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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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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
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Building serverless applications with Rust on AWS Lambda

AWS Lambda just bumpedRusttoGeneral Availability- production-ready, SLA covered, and finally with full AWS Support. Deploy withCargo Lambda. Wire it into your stack usingAWS CDK, which now has a dedicated construct to spin up HTTP APIs with minimal fuss. System-level shift:Serverless isn't just for .. read more  

Building serverless applications with Rust on AWS Lambda
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What if you don't need MCP at all?

MostMCP serversstuffed into LLM agents are overcomplicated, slow to adapt, and hog context. The post calls them out for what they are: a mess. The alternative? Scrap the kitchen sink. UseBash, leanNode.js/Puppeteer scripts, and a self-bootstrappingREADME. That’s it. Agents read the file, spin up the.. read more  

What if you don't need MCP at all?
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How to write a great agents.md: Lessons from over 2,500 repositories

A GitHub Copilot feature allows for custom agents defined inagents.mdfiles. These agents act as specialists within a team, each with a specific role. The success of an agents.md file lies in providing a clear persona, executable commands, defined boundaries, specific examples, and detailed informati.. read more  

How to write a great agents.md: Lessons from over 2,500 repositories
TruffleHog is a high-accuracy secret-detection tool designed to uncover exposed credentials such as API keys, tokens, private keys, and cloud secrets across large codebases. Originally created to scan Git commit history, it has evolved into a multi-source scanning engine capable of analyzing GitHub, GitLab, Bitbucket, Docker images, file systems, Terraform states, and cloud environments.

The scanner combines entropy detection, an extensive library of regular expression detectors, and live credential validation to minimize false positives. TruffleHog is widely used in security research, supply chain security, DevSecOps workflows, and bug bounty programs. Its speed, accuracy, and broad ecosystem coverage make it a core tool for identifying and preventing credential leakage in modern software development.