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!
As the world becomes increasingly digital and technology advances, so too do the threats. As a result, we must adapt and learn how to protect and safeguard our online presence, making robust cybersecurity measures more vital than ever before. This is especially important for companies, which generally have more at stake than individuals, necessitating a shift from outdated methods to new strategies to effectively combat cyber attacks.
In this post, I'll investigate integrating ConfigCat's feature management system with an ASP.NET Core 8 Web API Service and the Options<T> pattern. We'll also leverage the built-in polling mechanism of the ConfigCat client library to refresh feature flags' states during the application's runtime. Let's get started!
In this post, I'm going to be demonstrating how to integrate ConfigCat's feature flags in a React.js app. I will build a simple, pretty easy-to-understand application that simulates a race between three cars. Let's get started!
We'd like to share the story of how we successfully rolled out Config V2, the latest and most advanced version of ConfigCat. Let us take you behind the scenes and show you how we used our own tool to achieve a smooth and gradual rollout. Join us as we explain the steps we took to launch Config V2 and demonstrate how we "eat our own dog cat food".
Config V2 introduces New Comparators that make it much easier to create precise Targeting Rules by facilitating seamless comparison of texts, arrays, and dates.
In the ever-evolving world of software development, the ability to create personalized and dynamic user experiences is paramount. This article explores how InfluxDB, a prominent time-series database solution, leveraged ConfigCat's feature flags to enhance their Cloud 2 user testing processes. The insights shared in this article are based on a video presentation by Gavin Cabbage from InfluxDB.
The rise of AI in software development is reshaping the industry's landscape, redefining roles, and setting new benchmarks for efficiency, innovation, and creativity. Gone are the days when software development was exclusively the domain of developers meticulously crafting code, with the process steeped in manual oversight and prone to human error. The introduction of AI into this sphere has been akin to when factories started using assembly lines in manufacturing—revolutionizing productivity and output quality.
AI's role in software development has swiftly moved from a supportive backdrop to a central player, taking on tasks that once consumed countless hours of human labor. Its capabilities have snowballed, from analyzing vast data to identifying patterns, automating repetitive tasks, and even preempting developer actions by suggesting improvements and debugging possible errors or bugs. It has enabled developers to transcend traditional limitations, empowering them to innovate at an unprecedented pace and scale. Hence, AI has become a keystone in the quest for efficiency, precision, and speed in software development.
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.
