Need help executing a GitHub Actions workflow for your Rails application, especially when dealing with a MYSQL database? Whether you’re just starting or transitioning from another CI service, navigating potential pitfalls can be challenging. If you’ve found yourself nodding along, then this blog post is tailored just for you.

Roda is a web toolkit (or framework) that focusses on simplicity, reliability, extensibility, and performance. This is a short post to show the compatibility between Roda and Ruby across different versions.

Welcome to the third article in our Machine Learning with Ruby series!

In our previous article Machine Learning: An Introduction to CART Decision Trees in Ruby, we covered CART decision trees and built a simple tree of our own. We then looked into our first ensemble model technique, Random Forests, in Machine Learning: An Introduction to Random Forests. It is a good idea to review that article before diving into this one.

Random Forests are great for a wide variety of cases, but there are also situations where they don’t perform quite as well. In this article we’ll take a look at another popular tree-based ensemble model: Gradient Boosting.

In our previous article Machine Learning: An Introduction to CART Decision Trees in Ruby, we covered CART decision trees and built a simple tree of our own. Decision trees are very flexible and are a good tool for simple classification, but they are often not enough when it comes to real-world scenarios.

When dealing with large and complex data, or when dealing with data with a significant amount of noise, we need something more powerful. That’s where ensemble models come into play. Ensemble models combine a number of weak learners to build a strong model, with increased accuracy and robustness. Ensembles also help manage and reduce bias and overfitting.

In this article, we’ll cover a very popular tree-based ensemble model: Random Forest.

In the middle of last year, we released an internal tool to help address a pretty significant issue. That is how the Pecas tool was born, and you can read about the Business Case for Pecas here.

Pecas relies on a binary classification machine learning model to classify time entries as valid or invalid. It is a combination of a Django app, that hosts the Slackbot and other data processing tasks, and a FastAPI app that hosts the machine learning model built using the Scikit-learn Python library. Scikit-learn provides a great set of classification models you can use, which are optimized and very robust, making it a solid choice to build your model. However, understanding the principles behind the classification can be a bit tricky, and machine learning models can feel a bit like a black box.

In this series, we’ll explore some principles of machine learning, namely binary classifiers, and walk through how they connect to each other, in Ruby. This article will focus on decision trees, namely CART (Classification And Regression Trees) and a little bit of the mathematics behind them.

One essential tool that we as software developers rely on is known as “test doubles.” These versatile components come in various forms, including dummies, fakes, stubs, spies, and mocks. However, like other power tools, they require careful handling to prevent unintended consequences.

In this post, we’ll explore the strategic use of test doubles at the boundaries of our application, harnessing their full potential while minimizing associated risks.

Are you considering an upgrade for your Rails or Ruby application, but you’re concerned about low test coverage? Don’t worry! In this post, we’ll explore effective strategies to address the risks associated with low test coverage. By implementing these strategies, you’ll be able to upgrade your application with confidence. Let’s dive in!

Upgrading from Ruby 2 to Ruby 3 can be a challenging task, especially when your Rails application relies on ActiveJob with Sidekiq. In the process, you may encounter cryptic ArgumentError exceptions that make the upgrade seem daunting. By following along you’ll be well-equipped to avoid some of the hurdles that come your way.

We, full stack Rails engineers, have come a long way. We started off as full stack engineers with our backend and frontend all in one framework. We had the asset pipeline (with sprockets) to help us maintain this ecosystem. When the time came we introduced webpacker to fill in where sprockets fell short.

In this post we will take a look at how we can take the next step on our journey by migrating from webpacker to esbuild.

In this article we will discuss and demonstrate how we can use Ruby to encode UUIDs into URL friendly representations. This article does not assume any previous knowledge about UUIDs. Instead we will first discuss what exactly a UUID is. We look at all the reasons we would prefer using UUIDs over conventional incremental integers.

You can look forward to some binary math and adding a simple but effective encoding algorithm to your tool belt.