Updates and recent posts about AIStor..

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@kaptain shared a link, 5 months, 1 week ago

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How Kubernetes Became the New Linux

AWS just handed overKarpenterandKubernetes Resource Orchestrator (Kro)to Kubernetes SIGs. Big move. It's less about AWS-first, more about playing nice across the ecosystem. Kroauto-spins CRDs and microcontrollers for resource orchestration.Karpenterhandles just-in-time node provisioning - leaner, fa.. read more

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@kaptain shared a link, 5 months, 1 week ago

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How I Cut Kubernetes Debugging Time by 80% With One Bash Script

The reality of Kubernetes troubleshooting: 80% of the time is spent locating the issue, while only 20% is used for the fix. Managing eight Kubernetes clusters highlighted this pattern. A tool was developed to provide a complete cluster health report in under a minute, streamlining the process and sa.. read more

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@kaptain shared a link, 5 months, 1 week ago

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Top 5 hard-earned lessons from the experts on managing Kubernetes

Running Kubernetes in production isn’t just clicking “Create Cluster.” It means locking down RBAC, tightening up network policy, tracking autoscaling metrics, and making sure your images don’t ship with surprises. Managed clusters help get you started. But real workloads need more: hardened configs,.. read more

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@kaptain shared a link, 5 months, 1 week ago

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The guide to kubectl I never had.

Glasskube dropped a thorough guide tokubectl- the commands, the flags (--dry-run, etc.), how to chain stuff together, and how to keep your config sane. Bonus: a solid roundup ofkubectl plugins. Think observability (like K9s), policy checks, audit trails, and Glasskube’s take on declarative package m.. read more

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@kaptain shared a link, 5 months, 1 week ago

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Kubernetes Tutorial For Beginners [72 Comprehensive Guides]

The series dives deep into real-world Kubernetes - starting with hands-on setup viaKubeadmandeksctl, then moving throughmonitoring,logging,CI/CD, andMLOps. It tracks key release changes up tov1.30, including the confirmed death ofDockershimsince v1.24... read more

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@kala shared a link, 5 months, 1 week ago

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20x Faster TRL Fine-tuning with RapidFire AI

RapidFire AI just dropped a scheduling engine built for chaos - and control. It shards datasets on the fly, reallocates as needed, and runs multipleTRL fine-tuning configs at once, even on a single GPU. No magic, just clever orchestration. It plugs into TRL withdrop-in wrappers, spreads training acr.. read more

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@kala shared a link, 5 months, 1 week ago

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Code execution with MCP: building more efficient AI agents

Code is taking over MCP workflows - and fast. With theModel Context Protocol, agents don’t just call tools. They load them on demand. Filter data. Track state like any decent program would. That shift slashes context bloat - up to 98% fewer tokens. It also trims latency and scales cleaner across tho.. read more

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@kala shared a link, 5 months, 1 week ago

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Hacking Gemini: A Multi-Layered Approach

A researcher found a multi-layer sanitization gap inGoogle Gemini. It let attackers pull off indirect prompt injections to leak Workspace data - think Gmail, Drive, Calendar - using Markdown image renders across Gemini andColab export chains. The trick? Sneaking through cracks between HTML and Markd.. read more

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@kala shared a link, 5 months, 1 week ago

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'I'm deeply uncomfortable': Anthropic CEO warns that a cadre of AI leaders, including himself, should not be in charge of the technology’s future

Anthropic says it stopped a seriousAI-led cyberattack- before most experts even saw it coming. No major human intervention needed. They didn't stop there. Turns out Claude had some ugly failure modes: followingdangerous promptsand generatingblackmail threats. Anthropic flagged, documented, patched, .. read more

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@kala shared a link, 5 months, 1 week ago

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Building serverless applications with Rust on AWS Lambda

AWS Lambda just bumpedRusttoGeneral Availability- production-ready, SLA covered, and finally with full AWS Support. Deploy withCargo Lambda. Wire it into your stack usingAWS CDK, which now has a dedicated construct to spin up HTTP APIs with minimal fuss. System-level shift:Serverless isn't just for .. 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.