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THE VIDEO SHELF · history of computing ideas

Joe Armstrong - Keynote: The Forgotten Ideas in Computer Science - Code BEAM SF 2018

Erlang co-creator Joe Armstrong's Code BEAM SF 2018 keynote revisits good ideas from the history of computing that were dropped, and asks which should be revived. It also touches on Erlang and Elixir.

Why HN readers recommended it

HN people recommend it as a reading list of historical CS ideas worth knowing, and as a favorite among Armstrong's talks, especially after his death.

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
history of computing ideas
Format
talk
Level
intermediate
HN recommenders in dataset
4
HN mentions in dataset
7
Recommendation years in dataset
2018–2022

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.

  • Ted Nelson's Computers for Cynics probably doesn't contain technical information that's new to any of you, but Ted has a knack for reframing things in ways that make the arbitrariness of certain historical decisions clear: [LINK] On the subject of hypertext, The Web That Wasn't …

    enkiv2 on Hacker News · 2018-04
    In Ask HN: What is your favourite tech talk?
  • I love any talk with Joe Armstrong or Alan Kay in it. Joe Armstrong - Keynote: The Forgotten Ideas in Computer Science - Code BEAM SF 2018 [VIDEO]

    smilesnd on Hacker News · 2018-12
    In Ask HN: Best talks of 2018?
  • This is so sad. RIP Joe. I'll miss him so much. Erlang is one of the most Perlis languages for me. I'm always a little bit cynical and think Erlang deserve more attention. Several months ago Joe post this tweet about a game company refused …

    namelosw on Hacker News · 2019-04
    In Joe Armstrong has died

Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.