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News FAUN.dev() Team
@devopslinks shared an update, 3 months, 3 weeks ago
FAUN.dev()

Google Introduces Quantum-Safe KEMs in Cloud KMS for Future Security

Cloud KMS

Google introduces quantum-safe KEMs in Cloud KMS to counter future quantum computing threats, urging organizations to transition to post-quantum cryptography.

Google Introduces Quantum-Safe KEMs in Cloud KMS for Future Security
News FAUN.dev() Team
@kala shared an update, 3 months, 3 weeks ago
FAUN.dev()

Red Hat Joins Forces with NVIDIA to Bring CUDA Everywhere

NVIDIA CUDA Toolkit

Red Hat and NVIDIA partner to distribute the NVIDIA CUDA Toolkit across Red Hat platforms, aiming to simplify AI adoption and enhance developer experience.

Red Hat Joins Forces with NVIDIA to Bring CUDA Everywhere
News FAUN.dev() Team
@devopslinks shared an update, 3 months, 3 weeks ago
FAUN.dev()

Amazon to Lay Off 14,000 Workers as Part of 30,000-Job Restructuring

#Layoffs  #Workfor...  #Amazon  #aws  #AI 

Amazon plans to lay off 14,000 employees to streamline operations and boost AI investment, part of a broader strategy affecting up to 30,000 jobs.

Amazon to Lay Off 14,000 Workers as Part of 30,000-Job Restructuring
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@laura_garcia shared a post, 3 months, 3 weeks ago
Software Developer, RELIANOID

RELIANOID will be attending The North European Cyber Days 2025 in Oslo (Nov 4–6)!

We’re proud to support Europe’s cybersecurity and AI ecosystem — driving innovation, resilience, and trusted digital transformation with our high-performance ADC and proxy technologies. #Relianoid#CyberDays2025#CyberSecurity#AI#DigitalResilience#Innovation https://www.relianoid.com/about-us/events/t..

The north european cyber days oslo relianoid
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@elenamia shared a post, 3 months, 3 weeks ago
Technical Consultant, Damco Solutions

Why Enterprises Are Moving from Break-Fix to Proactive Application Maintenance and Support

Discover why forward-thinking enterprises are replacing break-fix models with proactive application maintenance.

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@brooksamybrook shared a post, 3 months, 3 weeks ago
Executive, CMARIX InfoTech

How to Assess and Improve Growth with the AI Maturity Model

AI maturity measures an organization’s ability to generate consistent business value from AI across strategy, data, people, and technology. The AI Maturity Model spans four stages—Initial, Repeatable, Defined, and Optimized—guiding firms from experimentation to full AI integration. Assessing AI maturity helps identify gaps, align investments, and turn AI from scattered projects into a sustainable, strategic advantage.

Assess Your Organization’s AI Maturity Model
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@arunsinghh011 shared a post, 3 months, 3 weeks ago
Business associate, Xcelore Private Limited

Building the Future: The Art of Smart AI Product Development

Have you ever wondered when “AI” stopped being a sci-fi buzzword and started showing up in your morning to-do list? Somewhere between that first predictive email and the chatbot that apologizes better than most customer reps, it happened. Quietly. Suddenly. Like caffeine sneaking into your bloodstream before the day really begins. That’s where AI product development services come in—not as some sterile tech jargon, but as the very engine redefining how we build, think, and work.

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@alexgrave876 shared a post, 3 months, 3 weeks ago
Content writer, Alpharive

The Architecture of Agentic AI: Building Machines That Think, Act, and Evolve

Explore the layered architecture of Agentic AI—how intelligent systems observe, reason, and act within their environments. Learn how perception, reasoning, and action layers create adaptive, self-improving machines built for real-world decision-making.

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@viktoriiagolovtseva shared a post, 3 months, 3 weeks ago

Why Use Atlassian Forge? Benefits, Pricing & Use Cases

Why Atlassian Needed a Modern App Development Platform

Building apps for Jira, Confluence, and other Atlassian products has traditionally been a resource-heavy process. Developers had to configure their own servers, ensure uptime, and pass rigorous security checks to get their apps approved for the Atlassian Marketplace. This setup required both development expertise and operational support, slowing app development and increasing costs.

Atlassian Forge was introduced to eliminate these barriers. It is a modern cloud-based app development platform that allows developers to build secure, serverless apps directly within Atlassian’s infrastructure. Forge simplifies building apps, giving developers more time to focus on functionality, while Atlassian handles hosting, security, and scaling.

This article explains Forge’s core features, key benefits, pricing model, and common use cases. Whether you are a developer considering your first Atlassian app or a team looking to transition from Connect to Forge, this guide will help you decide if Forge is the right solution for your business.

Screenshot 2025-10-30 at 12.16.48
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@viktoriiagolovtseva shared a post, 3 months, 4 weeks ago

Jira For HR: How to Automate HR Processes And Use Checklists in Jira

The more you can automate, the more time you will have for the “H” part of HR—humans. In addition to freeing up time, automation brings you many other benefits. It allows you to build clear and transparent processes, create a smooth employee experience, and improve retention rates.

In this blog post, we explain how to set up various types of automation in Jira for HR management purposes.

Screenshot 2025-10-29 at 15.11.18
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.