This trip to the mountains is a return to a place I belong, but also to a place that I've never been. It's the start of a lifetime of joy. As you probably know by now, I'm obsessed with Colorado. It's my favorite place in the world and it tugs on my heartstrings like almost nothing else. So, what a better way to solidify my life with the one person who does tug on my heartstrings like nothing else? It's a perfect plan and no one ever saw it coming! 
It's been said that the key to any healthy relationship is the ability to see eye-to-eye on life's important issues. The approach one has to politics, to love, to religion, to animals and children (sometimes they're the same thing), and to those less fortunate in society are key metrics to consider when evaluating the strength of your compatability to a significant other. Well, I've been pondering my relationship with Python, and I'm realizing that we just aren't compatible anymore. It's not me, it's you.
Table-driven tests (TDT) are a popular form of unit testing that allow you to rapidly iterate over multiple different inputs to your system in a way that is easily readable and understandable. This post will show my approach to TDT in Python and why all the current implementations of it are insufficient.
robots.txt, or the Robot Exclusion Protocol, is one of the oldest protocols on the web. It's a file, usually stored at the top level of a domain, that provides a list of rules which politely informs web crawlers what they are and are not allowed to do. This simple file is a great insight into the kinds of struggles that web administrators have in maintaining their websites.
https://www.youtube.com/watch?v=q0KGYwNbf-0
I will be taking notes of this video by Clement Mihailescu by listening to the prompts in the video and generating my own design. After I complete my design, I will listen to Clement's design and compare/contrast the differences.
When I design software systems, my modus operandi is generally to design the minimally acceptable set of features required by the business needs. This methodology allows me to be efficient with my time as I am focusing my energy on the known business problems. It also reduces the expectations your users have which is strangely a huge benfit: lower expectations usually translates to lower maintenance burdens. It allows you to focus your effort on the biggest problems your users face.
These are my notes from the GrafanaLive workshop held in the Google offices in Chicago.
In this post, we'll explore Profile Guided Optimizations (PGO) introduced in Go 1.20 and how they can be effectively used to improve the performance of your production applications. PGO is a compiler optimization technique that allows you to tune the performance of your Go applications in an environment- and workload-specific way. The profiles themselves are simple metadata files that contain information on what functions are called, how often they're called, what system calls are used, and generally what the computational profile is of your application. This information can be used by the compiler to better inform what sorts of optimizations are useful in your specific environment and workfload.
In this blog post, we discover how you can analyze what variables the Go compiler decides should escape to the heap, a common source of performance problems in Golang. We'll also show how you can configure the gopls language server in VSCode to give you a Codelens view into your escaped variables.
Systemd administrators often find themselves needing to run services on their bare-metal machines. Services can be broken down into roughly two broad categories:
Long-running services comprise of the majority of the services in use by Linux. One of the challenging aspects of long-running services in a production environment is the dual question of monitoring and reliability: