A zoomable timeline of 4M Wikipedia events
I'm building a journal app in Kotlin Multiplatform and for this purpose I have created a zoomable timeline interface. This is a side-project where I reuse the timeline interface to display 4 million events imported from Wikipedia / Wikidata, scored using PageRank. There is more information on the about page. If you're interested in the stack: I use Kotlin Multiplatform extensively, with Compose Multiplatform for the UI, communicates with the backend using Kotlinx-RPC and behind the hood a simple Postgres database on a Hetzner machine.
In plain words
A zoomable timeline interface displaying four million historical events from Wikipedia and Wikidata, ranked by PageRank relevance. Built as a side project using Kotlin Multiplatform and Compose Multiplatform, it serves as a demonstration of a timeline component originally created for a journal application. The tool allows users to explore and navigate through significant events across different time periods with an interactive zoom interface.
written from the facts on this page · September 2026
Does the same job
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- NINow I Get It – Translate scientific papers into interactive webpagesFeb 2026 · nowigetit.us · ▲305
Understanding scientific articles can be tough, even in your own field. Trying to comprehend articles from others? Good luck. Enter, Now I Get It! I made this app for curious people. Simply upload an article and after a few minutes you'll have an interactive web page showcasing the highlights. Generated pages are stored in the cloud and can be viewed from a gallery. Now I Get It! uses the best LLMs out there, which means the app will improve as AI improves. Free for now - it's capped at 20 articles per day so I don't burn cash. A few things I (and maybe you will) find interesting: * This is…
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I built a system that monitors ~200,000 news RSS feeds in near real-time and clusters related articles to show how stories spread across the web. It uses Snowflake’s Arctic model for embeddings and HNSW for fast similarity search. Each “story cluster” shows who published first, how fast it propagated, and how the narrative evolved as more outlets picked it up. Would love feedback on the architecture, scaling approach, and any ways to make the clusters more accurate or useful. Live demo: https://yandori.io/news-flow/
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Wiki Journey tracks your daily Wikipedia rabbit holes in a tree format. Available on Firefox and Chrome: https://addons.mozilla.org/en-US/firefox/addon/wiki-journey/ https://chromewebstore.google.com/detail/wiki-journey/lehenb... It's open source, feel free to contribute! https://github.com/demegire/wiki-journey
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