observability, performance, performance degradation, reliability, reliability strategy, revenue, SaaS, site reliability engineering, SLIs, SLOs, SRE Report 2026, Third-party Dependencies, trust, uptime, user experienceAs systems become more distributed and AI-driven, traditional uptime metrics are no longer enough. The 2026 SRE Report shows how reliability is shifting toward user experience, speed, and business impact, and how ...
