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Updates and recent posts about AIStor..

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Link

@devopslinks shared a link, 3 months, 1 week ago

FAUN.dev()

How We Saved 70% of CPU and 60% of Memory in Refinery’s Go Code, No Rust Required.

Refinery 3.0 cuts CPU by 70% and slashes RAM by 60%. The trick: selective field extraction from serialized spans. No full deserialization. Fewer heap allocations. Way less waste. It also recycles buffers, handles metrics smarter, and is gearing up to parallelize its core decision loop... read more

News FAUN.dev() Team

@kaptain shared an update, 3 months, 1 week ago

FAUN.dev()

Docker Brings Production-Grade Hardened Images to Developers at No Cost

#Complia... #Docker ... #supply-... #Securit... #docker

Docker has launched Docker Hardened Images, a secure and minimal set of production-ready images. These images are now freely available to developers.

Docker Brings Production-Grade Hardened Images to Developers at No Cost

Link

@anjali shared a link, 3 months, 1 week ago

Customer Marketing Manager, Last9

OTel Updates: OpenTelemetry Deprecates Zipkin Exporters

OpenTelemetry deprecates Zipkin exporters in favor of native OTLP support. Migration paths and timeline through December 2026.

News FAUN.dev() Team

@kaptain shared an update, 3 months, 1 week ago

FAUN.dev()

Argo CD 3.2.2 Improves Secret Management, Retry Safety, and Auth Checks

#Securit... #gitops #argocd #Argo CD #kuberne...

ArgoCD v3.2.2 has been released, featuring a new addition, two enhancements, and a bug fix. This update aims to improve the overall functionality and reliability of the platform.

Argo CD 3.2.2 Improves Secret Management, Retry Safety, and Auth Checks

News FAUN.dev() Team Trending

@devopslinks shared an update, 3 months, 1 week ago

FAUN.dev()

Rust Confirmed for Linux Kernel: Experiment Concludes Successfully

#Rust #linux #unix #Linux k... #The Rus...

The Rust experiment in the Linux kernel concludes, confirming its suitability and permanence in kernel development, with Rust now used in production and supported by major Linux distributions.

Rust Confirmed for Linux Kernel: Experiment Concludes Successfully

Course

@eon01 published a course, 3 months, 1 week ago

Founder, FAUN.dev

Generative AI For The Rest Of US

#AI #Generat... #LLM #Large L... #gpt

Your Future, Decoded

News FAUN.dev() Team

@kaptain shared an update, 3 months, 1 week ago

FAUN.dev()

Kubernetes v1.35 Timbernetes Release: 60 Enhancements

#k8s #Timbern... #cgroups #docker #kuberne...

Kubernetes v1.35, the Timbernetes Release, debuts with 60 enhancements, including stable in-place Pod updates and beta features for workload identity and certificate rotation.

Kubernetes v1.35 Timbernetes Release: 60 Enhancements

Activity

@kaptain added a new tool Kubernetes Gateway API , 3 months, 1 week ago.

News FAUN.dev() Team

@kala shared an update, 3 months, 1 week ago

FAUN.dev()

Google Releases Magika 1.0: AI File Detection in Rust

#Rust #Generat... #file ty... #Magika #AI

Google releases Magika 1.0, an AI file detection system rebuilt in Rust for improved performance and security.

Google Releases Magika 1.0: AI File Detection in Rust

Activity

@kala added a new tool Magika , 3 months, 1 week ago.

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.