• slazer2au@lemmy.world
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    6 days ago

    It would be a hard number to range. Do you lump part time, full time and hobbyist into the range?

    • Lost_My_Mind@lemmy.world
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      5 days ago

      I think scientists have theories on that.

      …what? Is that not what string theory is??? Well apperently I do not have a working knowledge on string theory then!

      …I don’t actually. No clue what it really is. Apperently the universe is made up of strings at a molecular level or something? Or maybe space is full of strings? I DON’T KNOW, OK???

  • Septimaeus@infosec.pub
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    5 days ago

    I would guess there’s an upward slope of young contributors that reduce significantly at post-grad and early career ages (e.g. early-mid-twenties) followed by another upward gradient on a 5-10 year delay that peaks in the late 30s then falls somewhat linearly up to 60s.

    The median age of the younger/learning cohort might be 19 and falling. The median age of the established developer cohort might be mid-40s and climbing.

    • lousyd@lemmy.sdf.orgOP
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      5 days ago

      Like, who is coding the apps that I use? Teenagers? Senior citizens? Mothers in a mid-life crisis?

      • palordrolap@fedia.io
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        5 days ago

        With open source it’s either someone incredibly dedicated to doing things for other people (unicorns), someone being paid by a company to do it (workhorses. Some might have a horn, it’s hard to tell. Or the company’s the unicorn), or it’s someone with programming knowledge who also needs and wants to use the software they’re writing (hobbyists).

        Outside of the horse analogues, you probably need to look at the demographics of the users of said software and put the programmer somewhere within that bell curve. As to precisely where, I’d guess not at the low end as they’ve had to gain at least some programming experience along with the knowledge of the topic the software is about.

        For the unicorns and the paid devs, well, they could be anyone.

        There are bound to be systemic skews not accounted for here. More men tend to go into programming than women, for example, or at least that used to be the case.