Terragrunt vs Terraform: Why I chose Terragrunt to eliminate code duplication, automate state management, orchestrate deployments, and follow pattern-level reuse

As applications grow, the question quickly shifts from what OTel can do to how we can deploy it effectively at scale. In this post, we’ll explore some deployment patterns for the OTel Collector!

Renovate is an automated dependency update tool. It helps to update dependencies in your code without needing to do it manually. When Renovate runs on your repo, it looks for references to dependencies (both public and private) and, if there are newer versions available, Renovate can create pull requests to update your versions automatically.

Walrus is a distributed message streaming platform built on a high-performance log storage engine. It provides fault-tolerant streaming with automatic leadership rotation, segment-based partitioning, and Raft consensus for metadata coordination.

simple terminal UI for git commands

Image management and caching for Kubernetes.

We're building a small registry to make image management in Kubernetes easy. The Trow Registry runs inside the cluster with very little resources, and is simple to set-up so it caches every image.

Runme is a tool that makes runbooks actually runnable, making it easier to follow step-by-step instructions. Shell/Bash, Python, Ruby, JavaScript/TypeScript, Lua, PHP, Perl, and many other runtimes are supported via Runme's shebang feature. Runme allows users to execute instructions, check intermediate results, and ensure the desired outputs are achieved. This makes it an excellent solution for runbooks, playbooks, and documentation that requires users to complete runnable steps incrementally—making operational docs reliable and much less susceptible to bitrot.

Runme achieves this by literally running markdown. More specifically, Runme runs your commands (shell, bash, zsh) or code inside your fenced code blocks. It's 100% compatible with your programming language's task definitions (Makefile, Gradle, Grunt, NPM noscripts, Pipfile or Deno tasks, etc.) and markdown-native. Much like a terminal session, environment variables are retained across execution, and it is possible to pipe previous cells' output into successive cells. Runme persists your runbooks in markdown, which your docs are likely already using.

The lazygit of SQL databases. Connect to Postgres, MySQL, SQL Server, SQLite, Supabase, Turso, and more from your terminal in seconds.

Simple DNS over HTTPS cli client for cloudflare

The Cloudflare platform is a critical system for Cloudflare itself. We are our own Customer Zero – using our products to secure and optimize our own services.

Within our security division, a dedicated Customer Zero team uses its unique position to provide a constant, high-fidelity feedback loop to product and engineering that drives continuous improvement of our products. And we do this at a global scale — where a single misconfiguration can propagate across our edge in seconds and lead to unintended consequences. If you've ever hesitated before pushing a change to production, sweating because you know one small mistake could lock every employee out of critical application or take down a production service, you know the feeling. The risk of unintended consequences is real, and it keeps us up at night.

This presents an interesting challenge: How do we ensure hundreds of internal production Cloudflare accounts are secured consistently while minimizing human error?

While the Cloudflare dashboard is excellent for observability and analytics, manually clicking through hundreds of accounts to ensure security settings are identical is a recipe for mistakes. To keep our sanity and our security intact, we stopped treating our configurations as manual point-and-click tasks and started treating them like code. We adopted “shift left” principles to move security checks to the earliest stages of development.

This wasn't an abstract corporate goal for us. It was a survival mechanism to catch errors before they caused an incident, and it required a fundamental change in our governance architecture.

Managing a handful of GitHub repositories is straightforward. Managing hundreds of them consistently is a challenge. Over the years at DNSimple, we've evolved from manual configuration to a fully automated Infrastructure as Code (IaC) approach. This is the story of that evolution, the lessons we learned, and how we built a system that now manages all our GitHub resources through pull requests and CI/CD pipelines.

At DNSimple, we've managed our internal infrastructure as code since day one, primarily using Chef for configuration management. Infrastructure as Code wasn't new to us, it was the foundation of how we operated. The challenge was applying these same principles to externally managed resources like GitHub repositories, which required a different approach than our traditional internal infrastructure management.

Stacked PRs. Stacked diffs. Stacked changes.
A better workflow to manage pull requests.

When your application ships to production, it becomes partly opaque. You own the code, but the runtime, network, and platform behaviors often fall outside your direct line of sight. That’s where Monitoring and Observability come in.

Monitoring warns you when predefined thresholds break. Observability lets you explore unknowns, asking new questions in real time and getting meaningful answers without redeploying.

For engineers running software in production, observability rests on three pillars: logs, metrics, and traces. Each offers a different lens into system behavior. Understanding where each excels and where it doesn’t is essential for building a practical, scalable visibility strategy.

Every Infrastructure as Code tutorial starts the same way: provision a single S3 bucket, create one EC2 instance, deploy a basic load balancer. The examples are clean, simple, and elegant. You follow along, everything works, and you feel like you understand Terraform.

Then you get to your actual production environment, and everything changes.

You’re not starting from scratch with a blank AWS account. You’ve got existing resources that were manually created two years ago by someone who left the company. There’s brownfield infrastructure everywhere with no clear documentation. You need to import existing state, figure out what’s actually running, and somehow wrangle it all into code without breaking production. On top of that, you need to manage 200 instances across dev, staging, and production environments. Multiple AWS accounts with different configurations and permissions. Three regions for disaster recovery. Azure for the legacy workloads that nobody wants to touch. GCP running your GKE clusters for the containerized applications.

Suddenly that elegant tutorial code becomes a nightmare of orchestration, state management, environment-specific configurations, and brownfield complexity. You’re not just writing infrastructure code anymore. You’re trying to organize, orchestrate, and maintain it at scale while dealing with the reality that infrastructure is messy, evolving, and full of historical baggage.

This is the scale gap, and it’s where the KISS vs DRY debate stops being theoretical and starts costing real time, money, and engineering effort.

PostgreSQL extension for BM25 relevance-ranked full-text search. Postgres OSS licensed.

The pgEdge Postgres Model Context Protocol (MCP) server enables SQL queries against PostgreSQL databases through MCP-compatible clients like Claude Desktop. The Natural Language Agent provides supporting functionality that allows you to use natural language to form SQL queries.

Modern Docker Management, Designed for Everyone