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Joined 1 year ago
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Cake day: July 1st, 2023

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  • I’m probably thinking about this in a naive way. I’d love to see proprietary models, if trained using public information, be required to become public and free via legislation. AI companies can compete on selling GPU time, on ease of use.

    And, if AI companies are required to figure out attribution in order to be able to use their work commercially, research will accelerate in that area because money. No I don’t know how that would work either.

    Still probably a bad idea but I haven’t figured out why yet.

    Thank you for your well written reply.




  • And those jobs are critical to the process of making new developers.

    An important part of my education - the part that grad school can’t teach you, you have to learn it on the job - was being new and terrible, grinding on a simple problem and feeling like a waste of money. Any of the experienced guys sitting behind me could have done this thing in a few hours but I’ve been working on it for a week. “What’s the point? Any minute now they’re going to tap me on the shoulder and tell me I’m done, it’s time to go find another job.”

    But that never happened.

    Those early problems weren’t fun. At home I would have never chosen to work on them. I’d leave them for someone else. “But now that I’m collecting a paycheck for it, this isn’t up to me. I have to work on it. I can’t give up. I can ask for help, but I need to show my peers that I belong. I can solve difficult problems. I can persevere.”

    As a mediocre professional developer, I had to struggle to learn that. I wasn’t getting far on my own, without mentorship and motivation. Homework, pursuing degrees, wasn’t getting me there. (And even now, I seem to have about two weeks of attention span, for projects at home.)


  • As a professional C# developer since 2012, I’d say a programmer needs four kinds of knowledge. As an organizational user of Github Copilot for a couple months, I’d say AI tools can help with one, maybe two of those.

    Understanding language and syntax, so you can communicate the ideas in your head to the machine accurately: AI is fairly good at this, will certainly get a lot better.

    Understanding algorithms and data structures, well enough to compare and contrast, and choose the most appropriate ones for each circumstance: AI can randomly select something, unless it’s a frequently solved problem. I don’t expect this to get better except for the most repetitive of coding tasks.

    Understanding your execution environment and adapting your solutions to use it well: I don’t see the current generation of AI tools ever approaching this. I don’t think they have context for how a piece of code is used, when trying to learn from it. One size fits all is not a great approach.

    Understanding your customer’s needs and specific problems, and creating products, not code. Problem domains and solutions are a business’s entire reason for existence. This is all kept confidential (and outside the reach of an AI training data set) for competitive reasons. As a human employee, you get to peek behind the curtain and learn these things yourself.