Text encoding is fundamental to programming. Web sites, user data, and even the code we write are all text. When encoding breaks, it can feel like the floor is falling out from under you. You're cast into a dimension of bitmasks and codepoints. Logs and backtraces are useless. You consider trading your text editor for a hex editor. But there's hope! In this article, Jose Manuél will show us how encoding errors happen, how they're expressed in Ruby, and how to troubleshoot them. So the next time one of these errors lands on your desk, you'll be in a better position to handle it.
When you're evaluating a language for your next project, few things are more important than available third-party libraries and the package manager that ties them together. While early versions of Go lacked a package manager, they've made up for lost time. In this article, Ayooluwa Isaiah introduces us to go's module ecosystem to help us decide if go is "a go" for our next project.
Developers make fun of legacy systems because we're scared of them. We're afraid that the tiniest change will cause the app to break in unexpected ways. We're afraid we won't realize it until a customer complains. One way to combat this fear is through testing. In this article, José Manuel shows us how to retrofit legacy systems with acceptance test suites so we can maintain them with less fear and more confidence.
If you are a software engineer, there's a good chance that deep learning will inevitably become part of your job in the future. Even if you're not building the models that directly use CNNs, you might have to collaborate with data scientists or help business partners better understand what is going on under the hood. In this article, Julie Kent dives into the world of convolutional neural networks and explains it all in a not-so-scary way.
Whoever first said that "the fastest code is no code" must have really liked memoization. After all, memoization speeds up your application by running less code. In this article, Jonathan Miles introduces us to memoization. We'll learn when to use it, how to implement it in Ruby, and how to avoid common pitfalls. Buckle up!
Building a neural network isn't *exactly* like building a human brain, but it's the closest any of us are going to get. In this article Julie Kent introduces us to neural nets as a concept and shows us how to implement a simple one in Ruby.
Few projects are as enticing or as rewarding as creating your own programming language. It's impractical, sure. But as an exercise, it strengthens muscles that most of us don't get to use very often and makes us better all-around developers. In this article — the first in a series — Alex Braha Stoll shows us how to get started building our own toy language and interpreter from scratch using Ruby.
These days fewer and fewer web developers get to specialize in a single language like Ruby. We use different tools for different jobs. In this article, Ayooluwa Isaiah argues that Go is the perfect complement to Ruby. The developer who knows both is in a great position to handle almost any back-end challenge.
We've all been there. You're clicking around your Rails application, and it just isn't as snappy as it used to be. You start searching for a quick-fix and find a lot of talk about caching. Take your existing app, add some caching, and voila, a performance boost with minimal code changes. However, it's not this simple. Like most quick fixes, caching can have long-term costs. In this article, Jonathan Miles discusses what caching is and what can go wrong, as well as explains non-caching strategies you can use to speed up your Rails app.
The world is full of linear relationships. When one apple costs $1 and two apples cost $2, it's easy to figure out the price of any number of apples. But what happens when you have 100s of data points? What if your data source is noisy? That's when it's helpful to use a technique called linear regression. In this article Julie Kent shows us how linear regression works, and walks through a practical example in Ruby.