Thoughtfully writing a blog post

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12 Things to consider when deploying HTTP Services

Nicola Adamchik on

Rather than putting all domain logic into a single application monolith, modern software architectures tend to split functionality into multiple applications and services. There are definitely pros and cons of either design; and it is not the goal of this article to go into the details of what is better and why. However, it is generally agreed upon that one of the detractors of using services is the increased complexity that it brings to the table. Specifically, there are many more points of failure, and building a robust system means expecting that nodes of your system may fail at any given point in time. These failures cannot be ignored and you cannot expect to be able to wait until your nodes are all green to operate normally. In order to address some of the added complexity that services introduce, I’ve compiled a checklist of important elements to consider:

Our Git Branching Model 2016

Dave Copeland on

Way back in 2013, I described Stitch Fix’s git flow, as a reaction to the popular post “A Successful Git Branching Model”. Recently, Jussi Judin posted a reaction using the classic considered harmful tag line. I read the post with enthusiasm, but found the process described still too complex. This felt like a good time to refresh my previous post with how Stitch Fix currently does branching and Git (it’s even simpler than it was in 2013).

Will Golden State Cruise to the Title this Year?

Kim Larsen on

Back in September – before the start of the 2015-2016 NBA season – we wrote a post about the Golden State Warriors that was titled “Strength in Numbers: Why Golden State Deserved to Win in All.” The post explored whether Golden State got lucky last season. Were they carried by their momentum and aided by injuries to the opposing teams, or did they simply have the best basketball DNA? Our conclusion was that Golden State did indeed have the best DNA, although the final series against Cleveland would have been a very close series if Kevin Love and Kyrie Irving had been healthy.

NY vs. LA: Who's the Flauntiest of Them All?

Tarek Rached, Eli Bressert, and Zhou Yu on

When it comes to data, Stitch Fix and its customers have a symbiotic relationship. The more information we have about a customer’s preference in clothing, the better we can cater to them with clothes that match their liking. Our customers are aware of this and in turn provide high quality feedback on pricing, color, style, attributes to avoid, and so much more.

Do you know what problem you are trying to solve?

David McClain on

This is a story I will one day tell my grandchildren. But as I have no grandchildren, you, dear reader, will have to do. Now sit down, pop this hard candy in your mouth and look interested.

Thinking Through Heroku's Preboot

Joe Cannatti on

Preboot is a feature provided by Heroku that can help you achieve zero downtime deploys. Meaning that when you push a new version of your code, there’s not even a split second that your users experience your app being down.

Unsupervised Computer Vision: The State of the Art

TJ Torres on

The field of computer vision is rapidly evolving, particularly in the area of unsupervised deep learning. Over the past year or so there have been many new and exciting methods developed to both represent and generate images in an automated fashion, but the field is evolving so rapidly that it can be hard to keep track of all these methods. I recently gave a research talk to the Styling Algorithms team here at Stitch Fix on the current state of the art (as I see it) in unsupervised computer vision research. It was by no means comprehensive, but more of a survey of of interesting methods I thought might be applicable to a problem I have been working on recently: how does one disentangle attributes at the level of a latent image representation?

A Fontastic Voyage: Generative Fonts with Adversarial Networks

TJ Torres on

Two weeks ago there was a lot of buzz around Erik Bernhardsson’s blog post, where he trained an autoencoder on more than 50,000 fonts. The results are fantastic and if you haven’t seen it yet, go check it out. A few months back we released a package called fauxtograph, which performs unsupervised deep learning on images via variational autoencoding (VAE). Less than a week ago we implemented some big changes in fauxtograph where convolutional and adversarial network (GAN) capabilities were added. So how will the updates in the package do with the fonts dataset that Erik shared?

iOS Code Signing - Part 3

Flora Lee on

Update (10/31/2016): We’ve written a newer blog post about how we test, integrate and deploy our iOS app. It complements the information here, and includes up-to-date details about our current process.

There Is no Happy Path in Programming

Dave Copeland on

It’s often expedient to discuss the “happy path”, which is the ideal or most simple flow of logic through a system. While it’s a great tool for conversation and identifying requirements, I’ve found it more and more problematic when thinking through actual implementations, especially when there are distributed systems involved. It’s often better to plan and design for all paths from the start.