THE VIDEO SHELF · app store shovelware
1,500 Slot Machines Walk into a Bar: Adventures in Quantity Over Quality
In this 2019 GDC talk, Alex Schwartz and Ziba Scott explain how they flooded the app stores with over 1,500 auto-generated slot machine games to find the lowest bar for success. They tour the dark side of the mobile app market.
Why HN readers recommended it
HN commenters recommend it as a hilarious account of mass-produced app store shovelware and cite it in threads on spam and low-quality content.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- GDC Festival of Gaming
- Topic
- app store shovelware
- Format
- talk
- Level
- general
- HN recommenders in dataset
- 3
- HN mentions in dataset
- 6
- Recommendation years in dataset
- 2020–2026
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.
There was a GDC talk telling a a story where two bored indie game devs made an AI that churns out throwaway low-quality slot machine games as a joke, but actually earned much more money they they’ve expected (thousands of dollars per day). They jokingly …
cyber_kinetist on Hacker News · 2022-07
In Unity merges with IronSourceReminds me of this brilliant GDC Talk - 1,500 Slot Machines Walk into a Bar: Adventures in Quantity Over Quality [VIDEO]
jordanwallwork on Hacker News · 2020-09
In Google requiring Android apps on Play store to use its billing system next yearIf you think that's bad, check out this GDC Talk: [VIDEO] They procedurally generated 1,500+ different iterations on top of the same Unity-based slots shovelware using noun/adjective pairs. The general success they enjoyed is as sad as it is hilarious . For the record, the …
chaorace on Hacker News · 2021-01
In A bit on scaling chess.com's database
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.