DevOps&SRE Library

Kubedock is a minimal implementation of the docker api that will orchestrate containers on a kubernetes cluster, rather than running containers locally. The main driver for this project is to run tests that require docker-containers inside a container, without the requirement of running docker-in-docker within resource heavy containers.

The open-source framework for AI SRE agents, and the training and evaluation environment they need to improve. Connect the 60+ tools you already run, define your own workflows, and investigate incidents on your own infrastructure.

Paneru is a MacOS window manager that arranges windows on an infinite strip, extending to the right. A core principle is that opening a new window will never cause existing windows to resize, maintaining your layout stability.

An infinite canvas for your code, terminals, browsers, docs, and AI agents.

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines.

Slumber is a TUI (terminal user interface) HTTP client. Define, execute, and share configurable HTTP requests.

“In the age of AI, taste is the ultimate differentiator.”

mq is a command-line tool that processes Markdown using a syntax similar to jq.

It's written in Rust, allowing you to easily slice, filter, map, and transform structured data.

You’re in a 1:1 with your manager, and things are going just fine. You talk about the project and that other thing. Toward the end, she asks: “Anything else?”

And there is something else. You want to lead that new initiative. Or move to a different team. Or you’ve been thinking about what stands in the way of your promotion. The thought is right there, sitting in the back of your throat. You’re going to say it, and then… “Nope, all good.”

You get out of the call feeling a specific kind of regret. You rationalize it somehow and then tell yourself you’ll bring it up next time (you won’t).

High-severity incidents expose how a team really works together, usually within the first ten minutes.

When working on code with GenAI assistance you need a better understanding of the system than when working without.

When thinking about Service Level Objectives (SLOs) and contractual Service Level Agreements (SLAs) for availability, I always like to put the percentages into concrete numbers.

Benchmarking is hard. There are many ways to do it wrong and few to do it right.

But zooming out from any single system or harness, there are broad principles that should be applied to all benchmarking. Using these correctly makes it difficult to produce biased results.

Am I the world's best benchmarker? Certainly not. I invented the language balls, after all. But correctness and precision are important parts of PlanetScale's culture. We've spent considerable time learning the art of benchmarking, and are here to share best-practices.

Here, we're focusing primarily on benchmarking databases, but these principles apply to many domains.

AI tools have genuinely changed how fast teams can produce code, but they haven't changed how fast a codebase can safely absorb that code.

The real learning comes from analyzing the incident while writing the document, not reading it; the document at the end is the residue of the learning.

A single front door for all your AI agents and MCPs

SlimFaas is a lightweight, plug-and-play Function-as-a-Service (FaaS) platform for Kubernetes (and Docker-Compose / Podman-Compose).

How I turned four Proxmox nodes, some enterprise surplus drives, and an afternoon into a fully automated HA k3s cluster with Rancher, Traefik, and Ansible — all running on hardware that draws less power than a gaming PC.

This post focuses on three areas that separate a demo from production systems: backups, recovery and connection pooling.

Workflow DevKit lets you write durable, long-running workflows directly in your Next.js and Node.js apps. You define steps with ’use step’, and the SDK handles persistence, retries, and replay automatically. Workflows survive server restarts, can sleep for days, and resume exactly where they left off.

On Vercel, all of this works out of the box — the platform handles deployment versioning and queue routing behind the scenes. But what happens when you deploy to your own Kubernetes cluster? Version mismatch. And it’s subtle enough to corrupt data before you notice.

We built Platformatic World to fix this. It’s a drop-in World implementation that brings the same deployment safety to any Kubernetes cluster. Every workflow run is pinned to the code version that created it. Queue messages are routed to the correct versioned pods. Old versions stay alive until all their in-flight runs are complete.