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Self Hostable Multi-Location Uptime Monitoring

Vigilant runs distributed uptime checks with self-registeringGo-based "outposts"scattered across the globe. Each one handles HTTP and Ping, reports back latency by region, and calls home over HTTPS. The magic handshake? Vigilant plays root CA, handing outephemeral TLS certson the fly... read more  

Self Hostable Multi-Location Uptime Monitoring
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Test Automation Structure for Single Code Base Projects

The authors discuss the development of a new automation infrastructure post-merger, leading to a unified automation project that can handle all cultures, languages, and clients efficiently. They chose Playwright over Cypress for its improved resource usage and faster execution times, aligning better.. read more  

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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, 4 days 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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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
Gemini 3 is Google’s third-generation large language model family, designed to power advanced reasoning, multimodal understanding, and long-running agent workflows across consumer and enterprise products. It represents a major step forward in factual reliability, long-context comprehension, and tool-driven autonomy.

At its core, Gemini 3 emphasizes low hallucination rates, deep synthesis across large information spaces, and multi-step reasoning. Models in the Gemini 3 family are trained with scaled reinforcement learning for search and planning, enabling them to autonomously formulate queries, evaluate results, identify gaps, and iterate toward higher-quality outputs.

Gemini 3 powers advanced agents such as Gemini Deep Research, where it excels at producing well-structured, citation-rich reports by combining web data, uploaded documents, and proprietary sources. The model supports very large context windows, multimodal inputs (text, images, documents), and structured outputs like JSON, making it suitable for research, finance, science, and enterprise knowledge work.

Gemini 3 is available through Google’s AI platforms and APIs, including the Interactions API, and is being integrated across products such as Google Search, NotebookLM, Google Finance, and the Gemini app. It is positioned as Google’s most factual and research-capable model generation to date.