Updates and recent posts about AIStor..

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@dwisiswant0 shared a post, 3 weeks, 1 day ago

The most practical, fast, tiny command sandboxing for AI agents

#sandbox #command... #AI agen... #AI agen... #AI agen...

Need to run one sketchy command without a full container? Here is the most practical, lightweight way to lock down one risky command in your AI pipeline. No daemon, no root, no image build.

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@laura_garcia shared a post, 3 weeks, 1 day ago

Software Developer, RELIANOID

🚀 Deploy RELIANOID Community Edition v7 on Microsoft Azure using Terraform.

⚡ Infrastructure ready in minutes ⚡ Official Terraform module ⚡ Fully automated Azure deployment Simple. Fast. Reproducible. #Terraform#Azure#DevOps#IaC#LoadBalancer#CloudInfrastructure#RELIANOID https://www.relianoid.com/resources/knowledge-base/community-edition-v7-administration-guide/deploy-reli..

terraform_relianoid_community_azure_img2

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@laura_garcia shared a post, 3 weeks, 2 days ago

Software Developer, RELIANOID

🚀 See you at DeveloperWeek — February 18–20, 2026!

🚀 See you at DeveloperWeek — February 18–20, 2026! The world’s largest independent software development & AI engineering conference lands in San Jose, bringing together developers, architects, and tech leaders shaping the future of software. From AI & cloud-native to DevSecOps and developer experien..

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@kala shared an update, 3 weeks, 2 days ago

FAUN.dev()

OpenAI Hires OpenClaw Creator Peter Steinberger; Project Moves to Independent Foundation

#OpenCla... #autonom... #AI #OpenAI #Peter S...

Peter Steinberger, creator of OpenClaw, is joining OpenAI to work on bringing AI agents to a broader audience, while OpenClaw will move to an independent open-source foundation and continue development outside OpenAI’s direct control.

OpenAI Hires OpenClaw Creator Peter Steinberger; Project Moves to Independent Foundation

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@juliocalves started using tool Terraform , 3 weeks, 3 days ago.

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@juliocalves started using tool Kubernetes , 3 weeks, 3 days ago.

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@juliocalves started using tool Kubectl , 3 weeks, 3 days ago.

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@juliocalves started using tool Grafana , 3 weeks, 3 days ago.

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@juliocalves started using tool Amazon ECS , 3 weeks, 3 days ago.

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@juliocalves started using tool Amazon CloudWatch , 3 weeks, 3 days 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.