Feature flags have equipped software developers to seamlessly roll out and roll back new features with the click of a button.
Due to their design and architecture, feature flags can be adapted and integrated into many languages and frameworks. They can be used with other technologies to enhance or add decoupled functionalities. Using them when scripting and automating your Unreal Engine editor is no exception to this.
GitOps is a framework that combines the practices of DevOps and Git and applies them to infrastructure management. It is not a tool or platform but a set of standards for managing IT infrastructure using Git. Within this realm, feature flags, like those from ConfigCat, can play a crucial role in streamlining workflows, managing releases, and enabling dynamic configurations.
Let’s dive into how ConfigCat and GitOps work together and why this combination is worth considering for your infrastructure and development processes.
Conducting an A/B test experiment is a popular approach to determining which variation of a particular feature works best for your users. This experiment can shape the success of any product or business. Conducting these tests can be tricky and time-consuming, but with the ideal tools, it doesn't have to be.
If you're a Ruby developer, you're in luck. I'll show you how to perform an A/B test experiment in Ruby using ConfigCat feature flags and how to record the results with Amplitude's data analytics platform. Let's get started!
Are you a game developer seeking an efficient way to roll out, manage, and control features in your games? Look no further. With ConfigCat's Unreal Engine SDK, you can seamlessly add feature flags directly into your Unreal Engine projects, allowing you to decouple feature releases from deployments. This not only gives you greater flexibility but also helps streamline your feature development process from start to finish. How does it work in practice? Let's dive in.
As developers, our job often involves regularly releasing new features. But how can we ensure these new features are working properly before safely releasing them to all users? It's simple: by using feature flags!
In a previous post, I covered best practices and tips for using ConfigCat feature flags with Docker. While Docker has advantages for easily sharing and deploying containerized applications, it poses challenges when deploying and managing those containers at scale. This is where Kubernetes comes in. Using feature flags, you can control your Kubernetes deployments and services with a simple click without editing your .yaml config files. Let's take a look together!
Config V2 introduces New Comparators that make it much easier to create precise Targeting Rules by facilitating seamless comparison of texts, arrays, and dates.
Machine learning models are the core building blocks of artificial intelligence. As of this writing, a popular AI chatbot circulating in the media and tech industry is ChatGPT. It uses several large generative language models under the hood and can perform tasks that some might describe as super-human.
This advancement in AI showcases the potential of machine learning models and their transformative impact. With the number of machine learning libraries available on the internet, you can even develop your custom models. What's even better is that you can decouple the features of your model and control how they behave using feature flags.
The primary goal of software developers is to ensure user satisfaction with the features or updates they introduce. However, achieving this goal can be challenging without the right release strategy. The question often asked, then, is, "How can developers be certain that a new update or feature delivers optimal results to end users?"
Two strategies that can be employed to address this concern are staged rollouts and canary releases. These strategies can be implemented using feature flags, and in this article, we explore how ConfigCat, a popular feature flag provider, can be used to perform staged rollouts and canary releases.
For a long time, it was normal to initially release a new feature or update into a test environment. If the feature passed, it was then released to the production environment. While this approach was highly respected and beneficial, it introduced more complexity into software development workflows, and releases took longer to reach end-users. Fortunately, with a mechanism known as feature flagging, you can deploy directly to production and ship releases faster while maintaining reliability.
