Heh, I agree with everything you said, but I’m afraid such a framework is impossible to create, let alone implement. It’s impossible to foresee the infinite possibilities for people to screw themselves through bad decisions, so all you’d create is a lot of bureaucracy to still end up in the same place.
It’s that the compiler doesn’t help you with preventing race conditions. This makes some problems so hard to solve in C that C programmers simply stay away from attempting it, because they fear the complexity involved.
It’s a variation of the same theme: Maybe a C programmer could do it too, given infinite time and skill. But in practice it’s often not feasible.
Which one should I pick then, that is both as fast as the std solutions in the other languages and as reusable for arbitrary use cases?
Because it sounds like your initial pick made you loose the machine efficiency argument and you can’t have it both ways.
I’m not saying you can’t, but it’s a lot more work to use such solutions, to say nothing about their quality compared to std solutions in other languages.
And it’s also just one example. If we bring multi-threading into it, we’re opening another can of worms where C doesn’t particularly shine.
Well, let’s be real: many C programs don’t want to rely on Glib, and licensing (as the other reply mentioned) is only one reason. Glib is not exactly known for high performance, and is significantly slower than the alternatives supported by the other languages I mentioned.
I would argue that because C is so hard to program in, even the claim to machine efficiency is arguable. Yes, if you have infinite time for implementation, then C is among the most efficient, but then the same applies to C++, Rust and Zig too, because with infinite time any artificial hurdle can be cleared by the programmer.
In practice however, programmers have limited time. That means they need to use the tools of the language to save themselves time. Languages with higher levels of abstraction make it easier, not harder, to reach high performance, assuming the abstractions don’t provide too much overhead. C++, Rust and Zig all apply in this domain.
An example is the situation where you need a hash map or B-Tree map to implement efficient lookups. The languages with higher abstraction give you reusable, high performance options. The C programmer will need to either roll his own, which may not be an option if time Is limited, or choose a lower-performance alternative.
Of course, but it needn’t be black and white. You can also diversify, make yourself less reliant on a single platform. And by doing so, enable your audience to follow you elsewhere. Or diversify into different activities altogether. And when it’s no longer half your income on the line, then switch.
But doing nothing and saying, “but half my income!”? That’s not only a choice, but also complacency.
Great points, except:
People can’t leave for anything smaller.
They can and some do. It’s still a choice.
Haha, sure thing!
So, today, TurboPascal isn’t a very popular programming language anymore. But that’s okay! We have new programming languages nowadays. Some of the popular languages that we use today include JavaScript, TypeScript and CSS. You don’t need to know much about these languages, except that they’re commonly used for creating websites and apps that run on the web.
Now, assume you want to create a website or a webapp, and you were to learn these languages for that purpose. In that case you have quite a learning experience ahead of you, which is great! Learning can be fun! But what’s not so great is that these languages have lots of room to make mistakes. Now, everyone makes mistakes, that’s just a fact of life, but when mistakes can be avoided, that’s generally preferred.
This is where Biome comes in: It is a tool – we call it a linter – that helps you to detect many kinds of common mistakes. It can show you where these mistakes are, and sometimes even fix them for you. It can also show you possible mistakes, things that are not necessarily a mistake, but things that look suspicious.
And on top of that, Biome offers you another tool: It’s called a formatter. When you write your code, it automatically takes care for you that the code looks consistent. So it fixes things like indentation and other use of whitespace for you, as well as where to place your parentheses and stuff like that.
Together, hopefully these two things can make your programming experience a little bit more enjoyable. Cheers!
They’re included in the beta!
Specifically, you can create GritQL plugins for custom linter diagnostics. There’s certainly more we’d like to do on that front, but we’re first going to see how these are being received to decide where to prioritise next.
I don’t have Nix experience myself, but what would it take to be better supported there? I think we’d be open to PRs for that ☺️
We don’t currently publish to Crates.io, but we do have CI integrations that can install without needing NPM.
Formatter and linter in one even :) I’ve updated the message at the top.
I’m making a case for custom codes, not for using a 200 status code with it. My reply said the 200 didn’t make sense.
Of course once you use custom codes, the actual HTTP status codes do become less important, because there’s some redundancy there. That’s not an argument to do it wrong, but it is an argument that accurate HTTP status codes are less of a priority. So understandably some people will take shortcuts.
Apparently you find this very frustrating, but in the end it’s just an implementation detail. But it also sounds like you’re more frustrated with the service API as a whole than the fact it uses custom error codes specifically, so I’m just going to leave it at that.
I’m not arguing against that. Merely providing some counterweight to the idea that the author was “flinging shit in the trenches” 😅
I found the title of that section slightly triggering too, but the argument they lay down actually makes sense. Consistency helps you to achieve correctness in large codebases, because it means you don’t have to reinvent what is correct over and over in separate pockets of the codebase. Such pockets also make incremental improvements to the codebase harder and harder, so they do come back to bite you.
Your example of vendors doesn’t relate to that, because you don’t control your vendor’s code. But you do control your organisation’s.
Well, looking at your example, I think a good case can even be made for it.
“s23” doesn’t look like an HTTP status code, so including it can make total sense. After all, there’s plenty of reasons why you could want custom error codes that don’t really align with HTTP codes, and customised error messages are also a sensible use case for that.
Of course duplicating the actual HTTP status code in your body is just silly. And if you use custom error codes, it often still makes sense to use the closest matching HTTP status code in addition to it (so yeah, I agree the 200 in your example doesn’t make a lot of sense). But neither of those preclude good reasons for custom codes.
Oh, and then I pair it with a very boring Dell mouse for extra style.
Ha, I better not tell you about the Apple Keyboard I use with my Linux laptop then. Don’t like macOS much, but I love their flat keyboards.
I see this argument repeated here every now and then, but it’s not true: https://aoav.org.uk/2020/the-effects-of-strategic-bombing-in-wwii-on-german-morale/
To summarise: Bombing had a profound effect on depressing morale through inducing defeatism, fear and apathy. Bombing did not harden popular resolve against the US. The hate and anger it aroused was directed against the Nazi regime, not the Allies.