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From Manual Testing to AI-Generated Automation: Our Azure DevOps MCP + Playwright Success Story

A team wired up Azure DevOps’MCP serverwithGitHub Copilotto crank outPlaywrightend-to-end tests from manual test cases. They now run tests on demand from Azure Test Plans, convert entire test suites in bulk, and drop the results into CI pipelines—no hand-holding required. System shift:AI's not just.. read more  

From Manual Testing to AI-Generated Automation: Our Azure DevOps MCP + Playwright Success Story
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How I eliminated networking complexity

A fresh pattern’s gaining traction:Docker + Tailscale sidecarsreplacing old-school reverse proxies and clunky VPNs. Each service runs as its ownmesh-routed node, containerized and independent. The trick?Network namespace sharing.App containers hook into the Tailscale mesh with no exposed ports, no .. read more  

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AI inference supercharges on Google Kubernetes Engine

Google Cloud's pushingGKEbeyond container orchestration, framing it as an AI inference engine. Meet the new crew: theInference Gateway(smart load balancer, talks models and hardware),custom compute classes, and aDynamic Workload Schedulerthat tunes for both speed and spend. The setup handles GPU an.. read more  

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MariaDB Kubernetes Operator 25.08.0 Adds AI Vector Support and Disaster Recovery Enhancements

MariaDB Kubernetes Operator 25.08.0 drops some real upgrades. First up:physical backups. Now supported through native MariaDB tools and Kubernetes CSI snapshots—huge win if you're dealing with chunky datasets and tight recovery windows. It alsodefaults to MariaDB 11.8, which brings in anative vect.. read more  

MariaDB Kubernetes Operator 25.08.0 Adds AI Vector Support and Disaster Recovery Enhancements
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Cloud native is not just for hyperscalers

CNCF just dropped anAI workload conformance program, built like the Kubernetes one—so AI tools play nice across clusters. Portability, meet your referee. It’s tightening the loop betweenOpenTelemetry and OpenSearch, turning ad-hoc hacks into actual cross-project coordination. AndBackstage and GitOp.. read more  

Cloud native is not just for hyperscalers
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Introducing Kubernetes for Snowflake

Snowflake just leveled up its workload scheduler—now driven by LLMs and reinforcement learning. Instead of locking jobs to static warehouses, it predicts where to send them in real-time. Smarter routing, tighter hardware use, over40%shaved off compute bills. Bigger picture:Another nod toward ML-bas.. read more  

Introducing Kubernetes for Snowflake
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Kubernetes Observability: Pillars, Tools & Best Practices

Kubernetes observability isn’t just about catching metrics or tailing logs. It’s about stitching togethermetrics, logs, and tracesto see what’s actually happening—across services, over time, and through the chaos. Thing is, Kubernetes doesn’t come with this built in. So teams hack together toolchai.. read more  

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Accessing the Kubernetes API from SQL Server 2025

SQL Server 2025 rolls outspinvokeexternalrestendpoint, a new way to hit REST APIs straight from T-SQL. That includes calling the Kubernetes API—thanks to a reverse proxy in front. The setup’s not exactly plug-and-play. You’ll need custom TLS certs, an nginx reverse proxy, and Kubernetes RBAC to kee.. read more  

Accessing the Kubernetes API from SQL Server 2025
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Cloudera Acquires Taikun for Managing Kubernetes and Cloud

Cloudera acquired Taikun for seamless deployment of data and AI workloads in any environment. This move reinforces Cloudera's commitment to flexibility and innovation in managing complex IT infrastructures... read more  

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Introducing Headlamp AI Assistant

Headlamp just dropped an AI Assistant plugin that foldsLLM-driven actions and queriesstraight into the Kubernetes UI. It taps intocontext-aware promptsto spot issues, restart deployments, and hunt down flaky pods—without leaving the interface. System shift:This pushes Kubernetes toward intent-based.. read more  

AIStor is an enterprise-grade, high-performance object storage platform built for modern data workloads such as AI, machine learning, analytics, and large-scale data lakes. It is designed to handle massive datasets with predictable performance, operational simplicity, and hyperscale efficiency, while remaining fully compatible with the Amazon S3 API. AIStor is offered under a commercial license as a subscription-based product.

At its core, AIStor is a software-defined, distributed object store that runs on commodity hardware or in containerized environments like Kubernetes. Rather than being limited to traditional file or block interfaces, it exposes object storage semantics that scale from petabytes to exabytes within a single namespace, enabling consistent, flat addressing of vast datasets. It is engineered to sustain very high throughput and concurrency, with examples of multi-TiB/s read performance on optimized clusters.

AIStor is optimized specifically for AI and data-intensive workloads, where throughput, low latency, and horizontal scalability are critical. It integrates broadly with modern AI and analytics tools, including frameworks such as TensorFlow, PyTorch, Spark, and Iceberg-style table engines, making it suitable as the foundational storage layer for pipelines that demand both performance and consistency.

Security and enterprise readiness are central to AIStor’s design. It includes capabilities like encryption, replication, erasure coding, identity and access controls, immutability, lifecycle management, and operational observability, which are important for mission-critical deployments that must meet compliance and data protection requirements.

AIStor is positioned as a platform that unifies diverse data workloads — from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets — within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.