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Joined 4 months ago
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Cake day: June 12th, 2024

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  • For me, as primarily a backend dev, the argument was that it’s a framework, unlike React, so you get an everything-in-one solution which is quite easy to setup and use.

    Given that Google still hasn’t killed this one yet, it’s also a mature platform with plenty of articles online on how to use it.

    IIRC the license was also better than React’s, at least last time I checked.

    Not sure on what the landscape looks like today, but when I was making the choice, the internet didn’t seem to consider other solutions to be competitive with either React or Angular.







  • It’s only as incomprehensible as you make it.

    If there are 6 subfunctions, that means there’s 6 levels of abstraction (assuming the method extraction was not done blindly), which further suggests that maybe they should actually be part of a different class (or classes). Why would you be interested in 6 levels of abstraction at once?

    But we’re arguing hypotheticals here. Of course you can make the method implementations a complete mess, the book cannot guarantee that the person applying the principles used their brain, as well.




  • You’re nitpicking.

    As it happens, it’s just an example to illustrate specifically the “extract to method” issues the author had.

    Of course, in a real world scenario we want to limit mutating state, so it’s likely this method would return a Commission list, which would then be used by a Use Case class which persists it.

    I’m fairly sure the advice about limiting mutating state is also in the book, though.

    At the same time, you’re likely going to have a void somewhere, because some use cases are only about mutatimg something (e.g. changing something in the database).





  • It makes me sad to see people upvote this.

    Robert Martin’s “Clean Code” is an incredibly useful book which helps write code that Fits In Your Head, and, so far, is the closest to making your code look like instructions for an AI instead of random incantations directed at an elder being.

    The principle that the author of this article argues against seems to be the very principle which helps abstract away the logic which is not necessary to understand the method.

    public void calculateCommissions() {
      calculateDefaultCommissions();
      if(hasExtraCommissions()) {
        calculateExtraCommissions();
      } 
    } 
    

    Tells me all I need to know about what the method does - it calculates default commissions, and, if there are extra commissions, it calculates those, too. It doesn’t matter if there’s 30 private methods inside the class because I don’t read the whole class top to bottom.

    Instead, I may he interested in how exactly the extra commissions are calculated, in which case I will go one level down, to the calculateExtraCommissions() method.

    From a decade of experience I can say that applying clean code principles results in code which is easier to work with and more robust.



  • But then it’s the tools to make the AI that are open source, not the model itself.

    I think that we can’t have a useful discussion on this if we don’t distinguish between the source code of the training framework and the “source code” of the model itself, which is the training data set. E.g, Mistral Nemo can’t be considered open source, because there is no Mistral Nemo without the training data set.

    It’s like with your Doom example - the Doom engine is open source, but Doom itself isn’t. Unfortunately, here the analogy falls apart a bit, because there is no logic in the art assets of doom, whereas there is plenty of logic in the dataset for Mistral - enough that the devs said they don’t want to disclose it for fear of competition.

    This data set logic - incredibly valuable and important for the behavior of the AI, as confirmed by the devs - is why the model is not open source, even though the training framework might be.


  • You’re trying to change the definition of open source for AI models and your argument is that they’re magic so different rules should apply.

    No, they’re not fundamentally different from other software. Not by that much.

    The training data is the source of knowledge for the AI model. The tools to train the model are the compiler for that AI model. What makes an AI model different from another is both the source of knowledge and the compiler of that knowledge.

    AFAIK, only one of those things is open source for Mistral - the compiler of knowledge.

    You can make an argument that tools to make Mistral models are open source. You cannot make an argument that the model Mistral Nemo is open source, as what makes it specifically that model is the compiler and the training data used, and one of those is unavailable.

    Therefore, I can agree on the social network analogy if we’re talking about whether the tools to make Mistral models are open-source. I cannot agree if we’re talking about the models themselves, which is what everyone’s interested in when talking about AI.


  • That’s like saying the source code of a binary is a bunch of hexadecimal numbers. You can use a hex editor to look at the “source” of every binary but it’s not human readable

    Yes, the model can be published without the dataset - that makes it, by definition, freeware (free to distribute). It can even be free for commercial use. That doesn’t make it open source.

    At best, the tools to generate a model may be open source, but, by definition, the model itself can never be considered open-source unless the training data and the tools are both open-source.