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Reliability in the Background: How GenkiApe Handles Large Assets

Running AI in a browser isn’t without its challenges. When you’re dealing with image upscaling—specifically using Real-ESRGAN—the assets involved aren’t exactly small. We’re talking about a WASM runtime for the engine and AI model files that often cross the 50MB or 100MB threshold.

At GenkiApe, we wanted to ensure that once a user waits for a download, they don’t have to do it again. Here is a look at the humble “plumbing” that makes that possible.

The Limits of the Standard Cache

Most of us rely on the browser’s default HTTP cache for everything. It’s a great system, but it treats large files with a “first-in, first-out” logic. If a user’s disk gets crowded, those heavy AI models are often the first things the browser deletes to make room for something else.

To provide a more consistent experience, we decided to move our most critical assets into IndexedDB.

A Shared Responsibility for Performance

Our approach isn’t about “magic” speed; it’s about being responsible with the user’s time and bandwidth.

• Version-Aware Storage: We don’t just save files; we save them with their specific version tags. Every time you open the app, it quietly checks if the local version matches our current build. If it does, we skip the network entirely.
• Transparent Loading: If a download is necessary, we use stream readers to show exactly what’s happening. It’s a small touch, but it removes the guesswork of “is this stuck?” during the initial setup.
• Wasm-Native Integration: By writing these saved bytes directly into the Emscripten virtual file system, we allow the C++ engine to find its “brain” instantly, without having to re-negotiate URLs or permissions.

Why We Chose This Path

The goal wasn’t to build something complex, but something reliable. By using a hybrid approach—letting the browser cache handle small JS files while we manage the heavy binaries in IndexedDB—we achieve a few simple things:

1. Lower Latency: After the first visit, the app starts up almost instantly because the “heavy lifting” is already sitting on your device.
2. Bandwidth Respect: We avoid re-downloading large files, which is better for users on limited data plans or slower connections.
3. Offline Continuity: If your connection drops, the upscaler doesn’t stop. The engine and models are already there, ready to work.

Closing Thoughts

GenkiApe is built on the idea that high-performance AI should feel lightweight. By moving the storage logic into IndexedDB, we’ve tried to create a tool that stays out of your way and simply works when you need it. It’s not a revolution, just a more thoughtful way to handle the bits and bytes.