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aifartist
ArtSpew
Python2023–2023
aifartist / ArtSpew

ArtSpew

An infinite number of monkeys randomly throwing paint at a canvas

2Distinct people
2Mentions
0Explicit recommenders
310GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

An infinite number of monkeys randomly throwing paint at a canvas

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by hour2 dated mentions

Observed mentions: 2023-12-14 23:50:20 UTC to 2023-12-15 19:37:52 UTC.

012023-12-14 23:00:00 UTC to 2023-12-15 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsDec 14, 23:002023-12-15 00:00:00 UTC to 2023-12-15 01:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 01:00:00 UTC to 2023-12-15 02:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 02:00:00 UTC to 2023-12-15 03:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 03:00:00 UTC to 2023-12-15 04:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 04:00:00 UTC to 2023-12-15 05:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 05:00:00 UTC to 2023-12-15 06:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 06:00:00 UTC to 2023-12-15 07:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 07:00:00 UTC to 2023-12-15 08:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 08:00:00 UTC to 2023-12-15 09:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 09:00:00 UTC to 2023-12-15 10:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsDec 15, 09:002023-12-15 10:00:00 UTC to 2023-12-15 11:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 11:00:00 UTC to 2023-12-15 12:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 12:00:00 UTC to 2023-12-15 13:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 13:00:00 UTC to 2023-12-15 14:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 14:00:00 UTC to 2023-12-15 15:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 15:00:00 UTC to 2023-12-15 16:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 16:00:00 UTC to 2023-12-15 17:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 17:00:00 UTC to 2023-12-15 18:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 18:00:00 UTC to 2023-12-15 19:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-12-15 19:00:00 UTC to 2023-12-15 20:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsDec 15, 19:00

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Dec 14, 23:002023-12-14 23:00:00 UTC2023-12-15 00:00:00 UTC101
Dec 15, 00:002023-12-15 00:00:00 UTC2023-12-15 01:00:00 UTC000
Dec 15, 01:002023-12-15 01:00:00 UTC2023-12-15 02:00:00 UTC000
Dec 15, 02:002023-12-15 02:00:00 UTC2023-12-15 03:00:00 UTC000
Dec 15, 03:002023-12-15 03:00:00 UTC2023-12-15 04:00:00 UTC000
Dec 15, 04:002023-12-15 04:00:00 UTC2023-12-15 05:00:00 UTC000
Dec 15, 05:002023-12-15 05:00:00 UTC2023-12-15 06:00:00 UTC000
Dec 15, 06:002023-12-15 06:00:00 UTC2023-12-15 07:00:00 UTC000
Dec 15, 07:002023-12-15 07:00:00 UTC2023-12-15 08:00:00 UTC000
Dec 15, 08:002023-12-15 08:00:00 UTC2023-12-15 09:00:00 UTC000
Dec 15, 09:002023-12-15 09:00:00 UTC2023-12-15 10:00:00 UTC000
Dec 15, 10:002023-12-15 10:00:00 UTC2023-12-15 11:00:00 UTC000
Dec 15, 11:002023-12-15 11:00:00 UTC2023-12-15 12:00:00 UTC000
Dec 15, 12:002023-12-15 12:00:00 UTC2023-12-15 13:00:00 UTC000
Dec 15, 13:002023-12-15 13:00:00 UTC2023-12-15 14:00:00 UTC000
Dec 15, 14:002023-12-15 14:00:00 UTC2023-12-15 15:00:00 UTC000
Dec 15, 15:002023-12-15 15:00:00 UTC2023-12-15 16:00:00 UTC000
Dec 15, 16:002023-12-15 16:00:00 UTC2023-12-15 17:00:00 UTC000
Dec 15, 17:002023-12-15 17:00:00 UTC2023-12-15 18:00:00 UTC000
Dec 15, 18:002023-12-15 18:00:00 UTC2023-12-15 19:00:00 UTC000
Dec 15, 19:002023-12-15 19:00:00 UTC2023-12-15 20:00:00 UTC101

What Hacker News says

2 sources
They're might be talking about this[0] as it has been popular recently. It can definitely do >60 imgs/s on my 3080Ti, but you're not going to want any of those images. They are absolute garbage.[1] I can do a little under an image a second and some may be quite usable, but nowhere near what you're going to get from the standard model. [0] https://github.com/aifartist/ArtSpew/ [1] but the project is still cool, just context...
In OnnxStream: Stable Diffusion XL 1.0 Base on a Raspberry Pi Zero 2
> I found this claiming an A100 can generate 1 image/s. The article you linked is over a year old. Needless to say there have been a LOT of optimizations in the last year. Back then it was common to use 50+ steps for many of the common samplers. Current methods use a few steps like 1. This OnnxStream are using SDXL-turbo, and you can combine LCM and a few other methods to go very fast. The reason it's so much faster now is the OnnxStream is only using a single step. This repo claims 149 images/s on a 4090 https://github.com/aifartist/ArtSpew However even if you only get 1 image/s with whatever GPU you have I stand by my original statement that unless you want to do it for the cool factor (which is very valid), pre-calculating them makes more sense.
In OnnxStream: Stable Diffusion XL 1.0 Base on a Raspberry Pi Zero 2

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