Updates and recent posts about AIStor..

Posts
Description

Link

@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

Zigbook – Learn the Zig Programming Language

Learning Zig is not just about adding a language to your resume. It is about fundamentally changing how you think about software. The book promise: “You came for syntax. You'll leave with a philosophy.”!.. read more

Link

@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

How to Improve Your Programming Skills by Building Games

Building games forces devs to get good atevent-driven code,modular design,real-time tuning, andcreative debugging, fast. It sharpens instincts aroundECS patterns, math-backed logic, and hands-on UX thinking... read more

Link

@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

What’s new in Flutter 3.38

Flutter 3.38 drops with Dart 3.10’s newdot shorthand- on by default. Less boilerplate, more signal. Android getspredictive back gestures, the web getsstateful hot reload, and Windows devs finally getmulti-monitor support. Overlay controls are tighter. Previews play nicer with your IDE. Under the hoo.. read more

Link

@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

Introducing Code Wiki: Accelerating your code understanding

Google just droppedCode Wikiin public preview. It builds live, structured docs straight from your codebase - and stays synced as things change. Docs evolve with your repo. Automatically. A Gemini-powered chat agent sits at the center, armed with full-repo context, clickable code links, and diagrams .. read more

Link

@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

Practical coding with AI Assistance

Developers using AI agents like Cursor are hitting a wall: vague, messy blob-code. Especially in frameworks likeLangChain, where sketchy training data can produce long-winded or broken output. The problem? AI generates "just vibes" instead of structure. The fix: go in with a plan. Aspec-driven, cont.. read more

Link

@varbear shared a link, 3 months, 1 week ago

FAUN.dev()

I Analyzed 2,500 YouTube Videos to Understand Why RedLetterMedia Are Internet Darlings

A data analyst pulled subtitles and metadata from ~2,500 YouTube videos, rigged upyt-dlpwith proxy routing and scripts to automate the haul. The goal? Stack RedLetterMedia (RLM) against algorithm-first mega-channels like MrBeast and MKBHD. By slicing through heatmaps, subtitles, and metadata, the an.. read more

Link

@kaptain shared a link, 3 months, 1 week ago

FAUN.dev()

AWS Backup now supports Amazon EKS

AWS Backup just added support forAmazon EKS. Now you can back up cluster state and persistent volumes, no agents, no third-party hacks. It handles scheduling, retention, and immutability out of the box. Restore full clusters or drill down to specific components, even across Regions and accounts... read more

Link

@kaptain shared a link, 3 months, 1 week ago

FAUN.dev()

ConfigHub: Why Your Internal Developer Platform Needs It

See why GitOps often feels like a sprawl of configs, discover how to manage Configuration as Data for your Kubernetes platform, and learn how ConfigHub can help... read more

Link

@kaptain shared a link, 3 months, 1 week ago

FAUN.dev()

KServe becomes a CNCF incubating project

KServe is upgrading.The CNCF pulled it into incubation, backing it astheKubernetes-native way to serve both generative and predictive AI. Translation: it’s not a side project anymore - it’s core infra. Version 0.15 steps up with tighter integrations across the stack:vLLM,Envoy Gateway,llm-d,Knative,.. read more

Link

@kaptain shared a link, 3 months, 1 week ago

FAUN.dev()

Amazon Elastic Kubernetes Service gets independent affirmation of its zero operator access design

Amazon EKS just went full Fort Knox. It now runs on azero operator accessmodel - meaning even AWS can’t peek inside your Kubernetes control or data plane. The setup leans on theNitro System’s confidential compute,guarded APIs, andmulti-party approval pipelines. NCC Group also kicked the tires and ga.. 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.