As a long-time Python developer, I’ve always been amazed by the sheer performance of Rust libraries whenever I used them. So, when I decided to build my own app, I spent some time weighing native development against multi-platform tools. That’s when Tauri, a Rust-based framework, caught my eye. Thus, the SyncWatcher journey began.
The Initial Rush: “Oh, Wow, This Is Fast!”
When I first started building with Tauri, the feeling could be summed up in one word: Exhilarating. Maybe it was because the app hadn’t grown complex yet, but both the build times and initialization speeds were outstanding. Developing with AI as a primary tool was also a refreshing and positive experience. Halfway through, I honestly felt like I was on the path to creating the fastest app in the world.
The Mid-Project Epiphany: The Synchronization Trap
However, as I aimed for higher stability and a more polished user experience, I hit an unexpected wall. It wasn’t that the program itself was slow—Rust’s core execution is blistering. The real challenge lay in handling high volumes of events between the Backend (Rust) and Frontend (Webview).
As the number of concurrent events grew, I had to implement multiple mechanisms to ensure stability and avoid conflicts. These measures eventually started adding overhead to UI updates. The initial “Wow, this is fast!” sensation began to fade under the weight of synchronization logic.
The complexity increased further during the app’s startup phase. Though the codebase is lightweight, SyncWatcher needs to read synchronization settings, potentially run an initial sync immediately, and reflect all these states in the UI in real-time. Crafting this process in a purely non-blocking way turned out to be architecturally difficult. Consequently, I had to introduce some blocking calls, which meant the app’s startup wasn’t as snappy as I originally envisioned.
Native Limits and Small Regrets
One persistent frustration was the inability to perfectly replicate native macOS behavior. Specifically, fine-grained control over window resizing proved tricky. Perhaps it was because I didn’t dig deep enough into the low-level APIs, but even with AI assistance, solving these subtle native UX issues was a struggle.
Why I’m Choosing Tauri Again
Despite these hurdles, I plan to stick with Tauri for my next project—a Knowledge Management Service. Here’s why:
- Memory efficiency: The footprint is incredibly small compared to Electron.
- Rust’s raw logic speed: It’s still unbeatable for heavy lifting.
- Productivity: It allows for a faster development cycle compared to going full native.
Ironically, SyncWatcher was designed strictly for macOS, meaning I didn’t actually need a multi-platform framework. I chose Tauri simply because I wanted to try it out. But this specialized use case taught me a lot about the gap between native and multi-platform solutions.
AI and the Worth of Fundamentals
Developing with AI has led me to a paradoxical conclusion: Computer science fundamentals are more important than ever. Without a solid understanding of event loops, memory management, and asynchronous operations, you can’t even diagnose why AI-generated code might be failing.
Yet, I can’t help but wonder: “Could a complete beginner overcome these gaps simply by being an elite communicator with AI?”
As a 40-something indie hacker, I’ve realized that while the tools evolve, the essence remains the same. The way we handle that essence just gets a little smarter. I’m already looking forward to how I’ll tackle those event-handling limits in my next project.