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@faun shared a link, 1 year ago

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Kubernetes 1.33: Resizing Pods Without the Drama (Finally!)

Kubernetes 1.33brings in-place pod vertical scaling, allowing you to adjust CPU and memory without restarting pods, a game-changer for seamless resource management in production workloads. This feature simplifies vertical pod autoscaling especially for stateful workloads like databases... read more

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@faun shared a link, 1 year ago

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GitOps for Kubernetes With Nixidy and ArgoCD

Nixidyturns Kubernetes YAMLs into sleek, declarative Nix setups. It offers a robust, repeatable config flow—even for those complex Helm charts. Spice up your deployment by pairingArgoCDwith encrypted secrets viasops-secrets-operator. Now you can wrangle sensitive data in Git with style—and security... read more

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@faun shared a link, 1 year ago

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F5, Inc Announces New Capabilities for F5 BIG-IP Next for Kubernetes

F5, Inc. announced new capabilities for F5 BIG-IP Next for Kubernetes in collaboration with NVIDIA Corporation. The F5 BIG-IP Next for Kubernetes will be accelerated with NVIDIA’s BlueField-3 DPUs and the NVIDIA DOCA software framework... read more

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@faun shared a link, 1 year ago

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State of App Dev: Security

Securityisn’t just for the IT crowd anymore. Everyone's on duty.Only 1%of developers bother to look the other way. A mere20%of organizations throw money at outsiders to handle it. The real trip wire? Planning. It derails teams faster than you'd believe... read more

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@faun shared a link, 1 year ago

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F5 Unleashes Innovation with Powerful New AI Capabilities on BIG-IP Next for Kubernetes on NVIDIA BlueField-3 DPUs

TheModel Context Protocol (MCP)just crashed the party, turning heads and flipping tables with its focus on tailor-made AI setups. EnterAI factoriesandNeoclouds—souped-up cloud havens crafted to power-hungry AI demands. Handle with care, because these bad boys redefine what's possible... read more

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@faun shared a link, 1 year ago

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Amazon EKS Pod Identity streamlines cross account access

Amazon EKS Pod Identityjust got an upgrade. Now you can tap into cross-account access usingIAM role chaining. Forget intricate setups and tiresome code changes. Drop in source and target IAM roles, and let EKS juggle temp credentials at runtime. It's innovation doing a happy dance... read more

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@faun shared a link, 1 year ago

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Publishing a Docker container for Microsoft Edit to the GitHub Container Registry

Edithits GitHub's Container Registry like a buzzsaw, powered by Docker. Built forApple Silicon, it ridesAlpinelike a speed demon. No fuss, just raw efficiency... read more

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@faun shared a link, 1 year ago

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AWS’ EKS Kubernetes ‘Critical Security’ Flaw Exposes Credentials, Says Trend Micro

Trend Microblows the lid off Amazon's EKS snafu—misconfigured Kubernetes containers brazenly leaking AWS credentials. Cue privilege escalation chaos. AWS shrugs, hiding behind the "Shared Responsibility" mantra. Trend Micro, undeterred, sounds the alarm: secure those configurations, and embrace the .. read more

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@faun shared a link, 1 year ago

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NSEnter and Kubernetes

nsenteris your backstage pass to aKubernetesnode. It plays with Linux namespaces, crashing through isolation walls for a direct look inside. Summon it withPID1 and proper permissions, and you're deep in the node's core. No middleman required... read more

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@faun shared a link, 1 year ago

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Why Chose OCI Artifacts for AI Model Packaging

Docker Model Runner injects LLMs into OCI artifacts, seamlessly marrying model delivery with container rituals. No need to invent custom toolchains. Think uncompressed "layers"—they're the secret sauce for faster, sharper, more efficient Model-Runner magic. It's not just a change; it's a quantum lea.. 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.