On Saturday, I finally finished Advent of Code 2018 (and pushed my solutions to GitHub). I did the whole thing in Swift (except for a couple parts which I did by hand), but I tried to avoid Xcode as much as possible, knowing that it would make automating things a whole lot harder. Instead, I ran everything directly in the command line. It took a while to figure out how to do this, with little official documentation on the subject, so I thought I’d explain how I did this so that anyone searching for it would find the information in one place.

I’m going to focus on command-line applications, though I expect the tips will also be useful for those of you looking to make macOS GUI applications.

Last night, I gave a talk at the very first Reproducibility and Productivity in Data Science (“RAPIDS”) meetup on the relative unimportance of code, the state of data management, and what we at Prodo.AI are trying to do about it.

As (almost) always, I wrote the majority of the content ahead of time, so if you weren’t there (and I know you weren’t), please take a look. I think you’ll enjoy it.

It focuses on an open-source tool we’ve been building to help us manage our own data problems, Plz, which I also encourage you to check out for all your data science and machine learning needs.

Here’s how it starts:

There’s a misconception that software developers have.

We think software is important.

Because of this, we often focus the majority of our attention towards the code that makes up the software. You know, the Python, or Java, or Ruby that the programmers write. We spend a lot of time grooming it, making it more maintainable, more readable, and generally prettier.

Unfortunately, we spend almost no time at all regarding the data that flows through our programs. Typically we’re content with a few examples for our test cases, a couple of edge cases so that we can verify our systems work in some sort of staging environment… and that’s about it. We tend to look at “production” data when there’s a bug.

This is kind of funny, when you think about it, because the software isn’t important. The data is.

Let me know your thoughts, and if you do try Plz, please file as many issues as you like. We want to help bring data science into the 21st century.

Check it out.

Originally posted as a Twitter thread.

I’ve been thinking a lot about serverless architecture and I’ve realised what bugs me about it.

This is gonna be a long one. Not gonna lie, I wrote a draft first. If I hadn’t, this thread would end right before it got interesting because I’d get distracted by a butterfly.

At its heart, serverless/FaaS/lambda/whatever-you-call-it works for me because it ties action to cost. The more I do (or the more inefficiently I do it), the more I pay. More users, more money. Gojko Adzic wrote about this years ago. It’s not new.

The problem, I find, is that in doing so, it breaks your application apart. Now we have many applications. They all work with HTTP, or message queues, or some other data interchange layer I don’t care about when I’m designing functionality for a user.