[spython]

I understand all that, but how would you explain that 8,6% of drawings from people coming from HN were dicks, while visitors from Tumblr would draw dicks only in 1,6% of the cases? My guess is that people from Tumblr already explored a lot of the transgressions, so it does not interest them much - just like "potty talk phase" stops being interesting after your reach a certain age.

[gus_massa]

Let's call it unpaid software testing team :) People want to know how good is your filtering.

Anecdote time: Last year my wife asked ChatGPT to make a multiple choice test to learn some Biology topic. I arrived and she showed the test to me. The options were a, b, c or d. I wrote e to see what happens :) ChatGPT gave a good reply explaining that I made a mistake :(

[spython]

Indeed, I think this is how it starts. However the filtering system does not indicate to the user if their drawing was filtered - otherwise it would turn into a "trick the system" game - a whole different genre. So the visitor always gets the satisfaction of seeing their penis galloping on the horizon. Still, many if not most penis artists then continue to add more and more lewd drawings. So it's not just a test but some other behaviour. Coming from Hacker News, they might feel like a hacker, a rebel breaking the system. And that's fine - after all there are not many ways in a society where you can feel this way and not hurt anyone.

[efilife]

https://rybakov.com/blog/how_do_people_draw_horses/

I see that you posted the same words on your blog but left out comparing HackerNews users to children aged 8 to 10. On purpose?

[spython]

It's the other way around - I integrated some things I wrote in the comments here into the blog post. I'm still writing the post though - it's not easy to hypothesize about people's intention without sounding like an ass.

[malfist]

> people coming from HN were dicks

I think you answered your question

[mft_]

Could we hypothesise a different gender ratio from Tumbler vs. HN?

[goodmythical]

Follow-up: Sexual activity predictability via analysis of preffered social media platforms.