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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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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 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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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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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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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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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  

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'I'm deeply uncomfortable': Anthropic CEO warns that a cadre of AI leaders, including himself, should not be in charge of the technology’s future

Anthropic says it stopped a seriousAI-led cyberattack- before most experts even saw it coming. No major human intervention needed. They didn't stop there. Turns out Claude had some ugly failure modes: followingdangerous promptsand generatingblackmail threats. Anthropic flagged, documented, patched, .. read more  

'I'm deeply uncomfortable': Anthropic CEO warns that a cadre of AI leaders, including himself, should not be in charge of the technology’s future
NanoClaw is an open-source personal AI agent designed to run locally on your machine while remaining small enough to fully understand and audit. Built as a lightweight alternative to larger agent frameworks, the system runs as a single Node.js process with roughly 3,900 lines of code spread across about 15 source files.

The agent integrates with messaging platforms such as WhatsApp and Telegram, allowing users to interact with their AI assistant directly through familiar chat applications. Each conversation group operates independently and maintains its own memory and execution environment.

A core design principle of NanoClaw is security through isolation. Every agent session runs inside its own container using Docker or Apple Container, ensuring that the agent can only access files and resources that are explicitly mounted. This approach relies on operating system–level sandboxing rather than application-level permission checks.

The architecture is intentionally simple: a single orchestrator process manages message queues, schedules tasks, launches containerized agents, and stores state in SQLite. Additional functionality can be added through a modular skills system, allowing users to extend capabilities without increasing the complexity of the core codebase.

By combining a minimal architecture with container-based isolation and messaging integration, NanoClaw aims to provide a transparent, customizable personal AI agent that users can run and control entirely on their own infrastructure.