Google Releases Magika 1.0: AI File Detection in Rust
Google releases Magika 1.0, an AI file detection system rebuilt in Rust for improved performance and security.
Updates and recent posts about AIStor.. Activity
@kaptain added a new tool Kubernetes Gateway API , 2 months ago.
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@kala shared an update, 2 months ago
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Google’s Cloud APIs Become Agent-Ready with Official MCP Support
Google supports the Model Context Protocol to enhance AI interactions across its services, introducing managed servers and enterprise capabilities through Apigee.
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@devopslinks added a new tool BigQuery , 2 months ago.
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@devopslinks shared an update, 2 months ago
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AWS Previews DevOps Agent to Automate Incident Investigation Across Cloud Environments
AWS introduces an autonomous AI DevOps Agent to enhance incident response and system reliability, integrating with tools like Amazon CloudWatch and ServiceNow for proactive recommendations.
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@devopslinks added a new tool ServiceNow , 2 months ago.
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@laura_garcia shared a post, 2 months ago
Software Developer, RELIANOID
The UK raises the bar on digital security
With cyberattacks on the rise, the Product Security and Telecommunications Infrastructure (PSTI) Act marks a major step toward making connected technology secure by design. In our latest article, we explain: What the PSTI Act requires Why it matters beyond consumer IoT How it signals a global sh..
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@shurup shared a post, 2 months ago
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New CNCF Sandbox projects in 2025: From Podman to CloudNativePG
Each year, 25-30 new Open Source projects related to the Cloud Native ecosystem are accepted to the CNCF Sandbox. In January 2025, there were 13 additions, with four of them donated by Red Hat. Here's the list of these newly added CNCF projects: - Podman Container Tools (security-focused Docker alte..
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@sancharini shared a post, 2 months ago
CI Testing Best Practices for Reliable and Fast Builds
As software teams adopt continuous integration, build speed and reliability become critical success factors. CI testing plays a central role in ensuring that every code change is validated quickly and consistently before it moves further down the delivery pipeline. Without clear practices, however, ..
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.
