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@faun shared a link, 5 months, 3 weeks ago

FAUN.dev()

Reduce Cloud Cross-Zone Data Transfer Costs with Kubernetes 1.33 trafficDistribution

Kubernetes 1.33 drops a new traffic policy that addszone-local routing. With it, kube-proxy now prefers endpoints in the same availability zone. Translation: less cross-AZ chatter, fewer surprise charges. On AWS, that can chop the usual $0.02/GB cross-AZ fee by up to 85%—especially in EKS clusters j.. read more

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@anjali shared a link, 5 months, 3 weeks ago

Customer Marketing Manager, Last9

Kubernetes Monitoring Metrics That Improve Cluster Reliability

Understand Kubernetes monitoring metrics that help detect issues early, improve reliability, and keep your cluster performing at its best.

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@laura_garcia shared a post, 5 months, 3 weeks ago

Software Developer, RELIANOID

🚀 Strengthening Europe’s Cybersecurity in Space

Just in case you missed it last month: The European Space Agency (ESA) has launched its brand-new Cybersecurity Operations Center (C-SOC) to safeguard satellites, mission control systems, and digital assets against growing cyber threats. 🌍 In today’s space-driven world, initiatives like this — suppo..

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@laura_garcia shared a post, 5 months, 3 weeks ago

Software Developer, RELIANOID

🔐 Cybersecurity Fundamentals: Defensive, Offensive & Hybrid Approaches 🔐

Cybersecurity isn’t just about deploying tools — it’s about knowing how and when to use the right strategies. Defensive security focuses on prevention with technologies like firewalls, antivirus, access control, and system hardening to reduce exposure. Offensive security takes the attacker’s perspec..

Cibersecurity concepts diagram RELIANOID

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@anjali shared a link, 5 months, 3 weeks ago

Customer Marketing Manager, Last9

What is APM Tracing?

Understand APM tracing to see how a request moves through services, helping you spot delays, errors, and bottlenecks quickly.

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@laura_garcia shared a post, 5 months, 3 weeks ago

Software Developer, RELIANOID

✨ In case you missed it ✨

DDoS attacks in 2025 are bigger, smarter, and easier to launch than ever before. From AI-driven attack strategies to IoT-based botnets, the threat landscape is evolving fast. Our latest blog explains what’s happening now — and how RELIANOID helps organizations stay resilient. 🔗 https://www.relianoid..

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@laura_garcia shared a post, 6 months ago

Software Developer, RELIANOID

SourceForge Favorite Award 🏆

We are proud to share that RELIANOID has been recognized with the SourceForge Favorite Award 🏆 This recognition is granted to only a handful of projects out of more than 500,000 open source projects hosted on SourceForge, based on downloads and user engagement. 👉 With nearly 20 million monthly users..

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@kkz7777 gave 🐾 to 🚀 RELIANOID is heading to Washington, DC! , 6 months ago.

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@laura_garcia shared a post, 6 months ago

Software Developer, RELIANOID

🚀 RELIANOID is heading to Washington, DC!

From September 9–12, 2025, we’ll be at the 16th Billington CyberSecurity Summit — the premier government cybersecurity event gathering 2,500+ leaders and innovators to shape the future of AI, Zero Trust, cloud security, and critical infrastructure protection. Meet our team and discover how RELIANOID..

billington cybersecurity summit washington event

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@idjuric660 shared a post, 6 months ago

Technical Content Writer, Mailtrap

Send emails with v0 and Mailtrap

#v0 #v0 Mail... #send em... #transac... #mailtra...

Learn how to integrate Mailtrap with your v0 application to send transactional emails and manage contacts without writing complex code.

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.