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@eon01 shared a post, 2ย months ago
Founder, FAUN.dev

Announcing FAUN.sensei() โ€” Self-paced guides to grow fast โ€” even when tech moves faster.

Docker GitLab CI/CD Helm Kubernetes GitHub Copilot

After months of hard work, FAUN.sensei() is finally alive!

FAUN.sensei()
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@laura_garcia shared a post, 2ย months ago
Software Developer, RELIANOID

๐ŸŒŸ ๐—ช๐—ฒโ€™๐—ฟ๐—ฒ ๐—›๐—ถ๐—ฟ๐—ถ๐—ป๐—ด! ๐—๐—ผ๐—ถ๐—ป ๐˜๐—ต๐—ฒ ๐—ฅ๐—˜๐—Ÿ๐—œ๐—”๐—ก๐—ข๐—œ๐—— ๐—ง๐—ฒ๐—ฎ๐—บ ๐ŸŒŸ

Are you passionate about technology, networking, and innovation? At RELIANOID, weโ€™re building cutting-edge solutions that power secure, scalable, and reliable infrastructures โ€” and weโ€™re looking for talented people to join us on this journey! ๐Ÿš€ Whether youโ€™re an experienced professional or just star..

careers RELIANOID hiring
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@arunsanna added a new tool AWS-Sage , 2ย months ago.
Story FAUN.dev() Team Trending
@eon01 shared a post, 2ย months ago
Founder, FAUN.dev

Helm Cheat Sheet: Everything You Need to Know to Start Using Helm

Helm Kubernetes

Helm is the package manager Kubernetes was missing. It lets you package applications and their dependencies into charts, deploy them as versioned releases, and manage installs, upgrades, and rollbacks in a consistent and repeatable way. This post walks through what Helm is, how to install it, and the core commands you will use day to day.

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@varbear added a new tool Git , 2ย months ago.
Course
@eon01 published a course, 2ย months ago
Founder, FAUN.dev

Helm in Practice

#Helmย 
Helm Argo CD Docker k3s Harbor Kubernetes

Designing, Deploying, and Operating Kubernetes Applications at Scale

Helm in Practice
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@zeno added a new tool Sigstore , 2ย months ago.
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@laura_garcia shared a post, 2ย months ago
Software Developer, RELIANOID

๐Ÿš€ ๐—ฅ๐—˜๐—Ÿ๐—œ๐—”๐—ก๐—ข๐—œ๐—— ๐Ÿณ.๐Ÿด.๐Ÿฌ ๐—–๐—ผ๐—บ๐—บ๐˜‚๐—ป๐—ถ๐˜๐˜† ๐—˜๐—ฑ๐—ถ๐˜๐—ถ๐—ผ๐—ป ๐—ถ๐˜€ ๐—ผ๐˜‚๐˜!

This new release brings a modern foundation and powerful cloud automation to the open-source community: ๐Ÿ”น Debian Bookworm 12.12 ๐Ÿ”น Major upgrade support ๐Ÿ”น Automated cloud deployments on AWS, Azure & GCP ๐Ÿ”น Terraform-based Infrastructure as Code ๐Ÿ”น Improved Web UI usability and responsiveness Designed t..

Community edition V7.8 release relianoid
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@viktoriiagolovtseva shared a post, 2ย months ago

A Ready-to-Use Business Travel Template For Jira

For many of us, even planning a personal trip can be a daunting task. Missing one detail when booking a flight or hotel can lead to a disaster. The challenge becomes even greater when you need to organize a business trip for others, especially for large events where many people travel simultaneously.ย 

Additionally, business travel often requires multiple approvals and cross-team collaboration. You will need a solution to keep everyone on the same page and easily track every detail. A business travel template for Jira is an ideal way to solve this challenge.ย 

In this blog post, we share a ready-to-use template and explain how to use it.

Zrzut ekranu 2025-12-17 143647
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 AIStorโ€™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 โ€” from unstructured storage for application data to structured table storage for analytics, as well as AI training and inference datasets โ€” within a consistent object-native architecture. It supports multi-tenant environments and can be deployed across on-premises, cloud, and hybrid infrastructure.