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Course
@eon01 published a course, 2ย months, 3ย weeks ago
Founder, FAUN.dev

Cloud-Native Microservices With Kubernetes - 2nd Edition

Helm Jaeger OpenTelemetry Prometheus Docker Grafana Loki Grafana Kubernetes Kubectl

A Comprehensive Guide to Building, Scaling, Deploying, Observing, and Managing Highly-Available Microservices in Kubernetes

Cloud-Native Microservices With Kubernetes - 2nd Edition
Course
@eon01 published a course, 2ย months, 3ย weeks ago
Founder, FAUN.dev

Building with GitHub Copilot

GitHub Copilot Go Python

From Autocomplete to Autonomous Agents

Building with GitHub Copilot
Link
@anjali shared a link, 2ย months, 3ย weeks ago
Customer Marketing Manager, Last9

Instrument Jenkins With OpenTelemetry

Instrument Jenkins with OpenTelemetry to understand pipeline behavior, stage latency, and deploy steps using a single telemetry flow.

Otel_injector
ย Activity
@devopslinks added a new tool Fleet , 2ย months, 3ย weeks ago.
ย Activity
@kaptain added a new tool Rancher Kubernetes Engine (RKE2) , 2ย months, 3ย weeks ago.
Course
@eon01 published a course, 2ย months, 3ย weeks ago
Founder, FAUN.dev

End-to-End Kubernetes with Rancher, RKE2, K3s, Fleet, Longhorn, and NeuVector

Rancher Longhorn Rancher Kubernetes Engine (RKE2) Rancher Kubernetes Engine (RKE) Fleet NeuVector k3s GNU/Linux Docker Traefik Kubectl

The full journey from nothing to production

End-to-End Kubernetes with Rancher, RKE2, K3s, Fleet, Longhorn, and NeuVector
Story
@laura_garcia shared a post, 2ย months, 3ย weeks ago
Software Developer, RELIANOID

๐Ÿ”ฅ ๐—•๐—น๐—ฎ๐—ฐ๐—ธ ๐—™๐—ฟ๐—ถ๐—ฑ๐—ฎ๐˜† ๐—ฎ๐˜ ๐—ฅ๐—˜๐—Ÿ๐—œ๐—”๐—ก๐—ข๐—œ๐——: ๐—˜๐˜…๐—ฐ๐—น๐˜‚๐˜€๐—ถ๐˜ƒ๐—ฒ ๐—ฃ๐—ฟ๐—ผ๐—บ๐—ผ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—”๐—ฟ๐—ฒ ๐—Ÿ๐—ถ๐˜ƒ๐—ฒ! ๐Ÿ”ฅ

This year, weโ€™re taking Black Friday to the next level โ€” with ๐˜๐—ฎ๐—ถ๐—น๐—ผ๐—ฟ๐—ฒ๐—ฑ ๐—ฝ๐—ฟ๐—ผ๐—บ๐—ผ๐˜๐—ถ๐—ผ๐—ป๐˜€ designed specifically for our users, partners, and customers, who will receive their ๐—ฒ๐˜…๐—ฐ๐—น๐˜‚๐˜€๐—ถ๐˜ƒ๐—ฒ ๐—ผ๐—ณ๐—ณ๐—ฒ๐—ฟ ๐—ฑ๐—ถ๐—ฟ๐—ฒ๐—ฐ๐˜๐—น๐˜† tomorrow, perfectly matched to their environment โžก๏ธ ๐ŸŽ ๐—–๐˜‚๐˜€๐˜๐—ผ๐—บ๐—ถ๐˜‡๐—ฒ๐—ฑ ๐—ข๐—ณ๐—ณ๐—ฒ๐—ฟ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—˜๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐—ก๐—ฒ๐—ฒ๐—ฑ. ๐Ÿš€ ๐——๐—ผ ๐˜†๐—ผ๐˜‚ ๐˜„๐—ฎ๐—ป๐˜ ๐˜๐—ผ ๐—ธ๐—ป๐—ผ..

Story
@thiagoterzi shared a post, 2ย months, 3ย weeks ago
CEO, dgt27

Overcoming Data Duplication in CRM Systems

Learn how to prevent and resolve CRM data duplication using automation, AI tools, and smart integrations like Marketo Salesforce integration to maintain cleaner data.

Overcoming Data Duplication in CRM Systems
Story
@laura_garcia shared a post, 2ย months, 4ย weeks ago
Software Developer, RELIANOID

๐Ÿš€ New Deployment Guide: RELIANOID Enterprise Edition v8 on Azure with Terraform

We're excited to share a 1-minute deployment guide showing how to quickly launch the RELIANOID Load Balancer Enterprise Edition v8 on Microsoft Azure using our official Terraform module. ๐Ÿ”ง Whatโ€™s inside the guide? Prerequisites (Terraform, Azure CLI, SSH keys) How to find the official Terraform modu..

terraform_relianoid_enterprise_azure_img2
Story
@laura_garcia shared a post, 2ย months, 4ย weeks ago
Software Developer, RELIANOID

๐Ÿš—๐Ÿ”’ Automotive Cybersecurity: Connected Cars, Connected Risks

Modern vehicles are no longer just machines โ€” theyโ€™re connected devices storing data, running AI-driven assistants, and linked to vast supply chains. But with innovation comes new threats: from remote car hacking to ransomware attacks targeting manufacturing lines. Recent incidents โ€” like the cybera..

Blog_Automotive Cybersecurity_relianoid
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.