Systems | Development | Analytics | API | Testing

When it comes to developing cross-platform apps, Flutter is one of the top choices that comes to developers' minds. But, what’s the reason behind it? Thanks to the innovative cross-platform development capabilities offered by the Flutter framework, creating native applications for both Android and iOS platforms has become remarkably simple.

With an ever-growing user base of approximately 6.7 billion active smartphone users globally, mobile apps have become an integral part of our modern existence and digital transformation. Recognizing their omnipresence and revenue-generating potential, it becomes imperative not to overlook their significance. Nevertheless, one of the initial queries posed by clients while presenting their app concepts to us revolves around understanding the app development cost.

If you compare the world 10 years ago with today's era, you can easily see the difference. From the difference, we don’t mean how the world looks, rather we mean how it functions now and used to function then. What do you think has changed in how the world functions now compared to back then? Is it purely due to digitalization? Or is it just a mere change? Keeping these questions aside, let us tell you that recent data suggests that people spend around 3 hours and 15 minutes on their phones daily.

Exceptions are inevitable. It’s how we deal with them that matters. An effective exception handling regime is the difference between an app that only works in sandbox and one that can adapt and scale in the real world. JavaScript can throw up all kinds of weird and wonderful exceptions, because it runs in inherently unpredictable environments. So we’ve put together this guide to give you a clear, repeatable plan for handling them.

‍Mobile development has a reputation for being slow, complex, and harder than it needs to be. Platform quirks, rigid review gates, and ever-growing app complexity can make it feel like the toolchain is working against you. But the data tells a different story. We analyzed tens of millions of builds across thousands of mobile teams on Bitrise, spanning three years of real-world data from 2022 to 2025. The results challenge some common assumptions, and confirm others.

For fifty years, the hardest part of software was writing it. That's no longer true. In 2025, AI coding assistants went mainstream — 90% of developers now use them (DORA 2025). Then came background agents: autonomous systems that take a ticket, write the code, run the tests, and open a pull request while the engineer sleeps. Stripe merges over 1,000 AI-written PRs per week. Ramp reached 30% AI-authored PRs within two months. Spotify has merged 1,500+ agent-generated PRs into production.

Jenkins has earned its place at the center of enterprise CI/CD. For organizations building backend services, orchestrating multi-stage deployments, and managing complex polyglot pipelines, Jenkins delivers the flexibility and control that engineering teams depend on. Ripping it out isn’t a conversation most organizations want to have - nor should it be. But mobile is different.

AI isn’t just some side project anymore. These days, it’s a real budget line for big companies, something boards talk about all the time. Global investment in AI is about to break $300 billion a year. McKinsey says AI could add up to $4.4 trillion to the economy every year. That’s huge. But even with all this promise, a lot of businesses still have trouble figuring out if their AI projects are actually paying off. That’s the spot most CXOs are stuck in now.

If you’re a mobile developer running builds on Jenkins, you already know the drill: a flaky agent goes down on a Friday afternoon, your Xcode version is three months behind, and the DevOps engineer who set the whole thing up left six months ago. The builds ship eventually - but at what cost? Jenkins is a powerful, battle-tested automation server. For teams building web backends or managing complex polyglot pipelines, it earns its place.

AI is already here and will be here for years and years to come. The best part is that it will be upgraded to a better version every passing day. And it will keep getting better and better. You must have seen now how people are actively using AI tools these days, and one of the famous examples would be ChatGPT. So, what’s shifting this change? What’s making people so reliant on gen AI tools?