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Docker Workbook - Your Guide to Containerization

This guide cuts through modern Docker workflows. It coversBuildKitfor faster, smarter builds. Shows howmulti-stage Dockerfilesmake images slimmer. Breaks down howENTRYPOINTandCMDactually work. Walks through usingsupervisordto wrangle multi-process containers. Then zooms out toDocker Compose, where l.. read more  

Docker Workbook - Your Guide to Containerization
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How Kubernetes Became the New Linux

AWS just handed overKarpenterandKubernetes Resource Orchestrator (Kro)to Kubernetes SIGs. Big move. It's less about AWS-first, more about playing nice across the ecosystem. Kroauto-spins CRDs and microcontrollers for resource orchestration.Karpenterhandles just-in-time node provisioning - leaner, fa.. read more  

How Kubernetes Became the New Linux
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How I Cut Kubernetes Debugging Time by 80% With One Bash Script

The reality of Kubernetes troubleshooting: 80% of the time is spent locating the issue, while only 20% is used for the fix. Managing eight Kubernetes clusters highlighted this pattern. A tool was developed to provide a complete cluster health report in under a minute, streamlining the process and sa.. read more  

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Kubernetes Tutorial For Beginners [72 Comprehensive Guides]

The series dives deep into real-world Kubernetes - starting with hands-on setup viaKubeadmandeksctl, then moving throughmonitoring,logging,CI/CD, andMLOps. It tracks key release changes up tov1.30, including the confirmed death ofDockershimsince v1.24... read more  

Kubernetes Tutorial For Beginners [72 Comprehensive Guides]
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The guide to kubectl I never had.

Glasskube dropped a thorough guide tokubectl- the commands, the flags (--dry-run, etc.), how to chain stuff together, and how to keep your config sane. Bonus: a solid roundup ofkubectl plugins. Think observability (like K9s), policy checks, audit trails, and Glasskube’s take on declarative package m.. read more  

The guide to kubectl I never had.
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Top 5 hard-earned lessons from the experts on managing Kubernetes

Running Kubernetes in production isn’t just clicking “Create Cluster.” It means locking down RBAC, tightening up network policy, tracking autoscaling metrics, and making sure your images don’t ship with surprises. Managed clusters help get you started. But real workloads need more: hardened configs,.. read more  

Top 5 hard-earned lessons from the experts on managing Kubernetes
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20x Faster TRL Fine-tuning with RapidFire AI

RapidFire AI just dropped a scheduling engine built for chaos - and control. It shards datasets on the fly, reallocates as needed, and runs multipleTRL fine-tuning configs at once, even on a single GPU. No magic, just clever orchestration. It plugs into TRL withdrop-in wrappers, spreads training acr.. read more  

20x Faster TRL Fine-tuning with RapidFire AI
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Code execution with MCP: building more efficient AI agents

Code is taking over MCP workflows - and fast. With theModel Context Protocol, agents don’t just call tools. They load them on demand. Filter data. Track state like any decent program would. That shift slashes context bloat - up to 98% fewer tokens. It also trims latency and scales cleaner across tho.. read more  

Code execution with MCP: building more efficient AI agents
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Practical LLM Security Advice from the NVIDIA AI Red Team

NVIDIA’s AI Red Team nailed three security sinkholes in LLMs:reckless use ofexec/eval,RAG pipelines that grab too much data, andmarkdown that doesn't get cleaned. These cracks open doors to remote code execution, sneaky prompt injection, and link-based data leaks. The fix-it trend:App security’s lea.. read more  

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Hacking Gemini: A Multi-Layered Approach

A researcher found a multi-layer sanitization gap inGoogle Gemini. It let attackers pull off indirect prompt injections to leak Workspace data - think Gmail, Drive, Calendar - using Markdown image renders across Gemini andColab export chains. The trick? Sneaking through cracks between HTML and Markd.. read more  

INTELLECT-3 is a frontier-class 100B+ Mixture-of-Experts language model developed by Prime Intellect and trained end-to-end using their large-scale asynchronous RL framework, PRIME-RL. Built on the GLM-4.5-Air base model, INTELLECT-3 combines supervised fine-tuning with long-horizon reinforcement learning across hundreds of verifier-backed environments spanning math, code, science, logic, and agentic tasks.

The model was trained on a high-performance cluster of 512 NVIDIA H200 GPUs across 64 nodes, supported by Prime Intellect’s Sandboxes execution engine, deterministic compute orchestration, and Lustre-backed distributed storage. The result is a model that surpasses many larger systems in reasoning benchmarks while remaining fully open-source.

Prime Intellect released not only the model weights but also the full training recipe: PRIME-RL, Verifiers, the Environments Hub, datasets, and evaluation suites. INTELLECT-3 is positioned as a foundation for organizations seeking to post-train or customize their own frontier-grade models without relying on proprietary AI labs.