Social – X
DevOps for GenAI Toronto Edition Hackathon: Unlocking New AI Market Opportunities
Toronto’s vibrant tech community is abuzz as the DevOps for GenAI Hackathon returns with John Willis, this time with ambitious new themes that reflect both global trends and uniquely Canadian innovation. Building ...
Google Code Wiki Aims to Solve Documentation’s Oldest Problem
Tom Smith | | AI code analysis, AI documentation tools, automated architecture diagrams, automated code documentation, code repository documentation, codebase understanding, developer productivity tools, legacy code maintenance, onboarding developers faster, software architecture diagramsGoogle introduces Code Wiki, an AI-powered platform that automatically generates and maintains documentation for code repositories. With Gemini integration, live diagrams, and interactive code-aware chat, Code Wiki aims to reduce onboarding time, ...
Five 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 ...
Survey Sees AI Coding Creating Need for More Software Engineers
Mike Vizard | | agentic AI, AI compliance challenges, AI in DevOps, AI in software development, AI tools adoption, AI-generated code vulnerabilities, developer upskilling, DevOps productivity, DevSecOps challenges, GitLab DevSecOps survey, platform engineering, SDLC automation, software engineer demand, technical debt from AI, vibecoding risksA GitLab survey of 3,266 DevSecOps professionals shows AI is boosting code creation but increasing the need for skilled engineers, compliance challenges and human oversight ...
How Hyperconnected AI Development Creates a Multi-System Secret Sprawl
Guillaume Valadon | | AI development security, AI secret leaks, API key sprawl, Claude Code security, Cursor IDE risks, database credential leaks, GitHub secret exposure, hyperconnected AI workflows, large language model security, leaked API keys, MCP configuration files, MCP security risks, mcp.json leaks, OWASP AI testing, secret scanning toolsIf you've been building artificial intelligence (AI) tools lately, you've probably noticed something: Your development workflow has become incredibly connected. Tools such as model context protocol (MCP) sit at the center of it all, acting as the ‘brain’ that orchestrates how your large language ...
GitOps in the Wild: Scaling Continuous Delivery in Hybrid Cloud Environments
Ankur Mahida | | continuous delivery, devops automation, drift detection, GitOps architecture, GitOps at scale, GitOps best practices, GitOps case study, GitOps challenges, GitOps governance, GitOps observability, GitOps security, hybrid cloud GitOps, hybrid cloud operations, infrastructure as code, Kubernetes delivery, multi-cloud DevOps, Open Policy Agent, policy as codeA deep dive into scaling GitOps for hybrid and multi-cloud environments, exploring architecture, governance, security, observability and real-world enterprise practices ...
Chronosphere Adds AI Remediation Guidance to Observability Platform
Chronosphere this week previewed artificial intelligence (AI) capabilities that are embedded into its observability platform that, in addition to helping identify the root cause of an issue, also provides remediation suggestions. Additionally, ...
ClickOps, IaC and the Excluded Avocado Middle
Let’s start with a confession: I think Infrastructure-as-Code solves a lot of problems. Terraform, OpenTofu, Pulumi, all of it. IaC changed how we work. It gave us version control, audit trails, repeatability, ...
AI-Driven Performance Testing: A New Era for Software Quality
Akash Thakur | | AI agents, AI defect detection, AI in DevOps, AI load testing, AI observability, AI performance testing, AI testing tools, continuous assurance, intelligent performance systems, LLMs in testing, ML-driven QA, next-gen performance testing, performance automation, performance engineering, performance engineers, performance lifecycle, performance monitoring, performance optimization, predictive performance analysis, predictive testing, requirements as code, self-healing systems, software reliability, SRE automation, testing transformationDiscover how AI and large language models (LLMs) are revolutionizing performance testing—shifting from reactive load testing to predictive, continuous assurance powered by intelligent agents and automation ...
Why Traditional SLOs Are Failing at Hyperscale: Building Context-Aware Reliability Contracts
Muhammad Yawar Malik | | adaptive reliability, AI-driven scaling, business-aligned reliability, context classification, context propagation, context-aware reliability, context-driven SLOs, cost reduction, devops, dynamic SLOs, gitops, hyperscale reliability, infrastructure optimization, multi-service reliability, observability, performance optimization, post-mortem analysis, predictive scaling, reliability budgets, reliability contracts, reliability engineering, reliability marketplace, site reliability engineering, SLA management, SLO optimization, SREDiscover how context-aware reliability contracts (CARC) redefine SLOs for hyperscale systems—optimizing uptime, reducing infrastructure spend by 33%, and aligning reliability with business value across user tiers, regions, and workloads ...
The Future of Observability: Predictive Root Cause Analysis Using AI
Karthikeyan Uthirapathy | | AI in DevOps, AI observability, AI-driven RCA, AI-powered monitoring, anomaly correlation, anomaly detection, artificial intelligence, automated incident response, cloud environments, devops, distributed systems, intelligent observability, kubernetes, logs metrics traces, machine learning, microservices, observability tools, Predictive Analytics, predictive monitoring, predictive root cause analysis, proactive observability, rca, self-healing systems, system reliabilityIn the past few years, systems have become more complex than ever. Microservices, Kubernetes, cloud environments and distributed application programming interfaces (APIs) have changed how we build and manage software. However, this complexity has also made it harder ...
JFrog Adds Ability to Track Usage of AI Coding Tools
Mike Vizard | | AI API management, AI code auditing, AI coding tools, AI compliance tracking, AI Governance, AI in DevOps, AI model inventory, AI risk mitigation, AI-generated code, application security, code risk detection, devsecops, Large Language Models, LLM security, MLOps, SCA, secure coding practices, Shadow AI detection, software composition analysis, software provenance, Software Supply Chain Security, software vulnerabilities, source code scanningJFrog introduces AI-Generated Code Detection and Shadow AI Detection tools to identify AI-created code, track model usage, and enhance DevSecOps governance across software supply chains ...
