Because every time they open up, the site gives them the F̶R̶O̶S̶T̶ cold shoulder.

When ReadableStream behaves the same in the browser, Workers, and other runtimes, stream-based code becomes portable and predictable. That reduces subtle backpressure bugs and eliminates “works here but not there” edge cases.

Standardization at the streams layer is a big deal for building reliable streaming systems across environments.

LLMs take on a huge portion of the work related to handling context, navigating documentation, and structuring thoughts. Today, it’s incredibly easy to start and develop almost any project. In the past, it was just as easy to get overwhelmed by the idea of needing a two-year course in Python (or any other field) and end up doing nothing.

In that sense, LLMs help people overcome the initial barrier, a strong emotional hurdle, and make it much easier to engage in the process from the very beginning.

For small to medium JSON responses, this won't improve performance meaningfully. It’s hard to imagine this being faster or more reliable than simply redesigning the backend to separate out the heavy parts (like article bodies or large comment trees) and fetch them independently. Or better yet, just use a proper streaming response (like chunked HTTP or GraphQL @defer/@stream).

In practice, trying to progressively hydrate JSON this way may solve a niche problem while creating broader engineering headaches.

Key metrics could include:

- Target bundle size

- Memory usage (RAM)

- Startup time

- CPU consumption under load

- Disk usage

- e.t.c.

Additionally, for frameworks like Tauri, it would be useful to include a WebView compatibility matrix, since the rendering behavior and performance can vary significantly depending on the WebView version used on each platform (e.g., macOS WKWebView vs. Windows WebView2, or Linux GTK WebKit). This divergence can affect both UI fidelity and performance, so capturing those differences in a visual format or table could help developers make more informed choices.