Have you ever worked with rich text editors? You know how frustrating of an experience that can be. Users enter content without much confidence in how the editor will preserve it. Developers have to harden their sites to handle the unstructured soup that is rich text. Designers see their layout completely destroyed by the cheer variation in shape and length. A recipe for frustration.
It’s time to add some noodles and flavour, give some structure to our bland broth and enjoy rich text again.
Developers love to automate things. Shave yaks. Automation can save tremendous time, but automating complex processes can also be a big time sink. This is a story in compromise.
The other thing developers love is free software. I love GitHub, Travis, and Heroku because they all provide a very generous free usage tier – let’s have a look at how we can leverage them to automate preview environments (think: preview the result of a new pull request) for Wagtail, a project I frequently contribute to.
A disclaimer – the end result isn’t full automation, it still is a manual process. We shall call this semi-automated preview environments.
Ever heard of Draft.js? We are rebuilding Wagtail’s rich text content management using this rich text editor framework from Facebook. Apart from a new editor, Draftail, we also needed an exporter to convert Draft.js content to HTML: the aptly-named draftjs_exporter.
In this post I’ll focus on this critical piece of the puzzle, which needs to operate as fast as possible regardless of how much content it has to handle. Memory profiling and benchmarks in a Python ecosystem were new to me, so here is what I learned along the way.
Ever heard of the Joel Test? It’s a simple 12 question measure of the quality of a software team, introduced by Joel Spolsky of Stack Overflow and Trello fame. The test is famous in the software development community as a no-BS calculation of the maturity of a development team. So, how’d we do?
I’m a self-diagnosed data nerd. For me, there’s nothing like trying to make sense of a phenomenon by looking at data, exploring an uncharted dataset to uncover insights. Sure, I don’t expect everyone to be as excited as I am about the release of the latest seasonally adjusted chain-volume GDP – but I do think that basic data literacy is becoming a vital part of running a successful business, or being an informed citizen.
In New Zealand and the rest of the world, the trend has always leaned towards siloing data in an organisation’s information system. These obscure spreadsheets are now in the public eye, and it’s time to make this data accessible to a larger audience so they can visualise it, analyse it, and explore it, all in the open.
We’re going to look at all the steps of this process in a series of blog posts, starting with an exploration of uncharted datasets to reveal their potential.