Cloud-Native Development
How Do Cloud-Native Teams Balance Engineering Excellence With Strong Product Branding?
Learn how cloud-native teams can integrate engineering and branding efforts to create high-performing products that resonate with users ...
Lucy Manole | | A/B Testing, agile development, Branding Strategies, cloud-native development, Cross-Functional Teams., Design Systems, Engineering Excellence, incident management, Performance Metrics, Product Branding, Software Scalability, Team Collaboration, Technical Performance, User Engagement, user experience
Kubernetes v1.35 Arrived, Right On Workload-Aware Schedule
Discover the latest enhancements in Kubernetes workload scheduling, including the Workload API and gang scheduling features aimed at optimizing application performance and management ...
Adrian Bridgwater | | autoscaling, cloud-native applications, Dynamic Resource Allocation, Gang Scheduling, kubernetes, Kubernetes v1.35, Multi-Node Scheduling, Opportunistic Batching, Performance Optimization, Pod Management, resource allocation, Scheduling Algorithms., Scheduling Improvements, Scheduling Latency, software engineering, Workload API, Workload Scheduling
Running Kubernetes in Production: Practical Lessons From the Field
Kubernetes has become the de facto platform for running containerized workloads at scale. While spinning up a cluster is relatively straightforward, operating Kubernetes reliably in production is far more challenging. Teams often ...
Building AI Agents Using Open-Source Docker cagent and GitHub Models
Discover how Docker’s open-source cagent framework and GitHub Models simplify AI agent orchestration. Learn to build, package, and share a vendor-neutral podcast-generation AI system with production-grade quality and cost efficiency ...
Naga Santhosh Reddy Vootukuri | | AI agent orchestration, AI agent runtime, AI development workflows, AI vendor lock-in, cagent framework, containerized AI agents, Docker AI framework, Docker cagent, Docker Hub AI agents, GitHub Models, MCP tools, Model Context Protocol, multi-agent AI systems, OpenAI compatible API, podcast generation AI, production AI agents, vendor-neutral AI, YAML-based AI configuration
Kubeflow and TFX: Accelerating Compute Infrastructure with Operational ML
In an era of exponential data growth, global infrastructure needs are undergoing a seismic shift. Enterprises are moving away from static, monolithic systems toward dynamic, intelligent and adaptive architectures. At the heart ...
Docker, Inc. Adds More Than a Thousand Free Hardened Container Images
Docker is releasing more than 1,000 hardened container images under an open source license, aiming to cut vulnerabilities and strengthen software supply chains ...
Flare Finds 10,000 Docker Hub Images Exposing Secrets
Researchers found thousands of Docker images exposing API keys and tokens, revealing how secrets sprawl, shadow IT, and poor hygiene fuel modern breaches ...
Solo Gets Stickier on Gloo Mesh for Amazon ECS
Solo.io, Gloo Mesh, Gloo Gateway, Amazon ECS, ECS service mesh, Istio Ambient Mode, sidecar-less service mesh, API management, microservices networking, cloud-native security, cloud observability, zero-trust policies, traffic management, App Mesh deprecation, ECS ...
AWS Lambda Managed Instances Offer Specialized Compute Configurations
AWS Lambda Managed Instances bring Lambda’s operational simplicity to EC2, enabling specialized compute options, cost efficiency, and predictable scaling ...
Adrian Bridgwater | | aws, AWS cloud engineering tools, AWS compute services, AWS Graviton4, AWS Lambda, AWS Lambda Managed Instances, AWS operational simplicity, cloud cost optimization, cloud native, cloud scalability, cloud services, compute-optimized instances, developers, EC2 specialized compute, GPU accelerated computing, Lambda compute configurations, Lambda infrastructure automation, Lambda steady-state workloads, memory-optimized instances, parallel request processing, pay-per-use compute, serverless compute, serverless vs EC2, storage-optimized instances, VPC configuration Lambda
You Can Stop Saying “Cloud,” But You Can’t Take the Cloud Out
Shimmy breaks down why AI hype and agentic rebranding can’t replace the cloud, arguing that despite shifting language, modern AI systems still rely on cloud as their essential backbone ...
Alan Shimel | | agentic AI hype, AI depends on cloud, AI rebranding trend, AWS reInvent insights, cloud AI infrastructure, cloud foundational tech, cloud maturity era, cloud native evolution, cloud not dead, cloud vs agentic AI, enterprise cloud strategy, hyperscaler growth, platform engineering cloud

