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magic_lobster_party @ magic_lobster_party @kbin.run
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  • I wish popularity wasn’t a factor when considering which language to use for a project. Just pick the language best suited for the job!

    This is obviously unrealistic. Choosing a largely unknown language is a bad idea because it makes recruitment unnecessarily difficult - even if it’s the best language in the world for this particular task.

    But imagine a world where we’re not stuck with Java, C++ and Python as the only viable options.

  • There’s bad code and then there’s worse code. “Best practices” might help you to avoid writing worse code.

    Good code might appear occasionally. In the rare event when it’s also useful, people will start to have opinions about what it should do. Suddenly requirements change and now it’s bad code.

  • The STD is maybe the only good thing C++ has over C, and even that is awful compared to other language’s standard libraries.

    I can’t name another good thing C++ has. Maybe templates. C++’s reliance on inheritance for polymorphism is awful (should’ve gone with interfaces/traits).

    Not to mention the mess with all the different types of constructors that must always be implemented.

    It’s just a bunch of bad design choices added on top of an old outdated language.

  • We have those parts of code someone wrote years ago that is heavily abstracted for no reason at all other than that the developer thought it might be good. It’s just endless clicking on methods just to find no code at all and then you have to deep dive to figure out where stuff is actually computed.

    Make it just one simple method call? Nah, it should be divided into 10 different abstract classes because whatever reason.

    It’s also the most buggy part of the code because no one currently in the team dares to touch it.

  • And the reason why Columbus managed to get funding for his journey was because he used incorrect estimations of the earth’s circumference, which indicated a much shorter distance to India than previous (much more accurate) estimations.

  • I get you. It’s just that when a company says they’re offering AI solutions, it’s most likely just outsourced workforce under the hood.

    Companies can either spend one or two years of R&D, possibly involving PhDs, to solve a task using AI, or they can get the product out to the market in a few months by outsourcing the AI part. The first option might not even be up to par with the outsourced option even with all the money spent on R&D.

  • I’ve worked with AI companies where their “AI” is mostly just outsourced to some human in a low wage country. Some small part was actual AI algorithms, so they weren’t completely lying to investors.

    Setting up actual AI is expensive and time consuming, especially for some new startup. Often it involves creating large amounts of labeled data and even that might not be enough. There are so many uncertainties involved. It might only be possible to achieve 90% or even lower accuracy for a given problem with actual AI algorithms, so the quick and easy solution is to outsource.

    I believe this practice is more common than most think.