Updates and recent posts about AIStor..

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@faun shared a link, 6 months, 3 weeks ago

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How to automatically disable users in AWS Managed Microsoft AD based on GuardDuty findings

AWS just dropped a new threat-response setup that tiesGuardDuty,EventBridge,Step Functions, andSystems Manager Run Commandinto one clean pipeline. The goal? Hunt for EC2 threats and lock downActive Directoryaccounts—automatically. GuardDuty kicks off the flow when it spots trouble. From there, Even.. read more

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@faun shared a link, 6 months, 3 weeks ago

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Kali Linux can now run in Apple containers on macOS systems

Cybersecurity professionals can now launch Kali Linux in a virtualized container on macOS Sequoia using Apple's new containerization framework. Apple announced a new framework at WWDC 2025, allowing Apple Silicon hardware to run isolated Linux distros in a virtualized environment. There are limitati.. read more

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@faun shared a link, 6 months, 3 weeks ago

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Introducing Approvals in Pulumi ESC

Pulumi ESC just leveled up withApprovals—structured reviews for environment config changes, straight from Console, CLI, SDK, or VS Code. Think pull requests, but for your infra settings. No more YOLO updates. Teams can now lock down config changes with required sign-offs. More control. Cleaner logs.. read more

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@faun shared a link, 6 months, 3 weeks ago

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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

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@faun shared a link, 6 months, 3 weeks ago

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🚨 Azure Service Health Built-In Policy (Preview) – Now Available!

Microsoft just droppedAzure Service Health Built-In Policy(Preview). It lets teams push Service Health alerts across every Azure subscription—automatically—using Azure Policy. No more piecemeal setup. It folds in AMBA lessons, supports custom rules and action groups, and locks in alert coverage at .. read more

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@faun shared a link, 6 months, 3 weeks ago

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Building on the foundation of OpenTelemetry eBPF Instrumentation: what’s new in Grafana Beyla 2.5

Grafana Beyla 2.5 goes all-in on upstreamOpenTelemetry eBPF Instrumentation, baking it right into the core. This release addsauto-instrumentation for MongoDB and JSON-RPC,manual spans in Go, and tightertrace correlation for NodeJS. New in town:survey mode. Think lightweight service discovery—no ful.. read more

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@faun shared a link, 6 months, 3 weeks ago

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Writing an internal Terraform provider from A to Z

Typeform rolled their ownTerraform providerto wrangle runtime data through an internal API. Built with HashiCorp’sGo SDK, the official scaffolding framework, and wired up withacceptance testsfor full lifecycle muscle. They skipped the publicTerraform Registryentirely. Instead, they shipped provider.. read more

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@faun shared a link, 6 months, 3 weeks ago

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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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@faun shared a link, 6 months, 3 weeks ago

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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

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@faun shared a link, 6 months, 3 weeks ago

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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

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.