Artificial Intelligence (AI) continues to rapidly evolve, fundamentally changing how apps are conceptualized, developed, and maintained. As we look forward, the role of AI in app development is becoming increasingly sophisticated, empowering developers to build smarter, more intuitive, and highly personalized apps. Here’s a look at the future of AI in app development, along with some real-world examples of companies leading the way.
Artificial intelligence (AI) is shaking things up in app development, making everything from automation to personalization feel like magic. Companies across industries are harnessing AI to improve efficiency, reduce costs, and deliver innovative products to their users. In this blog post, we’ll explore some real-world examples of companies that have successfully integrated AI into their apps and app development workflows.
AI is rapidly transforming app development by optimizing workflows, refining user experiences, and enhancing decision-making at every stage of the development lifecycle. From ideation to post-deployment, AI tools are enabling development teams to work smarter, and more efficiently. Here are some practical examples of how AI is transforming each phase of modern app development, as well as some recommended tools to get you started.
Today’s digital age demands speed, innovation, and precision — qualities that traditional app development methods often struggle to deliver consistently. Enter artificial intelligence (AI), a transformative force reshaping the way apps are conceptualized, designed, built, and maintained. AI is not just a buzzword; it’s a game-changer that empowers developers to work smarter, create faster, and achieve more.
This blog kicks off a comprehensive series on how AI is revolutionizing app development. Over the coming articles, we’ll take an in-depth look at how AI integrates into every stage of the app development lifecycle, from conception to deployment, and how it addresses some of the most pressing challenges faced by developers today. The goal is to equip you with actionable insights and practical knowledge to harness the power of AI in your projects.
In a previous blog post, I covered the “textbook” definition of continuous integration, along with a handful of tools and practices that fulfill or help to fulfill said definition. These tools and practices include breaking up your app into components (e.g. front-end and back-end, or, for much larger projects, using microservices), utilizing “watch” utilities locally to iteratively run tests, and choosing test-oriented frameworks (e.g. Rails, Django, Grails, etc.). However, I didn’t talk much about any specific continuous integration setup, nor some of the third-party services that go together to make an efficient release process. I also didn’t talk much about how continuous integration fits into the larger cycle of deployment and release management. I aim to cover some of those topics here, and fill in the larger picture of how CI helps to ensure code quality and stability in a software project.
I’ll be walking you through a solution I put together for one of our up and coming apps! It works rather well, I learned a lot, and so far no complaints…although there are no users yet either!
Feeling quite charitable, I’m going to let you in on some useful bits and pieces as we build up to polling: threading, late subscribing, replay, manual re-triggering and error handling (a must for preserving replays).
