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@moronizzz started using tool Go , 1 week ago.

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@moronizzz started using tool GNU/Linux , 1 week ago.

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@moronizzz started using tool Amazon Web Services , 1 week ago.

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@lanpotocnik07-dotcom started using tool RevolverMaps , 1 week, 1 day ago.

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@laura_garcia shared a post, 1 week, 2 days ago

Software Developer, RELIANOID

𝗔 𝗡𝗲𝘄 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗣𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗕𝗲𝗴𝗶𝗻𝘀: 𝗥𝗘𝗟𝗜𝗔𝗡𝗢𝗜𝗗 𝘅 𝗢𝗫𝗬 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀

We are thrilled to announce that 𝗢𝗫𝗬 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 — 𝘔𝘢𝘭𝘢𝘺𝘴𝘪𝘢’𝘴 𝘕𝘰.𝟭 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘺 𝘚𝘰𝘭𝘶𝘵𝘪𝘰𝘯 𝘗𝘳𝘰𝘷𝘪𝘥𝘦𝘳 — is now RELIANOID’s exclusive partner in Malaysia. This week, we had the pleasure of welcoming part of the 𝗢𝗫𝗬 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝘁𝗲𝗮𝗺 𝘁𝗼 𝗦𝗲𝘃𝗶𝗹𝗹𝗲, where we shared valuable discussions about the future, explored new..

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@laura_garcia shared a post, 1 week, 3 days ago

Software Developer, RELIANOID

🔒 The Future of Cyber Security – Newcastle 2025

📍 November 18 | The Glasshouse, Gateshead RELIANOID joins top experts and leaders at this one-day event to explore the UK’s evolving cyber defence strategy — from AI-driven threats to major breach lessons. Meet our team and discover how we strengthen digital resilience with intelligent ADC and secur..

the future of cybersecurity newcastle relianoid

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@prolixalias started using tool Python , 1 week, 4 days ago.

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@prolixalias started using tool NVIDIA CUDA Toolkit , 1 week, 4 days ago.

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@prolixalias started using tool Kubernetes , 1 week, 4 days ago.

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@prolixalias started using tool GNU/Linux , 1 week, 4 days ago.

Kueue is a Kubernetes-native job queueing and workload management system designed for large-scale, mixed compute environments such as AI/ML training, batch workloads, and HPC workflows. Instead of scheduling individual Pods, Kueue operates at the job level, deciding when a job should run based on resource quotas, fair-sharing policies, cluster availability, and workload priorities.

Kueue integrates tightly with Kubernetes, working alongside the default scheduler rather than replacing it. It provides features such as all-or-nothing (gang) admission, workload preemption, quota-based sharing across teams or tenants, and support for advanced frameworks like JobSet and Ray. Its goal is to help Kubernetes clusters run efficiently under heavy load while ensuring that critical, latency-sensitive, or large training jobs receive the resources they need without starving lower-priority workloads.