How Do You Know When Your App is Not Compliant?

Ensuring that your company’s website is current with compliance standards is extremely important and essential for any Rails application. Operating with a compliant application guarantees security that can help with handling sensitive data and maintaining users’ trust. The more compliant your website is, the more secure it will be against data breaches, which helps users feel safe when they’re using it.

So what does it take to be compliant? In this article, we will focus on security and cover some indicators to help identify if your Rails app might not be compliant anymore.

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Ruby & Sinatra Compatibility Table

Sinatra is known in the Ruby world for being a lightweight framework for building Ruby web applications with minimal effort.

Over time Sinatra has been through many versions, and sometimes it gets complicated keeping track of which versions of Sinatra are compatible with which versions of Ruby. Therefor, we made a handy chart!

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Dual-Boot Ruby

As we mentioned many times, at FastRuby.io we like to use the Dual-Boot technique during upgrades to quickly test the same code with the current and the next version of what we are upgrading. We usually talk about dual-booting Rails versions but this can be used to upgrade Ruby itself too. We have to make some changes to adapt the technique, and we’ll explain the basic changes in this article.

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Largest Contentful Paint

Is your goal to rank first on Google? Have you already tried using the best keywords and strategies to rank higher but none of that has worked? It might be because your LCP, or Largest Contentful Paint, score is high and needs improvement.

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ReadyTech Accelerates Revenue Growth, Cuts Infrastructure Costs 5-10% with Ruby on Rails Upgrade

Based in Sydney, Australia, ReadyTech combines technology with a people-centric approach to help organisations navigate complexity and deliver meaningful outcomes. With more than 500 employees globally, the publicly-traded company continues to grow rapidly and now serves 4,000+ customers across three vertical segments: Education & Work Pathways, Workforce Solutions, and Government & Justice.

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How To Delay A Rails Upgrade

Maintaining an up-to-date Ruby on Rails application is crucial for performance, security, and feature enhancements. However, finding the time for a full-scale upgrade can be challenging. In this blog post, we’ll explore practical steps you can take to delay a Rails upgrade when you don’t have the immediate bandwidth but want to set the stage for a seamless process in the future.

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Machine Learning: An Introduction to Gradient Boosting

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.

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Machine Learning: An Introduction to Random Forests

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.

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Machine Learning: An Introduction to CART Decision Trees in Ruby

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.

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Rails Performance Unveiled: Identifying Common Culprits

When it comes to improving application performance and areas to focus on, I would recommend looking at the APM data, and then deciding which areas to prioritize.

However this article isn’t about where to focus efforts, but rather a compilation of techniques to improve your application’s performance, from tackling common problems like N+1 queries and database indexing to leveraging the jemalloc memory allocator. Let’s look at these performance-boosting strategies designed to fine-tune your application.

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