Join us

ContentUpdates and recent posts about AIStor..
Link
@kaptain shared a link, 1 month ago
FAUN.dev()

Podman fixed every problem I had with Docker, and I switched in an afternoon

Author swappedDockerforPodman. The swap revealed CLI parity and minor networking and volume tweaks. Podmaneschews a centraldaemon. It runs containers as system processes and defaults torootlessviauser namespaces. That cuts privilege exposure and trims baseline overhead... read more  

Podman fixed every problem I had with Docker, and I switched in an afternoon
Link
@kala shared a link, 1 month ago
FAUN.dev()

Agentic payments are coming. Is your company ready?

Google'sChromeadded native support forUniversal Commerce Protocol (UCP). That letsGeminiagents execute agentic payments and pause for user confirmation. Merchants and platforms such asPayPal,Amazon Rufus, andHome Depotran agentic commerce pilots.PayPalimplementedUCPsupport. Agent scraping and protoc.. read more  

Agentic payments are coming. Is your company ready?
Link
@kala shared a link, 1 month ago
FAUN.dev()

Claude now creates interactive charts, diagrams and visualizations

Claude (beta) renders inline, temporary charts, diagrams, and visualizations in chat viaClaude Visual Composer. Visuals stay editable on request. Enabled by default. Claude can opt to generate visuals or follow direct prompts. Integrates withFigma,Canva, andSlack... read more  

Claude now creates interactive charts, diagrams and visualizations
Link
@kala shared a link, 1 month ago
FAUN.dev()

I Will Never Use AI to Code (or write)

This article discusses the negative impacts of relying on AI for coding and skill development. The cycle of using AI leading to skill decay, skill collapse, and the end of capability is highlighted as a major concern. The economic implications of AI usage in various industries and the lack of profit.. read more  

Link
@kala shared a link, 1 month ago
FAUN.dev()

How AI Agents Automate CVE Vulnerability Research

A multi-agent system runs onGoogle's Agent Development Kit (ADK). It orchestrates specialized AI models for CVE research and report synthesis. It runso4-mini-deep-researchwith web search. On timeouts it falls back toGPT‑5. It extracts structured technical requirements. It maps those requirements to .. read more  

How AI Agents Automate CVE Vulnerability Research
Link
@devopslinks shared a link, 1 month ago
FAUN.dev()

AWS RDS Cost Optimization Guide: Cut Database Costs in 2026

Amazon RDS costs are not fixed - they vary based on configuration and usage. Making informed configuration and governance decisions is key to optimizing costs. Graviton instances offer better price-performance for common databases, while storage costs can be reduced by decoupling performance from ca.. read more  

AWS RDS Cost Optimization Guide: Cut Database Costs in 2026
Link
@devopslinks shared a link, 1 month ago
FAUN.dev()

Top 10 best practices for Amazon EMR Serverless

Amazon EMR Serverless allows users to run big data analytics frameworks without managing clusters, integrating with various AWS services for a comprehensive solution. The top 10 best practices for optimizing EMR Serverless workloads focus on performance, cost, and scalability, including consideratio.. read more  

Top 10 best practices for Amazon EMR Serverless
Link
@devopslinks shared a link, 1 month ago
FAUN.dev()

Building a Database on S3

This paper from 2008 proposes a shared-disk design over Amazon S3 for cloud-native databases, separating storage from compute. Clients write redo logs to Amazon SQS instead of directly to S3 to hide latency. The paper presents a blueprint for serverless databases before the term existed... read more  

Link
@devopslinks shared a link, 1 month ago
FAUN.dev()

Introducing Agentic Observability in NGINX: Real-time MCP Traffic Monitoring

NGINX ships an open-sourceAgentic ObservabilityJS module. It parsesMCPtraffic and extracts tool names, error statuses, and client/server identities. The module uses nativeOpenTelemetryto export spans. A Docker Compose reference wires upOTel collector,Prometheus, andGrafanafor realtime throughput, la.. read more  

Introducing Agentic Observability in NGINX: Real-time MCP Traffic Monitoring
Link
@devopslinks shared a link, 1 month ago
FAUN.dev()

AI Isn't Replacing SREs. It's Deskilling Them.

This post discusses the impact of AI on the role of Site Reliability Engineers (SREs) by drawing parallels to historical research on automation. It highlights the risk of deskilling and never-skilling for SREs who heavily rely on AI tools for incident response. The post also suggests potential appro.. 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.