Many people mistakenly believe that building a software product using AI is as simple as opening ChatGPT or Claude, typing a prompt to build an app with certain
specifications, and having a market-ready product moments later.
In reality, writing the code itself might actually be the easiest part of the entire journey.
While working on our platform, we faced numerous challenges—many of which only surfaced when we began dealing with real users, live data, and unpredictable real-world scenarios.
We had to tackle concurrency and race conditions, figuring out how to prevent two simultaneous processes from corrupting data at the exact same moment. This was especially critical for timers, task assignments, and payments.
We also dove into the complexities of real-time synchronization, database migrations, and backward compatibility. We had to learn how to add new features or alter the system’s architecture without breaking existing code or putting user data at risk.
However, one of the most demanding challenges that required massive effort was the multi-device architecture.
Making a product available on web, mobile, and desktop doesn’t just mean building three different interfaces for the same backend. The real challenge is making all three work seamlessly together as if they were a single application.
Imagine opening on your phone and starting a timer on a specific task. A few moments later, you open your laptop. You should find the exact same task with the timer running seamlessly from where you left off. If you pause it from the desktop app, that change must immediately reflect on both mobile and web.
But what if the internet disconnects on one device? What if a user starts a timer on a second device at the exact same instant? What if the same request is sent twice? Or what if one device holds outdated data while another has the latest version?
To handle all these scenarios, you need to design a unified backend, a single source of truth, real-time events, and state synchronization. Most importantly, the business logic must remain entirely consistent across all devices.
Furthermore, we had to review every backend change and evaluate its impact on web, mobile, and desktop. A tiny modification in one place could easily break a feature on another device without you even noticing.
Even harder than coding all of this is knowing exactly what needs to be built, when, and why—and ensuring that your solution won’t create a much bigger problem down the road.
AI has undoubtedly saved us a tremendous amount of time and effort. It allowed us to execute tasks that would typically require a larger team and much more time. However, this does not mean that software engineering has been reduced to just writing prompts.
AI can write thousands of lines of code for you in hours, but that does not mean this code is secure, scalable, or production-ready.
There is a massive difference between building a working application for yourself and building a software product that people can rely on to run their business and secure their data.
AI made writing code easier, but it did not eliminate the inherent complexity of building software.
