Agent Sandbox Brings Kernel-Level Guardrails to AI Agents on Kubernetes
Agent Sandbox, a new Kubernetes primitive, was introduced at KubeCon NA 2025 to enhance AI agent management on Kubernetes and Google Kubernetes Engine.
Updates and recent posts about AIStor..
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@devopslinks shared an update, 3 months, 4 weeks ago
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AWS Unveils Graviton5: A 192-Core Leap in Cloud Performance and Efficiency
AWS introduces Graviton5-based EC2 M9g instances, boosting performance by 25% and enhancing scalability while reducing costs.
News FAUN.dev() Team Trending
@varbear shared an update, 3 months, 4 weeks ago
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Tor Goes Rust: Introducing Arti, a New Foundation for the Future of Tor
The development of "Arti," a Rust-based Tor implementation funded by Zcash, aims to enhance security and efficiency by addressing the limitations of the current C-based Tor.
News FAUN.dev() Team
@kala shared an update, 3 months, 4 weeks ago
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Gemini Deep Research Is Now Programmable Through a New API
The enhanced Gemini Deep Research agent is now available via API, enabling developers to integrate advanced research capabilities into applications, with the open-sourcing of DeepSearchQA for evaluating complex tasks.
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@kala shared an update, 3 months, 4 weeks ago
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GitHub Copilot Adds GPT-5.2 With Long-Context and UI Generation
OpenAI unveils GPT-5.2 for GitHub Copilot, enhancing software engineering with improved long-context reasoning and UI generation, integrated with Microsoft Azure and NVIDIA.
News FAUN.dev() Team
@kala shared an update, 3 months, 4 weeks ago
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GPT-5.2 Quietly Beats Human Experts at Knowledge Work
OpenAI releases GPT-5.2, enhancing professional tasks with improved speed and cost-effectiveness, now available for paid users in ChatGPT and via API.
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.
