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@faun shared a link, 3 months, 2 weeks ago
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v1.34: Pod Replacement Policy for Jobs Goes GA

ThePod replacement policyin Kubernetes v1.34 just hit GA. Jobs can now hold off on spinning up new Pods until the old ones arefullygone. No more duplicates per index. No more blowing through quotas or stalling schedulers—big win for workloads like ML training. System shift:This rewires how Jobs hand.. read more  

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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  

Reduce Cloud Cross-Zone Data Transfer Costs with Kubernetes 1.33 trafficDistribution
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v1.34: PSI Metrics for Graduates to Beta

Kubernetes v1.34 bumpsPressure Stall Information (PSI) metricsto Beta. Now kubelets expose kernel-level resource pressure—CPU, memory, and I/O—through the Summary API and Prometheus. Instead of just tracking how much a resource gets used, PSI shows how often workloads get throttled or blocked. That .. read more  

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@anjali shared a link, 3 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, 3 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, 3 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, 3 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, 3 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, 3 months, 3 weeks 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! , 3 months, 3 weeks ago.
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.