THE VIDEO SHELF · hypertext and personal wikis
Joe Armstrong & Jeremy Ruston - Intertwingling the Tiddlywiki with Erlang | Code Mesh LDN 18
Joe Armstrong, creator of Erlang, and Jeremy Ruston, creator of TiddlyWiki, discuss hypertext, Ted Nelson's idea of intertwingled knowledge, and a way to combine TiddlyWiki with Erlang. It is a Code Mesh LDN 2018 talk.
Why HN readers recommended it
Recommended in note-taking and personal wiki threads for its enthusiasm about granular, densely linked notes and TiddlyWiki.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- Code Sync
- Topic
- hypertext and personal wikis
- Format
- talk
- Level
- intro
- HN recommenders in dataset
- 2
- HN mentions in dataset
- 5
- Recommendation years in dataset
- 2020–2024
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
Joe Armstrong of Erlang fame was a fan of Tiddly Wiki, and there's a video of the Tiddly Wiki creator and him enthusing about it here [VIDEO]
jodrellblank on Hacker News · 2020-01
In Managing my personal knowledge baseIf you're interested in quines, see also TiddlyWiki[0] (and specifically this[1] great talk by its creator Jeremy Ruston and the late creator of Erlang, Joe Armstrong) [0] [LINK] [1] [VIDEO]
vector_spaces on Hacker News · 2020-10
In This page is a truly naked, brutalist HTML quineThings which are deeply intertwingled. Joe Armstrong (Erlang) gave at least one talk about it, available on YouTube: [VIDEO] The key points were that Wikipedia style wikis force you into a page-at-a-time model. And TiddlyWiki uses more like a post-it-note sized amount of information. Wikipedia …
jodrellblank on Hacker News · 2021-11
In Launch HN: Dendron (YC W21) – Structured note-taking for developers and teams
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.