• Building a Go template playground with Wasm Apr 30, 2022

    Creating an interactive live parser to experiment with the Go templating language, built using WebAssembly and running the Go standard library directly in your browser.

    This post walks through how I created the [Go Templates Playground][playground]. It pulls Go template rendering capabilities directly into your browser with WebAssembly (Wasm). I was motivated to this after recently seeing the utility of other similar environments out there for different programming languages1. The Go template language has relatively wide adoption in projects like Helm and Hugo, which this tool can be used to prototype and experiment for. I’ve also been interested in incorporating Wasm into a deployed project, and this was a great opportunity for it, as well as chance to play with alternate approaches to help bring what are traditionally backend languages to the browser.

  • Adding search to a static site with Lunr and Preact Feb 4, 2022

    Leveraging the templating capabilities and Javascript rendering capabilities of Hugo to generate a Lunr index, rendered with search as you type using Preact.

    The website you are reading this on is made up of a set of static assets rendered with Hugo. This post walks through adding a basic search feature to this site that runs locally in your browser. I wanted to build out search in a way that aligned with the current statically generated approach, avoiding any server-side logic and executing queries entirely within the browser. Additionally, I wanted to keep the build process for the site simple, so avoiding the addition of any special steps in the local development or production build processes.

  • Building a Neural Network in Go Dec 12, 2021

    Creating a multi-layer perceptron from scratch with just the Go standard library.

    This post covers the creation of a multi-layer neural network written in Go. We will walk through the basics of what neural networks are and how they work, specifically looking at some of the earliest types of feed-forward neural networks. We will then walk through the implementation of a Multi-Layer Perceptron (MLP). Our goal in this process is to create a network that performs well at recognizing handwritten digits on the MNIST dataset.

  • Linking Dynamic C++ Libraries with Go Jun 20, 2015

    Creating a Go wrapper around a cross-platform build process for a dynamically built and linked C++ library

    These days, I spend a lot of time working with, designing, and implementing APIs. Since Meta is a microservices based application, the contracts that those APIs provide are crucial to designing the interactions with them. Quickly, maintaining good documentation and client libraries becomes nearly as important of a part of the applications as the code itself. Each step forward in functionality must provide solid footing to keep on building. A spectacular tool that we have been using is Apiary, a service that provides API documentation through a super set of markdown that is fully parsable, providing mocked APIs and examples through a single set of documentation.