February 1, 2026
Doomscroll turned Loadscroll
Show HN: Wikipedia as a doomscrollable social media feed
Brilliant idea, but people got stuck at 3% and want lazy loading
TLDR: Xikipedia turns Simple Wikipedia into an endlessly scrolling social feed that learns what you click, all locally with no tracking. The crowd loved the concept but roasted the experience: giant initial loads, iOS refresh loops, and calls for lazy loading—proof that speed beats cleverness.
Meet Xikipedia, a playful demo that turns Simple Wikipedia into a doomscroll-style feed that learns what you click—no tracking, no data sharing, all local. On paper, it’s catnip: an algorithm that figures out your interests without the usual “we’re watching you” creepiness. There’s even a heads‑up that random articles might be NSFW, which only added to the intrigue. But when the community tried it, the vibe swung from “this rules” to “this loads forever.” The mood? Impatient and hilarious. One user saw “3% of 40MB” and peaced out, another reported “very, very slowly,” and someone on iOS watched their browser refresh at 100% and reset to zero like a Sisyphean scroll. The hottest take wasn’t just shade—it was a fix: don’t grab everything at once; load a few and fetch more as we scroll. That sparked a chorus of “I’ll return if you fix the loading!” alongside jokes about turning doomscroll into “loadscroll,” forming a whole 3% club of quitters. The concept still shines—local learning without surveillance is cool—but the crowd’s message is clear: give us fast, snackable bites and we’ll binge. Until then, GitHub gets the love while the feed gets the side-eye.
Key Points
- •Xikipedia is a pseudo social media feed that displays content from Simple Wikipedia.
- •It uses a basic, non-ML algorithm to learn user engagement and recommend similar content.
- •The algorithm runs locally in the browser; no data is collected or shared externally.
- •Any stored interaction data is ephemeral and disappears on refresh or tab close.
- •The project is open-source with source code available on GitHub; discussion links are provided.