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How when AWS was down, we were not

During the AWS us-east-1 meltdown - when DynamoDB, IAM, and other key services went dark - Authress kept the lights on. Their trick? A ruthless edge-first, multi-region setup built for failure. They didn’t hope DNS would save them. They wired in automated failover, rolled their own health checks, an.. read more  

How when AWS was down, we were not
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Collaborating with Terraform: How Teams Can Work Together Without Breaking Things

When working with Terraform in a team environment, common issues may arise such as state locking, version mismatches, untracked local applies, and lack of transparency. Atlantis is an open-source tool that can help streamline collaboration by automatically running Terraform commands based on GitHub .. read more  

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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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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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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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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, 5 months ago
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Docker Desktop 4.50 Supercharges Daily Development With AI, Security, and Faster Workflows

Docker Docker Desktop Docker Compose Kubernetes

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
Slurm Workload Manager is an open-source, fault-tolerant, and highly scalable cluster management and scheduling system widely used in high-performance computing (HPC). Designed to operate without kernel modifications, Slurm coordinates thousands of compute nodes by allocating resources, launching and monitoring jobs, and managing contention through its flexible scheduling queue.

At its core, Slurm uses a centralized controller (slurmctld) to track cluster state and assign work, while lightweight daemons (slurmd) on each node execute tasks and communicate hierarchically for fault tolerance. Optional components like slurmdbd and slurmrestd extend Slurm with accounting and REST APIs. A rich set of commands—such as srun, squeue, scancel, and sinfo—gives users and administrators full visibility and control.

Slurm’s modular plugin architecture supports nearly every aspect of cluster operation, including authentication, MPI integration, container runtimes, resource limits, energy accounting, topology-aware scheduling, preemption, and GPU management via Generic Resources (GRES). Nodes are organized into partitions, enabling sophisticated policies for job size, priority, fairness, oversubscription, reservation, and resource exclusivity.

Widely adopted across academia, research labs, and enterprise HPC environments, Slurm serves as the backbone for many of the world’s top supercomputers, offering a battle-tested, flexible, and highly configurable framework for large-scale distributed computing.