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How Netflix optimized its petabyte-scale logging system with

Netflix overhauled its logging pipeline to chew through5 PB/day. The stack now leans onClickHousefor speed andApache Icebergto keep storage costs sane. Out went regex fingerprinting - slow and clumsy. In came aJFlex-generated lexerthat actually keeps up. They also ditched generic serialization in fa.. read more  

How Netflix optimized its petabyte-scale logging system with
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The AI Gold Rush Is Forcing Us to Relearn a Decade of DevOps Lessons

Sauce Labs just dropped a reality check:95% of orgshave fumbled AI projects. The kicker?82% don’t have the QA talent or toolsto keep things from breaking. Even worse,61% of leaders don’t get software testing 101, leaving AI pipelines full of holes - cultural, procedural, and otherwise. System shift:.. read more  

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A Love Letter to FreeBSD

A Linux user takes FreeBSD for a spin - and comes away impressed. What stands out? Clean, deliberate engineering.Boot environmentsmake updates stress-free. The newpkgbasesystem adds modularity without chaos. And the OS treatsuptimenot just as a metric, but as a design goal. The essay makes a solid c.. read more  

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Terraform Workbook - Your Guide to Infra as Code (IaC)

This post outlines the various Terraform project files and their purposes, such as vars.tf for default variable declarations, terraform.tfvars for overriding default variable values, terraform.tf for tfstate backends and provider declarations, version.tf for Terraform version constraints, and .terra.. read more  

Terraform Workbook - Your Guide to Infra as Code (IaC)
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The $1,000 AWS mistake

A missingVPC Gateway Endpointsent EC2-to-S3 traffic through aNAT Gateway, lighting up over$1,000in unnecessary data processing charges. All that for in-region traffic hitting an AWS service. Why? AWS defaulted the route to the NAT Gateway. It only takes the free S3 Gateway Endpoint if youtellit to. .. read more  

The $1,000 AWS mistake
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@kaptain shared an update, 2 weeks, 1 day ago
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Docker Desktop 4.50 Supercharges Daily Development With AI, Security, and Faster Workflows

Docker Docker Compose Kubernetes Docker Desktop

Docker Desktop 4.50 enhances software development with improved debugging, AI integration, and enterprise security features, streamlining workflows and boosting productivity.

Docker Desktop 4.50 Supercharges Daily Development With AI, Security, and Faster Workflows
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@kala shared an update, 2 weeks, 1 day ago
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Guido van Rossum: “AI Should Adapt to Python - Not the Other Way Around”

Python TypeScript

Guido van Rossum discussed Python's enduring relevance in AI and education at GitHub's Octoverse, emphasizing its clarity, accessibility, and community-driven growth despite TypeScript's rise.

Guido van Rossum: “AI Should Adapt to Python - Not the Other Way Around”
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Kubernetes 1.35 new alpha features

Kubernetes

The next Kubernetes release, v1.35, is scheduled for December 17th. It should bring 15 new Alpha features, including the following ones: - Gang scheduling support - Mutable PersistentVolume node affinity - Restart all containers on container exits - Consider terminating Pods in Deployments - CSI vol..

Kubernetes v1.35 release
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NordPass: Worst Passwords of 2025 and How Each Generation Compares

NordPass's latest research reveals the ongoing global reliance on weak passwords like "123456" and "password," despite slight improvements in security practices.

NordPass: Worst Passwords of 2025 and How Each Generation Compares
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Kubernetes v1.35: A Deep Dive Into the Biggest Changes Before the December 17 Release

Kubernetes containerd

Kubernetes v1.35 release removes cgroup v1 and containerd v1.X support, urging admins to migrate to newer versions and adopt enhancements like in-place Pod updates and OCI image volume support.

Kubernetes v1.35: A Deep Dive Into the Biggest Changes Before the December 17 Release
Vertex AI is Google Cloud’s end-to-end machine learning and generative AI platform, designed to help teams build, deploy, and operate AI systems reliably at scale. It unifies data preparation, model training, evaluation, deployment, and monitoring into a single managed environment, reducing operational complexity while supporting advanced AI workloads.

Vertex AI supports both custom models and foundation models, including Google’s Gemini model family. It enables organizations to fine-tune models, run large-scale inference, orchestrate agentic workflows, and integrate AI into production systems with strong security, governance, and observability controls.

The platform includes tools for AutoML, custom training with TensorFlow and PyTorch, managed pipelines, feature stores, vector search, and online and batch prediction. For generative AI use cases, Vertex AI provides APIs for text, image, code, multimodal generation, embeddings, and agent-based systems, including support for Model Context Protocol (MCP) integrations.

Built for enterprise environments, Vertex AI integrates deeply with Google Cloud services such as BigQuery, Cloud Storage, IAM, and VPC, enabling secure data access and compliance. It is widely used across industries like finance, healthcare, retail, and science for applications ranging from recommendation systems and forecasting to autonomous research agents and AI-powered products.