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Mirantis Extends Swarm Support Another Five Years

Mirantisthrows a lifeline toSwarm, promising five more years of support. Why? Simplicity. Even as theKubernetesjuggernaut thunders on, over100clients hang tight to Swarm's straightforward charm.MKEcleverly blends these orchestrators, smoothing your path to Kubernetes while cranking up the security d.. read more  

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Kmesh v1.1.0 Officially Released!

Kmesh v1.1.0shakes things up with an overhauled DNS module. It’s got one job: tackle hostname resolution—no more, no less. BPF configuration? Now effortless, thanks to global variables. As for Kernel-Native mode, it’s less needy. Just a single tweak left inLinux kernel 6.6. Progress... read more  

Kmesh v1.1.0 Officially Released!
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Playbook for building Secure Cloud or Kubernetes Applications

Kubernetes and cloud apps shouldn't toy with security.Least Privilege,Privilege Separation, andZero Trustaren't trendy buzzwords; they're must-have armor. These principles nail down strict controls, carve duties into distinct silos, and demand proof at every turn. What do they transform? They turn u.. read more  

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Running high-performance PostgreSQL on Azure Kubernetes Service

PostgreSQLpumps life into 36% ofKubernetesworkloads. Over at Azure, they've got localNVMestorage that's as fast as a hot knife through butter—perfect for those deployments that absolutely must defy gravity. For the budget-conscious,Premium SSD v2struts in offering beefy scalability. We're talking up.. read more  

Running high-performance PostgreSQL on Azure Kubernetes Service
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Basically Everyone Should Be Avoiding Docker

Docker’s magic? Slick deployment for the Unix-challenged. But here’s the catch: it ties skilled users in knots.Sure, it smooths some bumps, but at the price of freedom. Customizations? Troublesome. Troubleshooting? A nightmare. Simple tasks become tangled puzzles... read more  

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Improving Amazon ECS deployment consistency with SOCI Index Manifest v2

SOCI index manifest v2locks onto container images like a heat-seeking missile. It banishes AWS Fargate deployment gremlins and declutters index management. Switching to v2? Simple—deploy a shiny new CLI subcommand. Voilà, no more accidental SOCI index deletions wreaking havoc on your image indexes... read more  

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How Fortune 500 Companies Are Really Using Kubernetes: Insights from KubeCon London

Platform engineeringis practically the law of the land for cloud-native warriors. Crank upscalability, lighten the load on your talent, and hit that mythical99.9% uptimewith a sprinkling ofOpenTelemetry. Meanwhile, yourGPU utilizationhovers at a pitiful5%. Ouch. That's like having a Ferrari stuck in.. read more  

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Unveiling the Truth: Kubernetes as a Panacea or a Myth?

Kubernetespunches well above its weight, doling out scalability and resilience. But it trips over complexity, gulps down resources, and fumbles database migration. Meanwhile,Istioswoops in with swifttraffic managementand crystal-clear observability. Sadly, it can't magic away those pesky database bo.. read more  

Unveiling the Truth: Kubernetes as a Panacea or a Myth?
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Kubernetes FinOps 2.0: AI-Powered Cost Optimization with Predictive Scaling

Kubernetes FinOps 2.0 leverages AI to slash cloud costs by40–60%throughpredictive autoscalingandspot instance optimization, ensuring peak performance at a fraction of the price. Transition from reactive cloud-cost management to agile, self-optimizing strategies is key for modern software teams aimin.. read more  

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Navigating Failures in Pods With Devices

Kubernetes stumbles when GPUs break down in AI/ML work.Why? It clings to static resource guesses and lacks strong tools to handle crashes. Despite this, developers flock to Kubernetes for its bustling ecosystem. Sure, DIY hacks can patch some holes. But let's be honest—complex workloads deserve smar.. read more  

Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.