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@kaptain shared a link, 1 month ago
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What has Docker become?

Docker’s not just about containers anymore. It’s pivoting hard into AI infrastructure - with some teeth. The newModel Runner,GPU offloading, and fresh AI-native integrations with Google Cloud and Vercel show where it’s headed: less dev environment, more AI runtime engine. Under the hood, Docker drop.. read more  

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Dockhand - The Ultimate Self-Hosted Docker Management Tool

Dockhand just dropped, and it's aiming straight at the bloated SaaS stack. It’s a fully self-hosted Docker management tool with zero license walls. Local or remote? Doesn’t matter. It even plays nice behind NAT using outbound WebSocket agents. You get container lifecycle controls, a visual Compose e.. read more  

Dockhand - The Ultimate Self-Hosted Docker Management Tool
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v1.35: Mutable PersistentVolume Node Affinity (alpha)

Kubernetes 1.35 (alpha) cracks openPersistentVolume node affinity. You can now update it on the fly. Before, it was locked down - once set, it stayed set. That got in the way of shifting workloads when disks were upgraded or moved across zones. Now? More flexibility. Less pain... read more  

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@kala shared a link, 1 month ago
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How to build a Frontend for LangChain Deep Agents with CopilotKit!

LangChain recently introduced Deep Agents: a new way to build structured, multi-agent systems that can plan, delegate, and reason across multiple steps. It comes with built-in planning, a filesystem for context, and subagent spawning. But connecting that agent to a real frontend is still surprisingl.. read more  

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How to Train an AI Agent for Command-Line Tasks with Synthetic Data and Reinforcement Learning

NVIDIA shows how to fine-tuneNemotron-Nano-9B-V2to handle new CLI tools - without touching real user data. The trick? A mix ofsynthetic data,reinforcement learning with verifiable rewards (RLVR), and their home-grown trainer stack:NeMo GymplusGRPO. The result: an LLM agent that adapts fast, plays ni.. read more  

How to Train an AI Agent for Command-Line Tasks with Synthetic Data and Reinforcement Learning
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The Rise of GPUOps: Where Infrastructure Meets Thermodynamics

GPU demand for AI has shot up 600% since 2020. It’s outpaced the cloud abstractions devs rely on - highlighting a growing gap between slick DevOps dashboards and the gritty realities of heat, cost, and silicon. EnterGPUOps. It's not just a trend - it’s a new layer in the stack. Think observability w.. read more  

The Rise of GPUOps: Where Infrastructure Meets Thermodynamics
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Don't fall into the anti-AI hype

The writer recently left their job to explore AI and programming through various projects, including creating a YouTube channel focused on these topics. They discuss how AI is changing the landscape of programming, allowing for faster, more efficient coding methods. Despite concerns about job displa.. read more  

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How we built an AI SRE agent that investigates like a team of engineers

Datadog just droppedBits AI SRE, an autonomous agent that thinks more like an SRE than a chatbot. It doesn't just regurgitate summaries - it investigates. It builds hypotheses, tests them against telemetry, and chases down actual root causes. Older tools leaned hard on LLMs to summarize alerts. That.. read more  

How we built an AI SRE agent that investigates like a team of engineers
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Preparing for Post-Quantum Cryptography

NIST locked in itsPost-Quantum Cryptography (PQC) standardsin August 2024. The countdown’s on: U.S. federal systems need to make the leap by 2035. Wiz jumped early with aPQC Security Framework. It scans for shaky encryption, maps your crypto assets, and flags what’s PQC-ready, all cloud-wide, using .. read more  

Preparing for Post-Quantum Cryptography
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What came first: the CNAME or the A record?

A recent change to 1.1.1.1 accidentally altered the order of CNAME records in DNS responses, breaking resolution for some clients. This post explores the technical root cause, examines the source code of affected resolvers, and dives into the inherent ambiguities of the DNS RFCs... read more  

What came first: the CNAME or the A record?
k3d is an open-source utility designed to simplify running Kubernetes locally by wrapping K3s (Rancher’s lightweight Kubernetes distribution) inside Docker containers. Instead of creating virtual machines, k3d uses Docker as the execution layer, allowing developers to spin up multi-node Kubernetes clusters in seconds using minimal system resources.

k3d is especially popular for local development, CI pipelines, demos, and testing Kubernetes-native applications. It supports advanced setups such as multi-node clusters, load balancers, custom container registries, port mappings, and volume mounts, while remaining easy to tear down and recreate.

Because it uses K3s, k3d inherits a simplified control plane, bundled components, and reduced memory footprint compared to full Kubernetes distributions. This makes it ideal for developers who want a realistic Kubernetes environment without the overhead of tools like Minikube or full VM-based clusters.

k3d integrates cleanly with common Kubernetes workflows and tools such as kubectl, Helm, Skaffold, and Argo CD. It is frequently used to validate manifests, test Helm charts, and simulate production-like environments locally before deploying to cloud or on-prem clusters.