DevOps&SRE Library

Terratag is a CLI tool allowing for tags or labels to be applied across an entire set of Terraform files. Terratag will apply tags or labels to any AWS, GCP and Azure resources.

croc is a tool that allows any two computers to simply and securely transfer files and folders. AFAIK, croc is the only CLI file-transfer tool that does all of the following:

- allows any two computers to transfer data (using a relay)
- provides end-to-end encryption (using PAKE)
enables easy cross-platform transfers (Windows, Linux, Mac)
- allows multiple file transfers
- allows resuming transfers that are interrupted
- local server or port-forwarding not needed
- ipv6-first with ipv4 fallback

TL;DR: In this guide, you will learn how to create clusters on the AWS Elastic Kubernetes Service (EKS) with eksctl and Terraform. By the end of the tutorial, you will automate creating three clusters (dev, staging, prod) complete with the ALB Ingress Controller in a single click.

Whispers is a static code analysis tool designed for parsing various common data formats in search of hardcoded credentials and dangerous functions. Whispers can run in the CLI or you can integrate it in your CI/CD pipeline.

an SSO solution for Nginx using the auth_request module.

Gitleaks is a SAST tool for detecting hardcoded secrets like passwords, api keys, and tokens in git repos. Gitleaks aims to be the easy-to-use, all-in-one solution for finding secrets, past or present, in your code.

At Glitch we’ve recently completed a project to migrate to SLO-based alerts. It’s too early to tell if this has been a success or not, but in this post I’ll write about our motivation for going down this route, and give an introduction to all the concepts you need to know, should you want to give it a go as well.

What is CML? Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.