Updates and recent posts about AIStor..

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@yelbur started using tool Go , 5 days, 12 hours ago.

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@yelbur started using tool Fedora , 5 days, 12 hours ago.

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@yelbur started using tool Docker , 5 days, 12 hours ago.

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@yelbur started using tool BigQuery , 5 days, 12 hours ago.

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@kala shared a link, 6 days, 18 hours ago

FAUN.dev()

Realtime Prompting Guide

OpenAI shipsgpt-realtimeand declares GA for theRealtime API. It's a speech-to-speech model that tightens instruction-following, steadiestool calling, and lifts voice fidelity. Latency drops. True realtime agents become possible. The release prescribesprompt skeletons,JSON envelopetool outputs,sessio.. read more

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@kala shared a link, 6 days, 18 hours ago

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Do you need an MCP to build your native app?

Do you need an MCP to build your native app? Surprisingly, modern agents succeed either way. The real difference is how much time, cost, and context you waste along the way... read more

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@kala shared a link, 6 days, 18 hours ago

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The Pentagon is making a mistake by threatening Anthropic

Anthropic's Claude Gov, optimized for national security uses, has fewer restrictions than regular versions. The Pentagon is threatening retaliation if Anthropic does not waive these restrictions by Friday, including invoking the Defense Production Act or declaring Anthropic a supply chain risk. Anth.. read more

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@kala shared a link, 6 days, 18 hours ago

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Introducing helm

helm usesTypeScripttypes to registerskillsas typed functions with structured I/O. Permissions follow a clear precedence: exact→wildcard→skill→global. Agents get a keywordsearchtool and a code-execution tool that runs JS inside anSESsandbox. A recursiveproxyforwards calls overIPCto the parent, which .. read more

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@kaptain shared a link, 6 days, 18 hours ago

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Before You Migrate: Five Surprising Ingress-NGINX Behaviors You Need to Know

The K8s blog exposesIngress-NGINXdefaults that clash withGateway API. These include case-insensitive prefix regexes. Host-wide annotation effects. Path rewrites. Slash redirects. URL normalization. Kubernetes retiresIngress-NGINXinMarch 2026.Gateway API 1.5graduatesListenerSetand theHTTPRoute CORS.. read more

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@kaptain shared a link, 6 days, 18 hours ago

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From Chaos to Clarity: How We Built a Self-Healing CI/CD Pipeline That Talks to JIRA

Transitioning JIRA tickets to trigger deployments was key for this team struggling with manual deploys, leading to significant savings in time and reduction in errors. The architecture involved a JIRA Controller Pipeline, a Project Deployment Pipeline, and a JIRA Manager Pipeline, all aimed at seaml.. 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.