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@faun shared a link, 8 months, 1 week ago
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Amazon EKS Enables Ultra-Scale AI/ML Workloads with Support for 100K Nodes per Cluster

Amazon EKS just cranked its Kubernetes cluster limit to100,000 nodes—a 10x jump. The secret sauce? A reworkedetcdwith an internaljournalsystem andin-memorystorage. Toss in tightAPI server tuningand network tweaks, and the result is wild: 500 pods per second, 900K pods, 10M+ objects, no sweat—even un.. read more  

Amazon EKS Enables Ultra-Scale AI/ML Workloads with Support for 100K Nodes per Cluster
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Kubernetes VPA: Limitations, Best Practices, and the Future of Pod Rightsizing

Kubernetes'Vertical Pod Autoscaler (VPA)tries to be helpful by tweaking CPU and memory requests on the fly. Problem is, it needs to bounce your pods to do it. And if you're also runningHorizontal Pod Autoscaler (HPA)on the same metrics? Now they're fighting over control. VPA sees a narrow slice of .. read more  

Kubernetes VPA: Limitations, Best Practices, and the Future of Pod Rightsizing
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Kubernetes DNS Exploit Enables Git Credential Theft from ArgoCD

A new attack chain messes withKubernetes DNS resolutionandArgoCD’s certificate injectionto swipe GitHub credentials. With the right permissions, a user inside the cluster can reroute GitOps traffic to a fake internal service, sniff auth headers, and quietly walk off with tokens. What’s broken:GitOp.. read more  

Kubernetes DNS Exploit Enables Git Credential Theft from ArgoCD
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The Quiet Revolution in Kubernetes Security

Nigel Douglas discusses the challenges of security in Kubernetes, particularly with traditional base operating systems. Talos Linux offers a different approach with a secure-by-default, API-driven model specifically for Kubernetes. CISOs play a critical role in guiding organizations through the shif.. read more  

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Lucidity turns spotlight onto Kubernetes storage costs

Lucidity has upgraded itsAutoScaler. It now handles persistent volumes on AWS-hosted Kubernetes, automatically scaling storage and reducing waste. The upgrade bringspod-level isolation,fault tolerance, andbulk Linux onboarding. Azure and GCP are next on the list... read more  

Lucidity turns spotlight onto Kubernetes storage costs
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@laura_garcia shared a post, 8 months, 2 weeks ago
Software Developer, RELIANOID

🌐 NIS2 is reshaping cybersecurity compliance across Europe.

At RELIANOID, we are fully aligned and compliant with NIS2 requirements, helping organizations strengthen their security posture. 👉 Explore more: https://www.relianoid.com/security-compliances/relianoid-nis2-compliance/ #NIS2#CyberSecurity#Compliance#Regulation#EUCompliance#InfoSec#DataProtection#Go..

nis2 compliance RELIANOID
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@ketbostoganashvili shared a post, 8 months, 2 weeks ago
Technical Content Writer

Send emails with Vercel and Mailtrap

Next.js Vercel Mailtrap.io

Learn how to integrate Mailtrap with your Vercel-hosted applications to send transactional emails with reliable delivery and comprehensive analytics.

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@ketbostoganashvili shared a post, 8 months, 2 weeks ago
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Send emails with Bolt.new and Mailtrap

Bolt Mailtrap.io

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

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@anjali shared a link, 8 months, 2 weeks ago
Customer Marketing Manager, Last9

APM for Kubernetes: Monitor Distributed Applications at Scale

Understand Kubernetes APM by linking request flows with pod, node, and cluster data to get complete visibility at scale.

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@laura_garcia shared a post, 8 months, 2 weeks ago
Software Developer, RELIANOID

🌊 Load Balancing Smart Wave with RELIANOID — Built for Marine Telemetry

The Smart Wave platform is key for real-time telemetry from offshore buoys, vessels, and coastal stations. But how do you ensure it performs reliably — even over satellite links? We've published a new technical guide showing how to load balance Smart Wave using RELIANOID: ✅ MQTT & TCP ingestion for ..

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Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.