I mean if youre going to think of it that way any Turing complete language fits the bill, but what I mean by universal is a language you would reach for to solve any problem you have and it would be better than any other language. It’s not a computer science problem it’s a software engineering problem.

There can be a universal language in theory, but it’s borderline impossible to achieve. Every domain has a different set of problems that it needs to solve, and language design involves tradeoffs that may make sense for one domain but not another. That’s why I think language wars are silly, without context it’s impossible to say which language is “better”, because you could have different answers depending on what you’re trying to do.

In the end you shouldn’t be too concerned with it. There are lots of languages, but all of them fall under two or three paradigms where if you learn one language from that paradigm, your skills are mostly transferable.

DMD is the reference implementation as far as I know, so I don’t think they have the same issue that C and C++ have with regards to needing to have a standard that pleases everyone. I agree that it has an issue positioning itself relative to other languages, but to me D is the good kind of boring. It has most of what you need, there is very little that is surprising in it, if you find yourself needing to do something, probably D has an easy-ish way of doing it.

With the FOSS model you get credited at least, so you are getting something out of it even if it’s not monetary. With ChatGPT you don’t even get that. You’re feeding an AI that’s being monetized by someone else, what possible incentive could people have to contribute anymore?

Yeah but will people still care about contributing that information if they’re not going to be compensated for it in any way? Like people get something out of contributing to stack overflow, even if it’s just recognition. This is gone with ChatGPT.

I can think of four aspects needed to emulate human response: basic knowledge on various topics, logical reasoning, contextual memory, and ability to communicate; and ChatGPT seems to possess all four to a certain degree.

LLM’s cannot reason, nor can they communicate. They can give the illusion of doing so, and that’s if they have enough data in the domain you’re prompting them with. Try to go into topics that aren’t as popular on the internet, the illusion breaks down pretty quickly. This isn’t “we’re not there yet”, it’s a fundamental limitation of the technology. LLM’s are designed to mimick the style of a human response, they don’t have any logical capabilities.

Regardless of what you think is or isn’t intelligent, for programming help you just need something to go through tons of text and present the information most likely to help you, maybe modify it a little to fit your context. That doesn’t sound too far fetched considering what we have today and how much information are available on the internet.

You’re the one who brought up general intelligence not me, but to respond to your point: The problem is that people had an incentive to contribute that text, and it wasn’t necessarily monetary. Whether it was for internet points or just building a reputation, people got something in return for their time. With LLM’s, that incentive is gone, because no matter what they contribute it’s going to be fed to a model that won’t attribute those contributions back to them.

Today LLM’s are impressive because they use information that was contributed by millions of people. The more people rely on ChatGPT, the less information will be available to train it on, and the less impressive these models are going to be over time.

Hey, if people are going to go back to reading manuals like we’re in the 1980’s again is it such a bad thing? /s

It’s insane how a single tool managed to completely destroy the value collectively created by people in over a decade.

We’re not able to properly define general intelligence, let alone build something that qualifies as intelligent.

Also being able to prove the relationship between different parts of the code enables a lot of productivity tooling like IDEs. Simple things like renaming a class or a struct become chores at best in a statically typed language, whereas in dynamic languages there is an element of risk in refactorings like that.

I completely understand where you’re coming from because I very much used to be very much the same way. Problems like this can be frustrating because you feel like you’re wasting your time, youre telling the engine the right thing but its not working and you feel like it’s taking time from other problems you could be solving.

My advice: go easy on yourself. Write down all the other stuff you have to do so you dont forget it, and give yourself time to focus on that specific problem to see how you can solve it. Dont look at it as something blocking the other stuff that you have to do, look at it as just another task in your project.

In the case of the Z axis issue, take a step back and start reading about the Z axis and how it works. Create a separate project and start playing with it to see how it works for yourself. Convince yourself that this feature is not broken, and if you still can’t make it work on a simple project at least you’ll have reduced the problem so that if you ask for help online, it’s more likely someone can spot the issue and tell you where you went wrong. If you do manage to make it work, at least you’ll have something working to compare your original code with and make it more likely to spot the problem.

What differentiates an experienced developer from someone who’s learning, is that an experienced developer has already ran into and solved a ton of small problems like these. It’s a hurdle you need to overcome if you want to become effective at developing games.

It’s useful for audit trails and the like, generally OS audit logs only tell you who accessed the machine not what they did on the production database. Things like that. Databases like postgres come with admin tooling in general that SQLite isn’t really meant for. As you said, backups as well are a problem.

The difference is that you can’t have a generic mechanism in Go for error handling. It’s basically up to the programmer, and every if error != nil line is a potential source of bugs.

The database in a state where it’s violating some assumptions I’m making and I need to manually intervene without taking down my application for example. I need to have an audit trail on the changes being made to the database and who made them. I need to create replicas to implement failover. I need to replicate my application on multiple machines and all the replicas need to have the same view of the data. I need to mitigate the possibility of data leaks if I have multiple tenants sharing a database.

I’m not saying that you’re wrong for using it. I’m just saying that it doesn’t work for everything.

I went into this with an open mind, then I saw the rant about error handling and I closed the article immediately.

Rusts error handling in combination with eyre/anyhow is the most pleasant error handling I have used in any language. You know exactly which lines can return errors, you can very quickly propagate them and attach context so that you can quickly troubleshoot later on, and it’s completely natural and unintrusive. No figuring out whether you already logged the error somewhere else, no inconsistent handling of them, it’s one library that does everything for you and you never have to think about it.

The go example is comparatively awful. You are forced to handle the error at each stage, and it runs into the same problem as exceptions where you’re not really sure at which point you actually handled the error among other things.

Sorry about that, it seems I unintentionally created a bit of controversy and am being a bit defensive.

I do use the tool for the job. I don’t understand your point.

Regardless, I don’t see it as something that is the silver bullet that people make it out to be. Being able to introspect the production database, query it, and generally have a set of tools to properly manage your data as opposed to having everything in a file fully managed by your application is something useful for me that you lose with SQLite.

Proper = has actual admin tooling vs. just a file format.

I don’t know, SQLite it’s something that makes sense in theory, but I think its easier for ops people to just use a proper database. If you need to move the database to a separate machine, limit permissions, etc. its just easier to do.

SQLite is great for local apps that need a structured way to store their data, but I’m not really comfortable using it for more than that.

Its not docker’s fault, its the fault of the stack of crap that is needed to run it(WSL and co). My point is that it isn’t worth the trouble. I could figure it out myself, but dev containers don’t bring enough value to my team to justify the investment, and I really dont want to spend a bunch of time troubleshooting issues related to it not just for myself, but for everyone on my team. I played that role before, and it is exhausting to have to do that on top of the other things I need to do.

Docker is really great for CI, for deployments, etc. I really like it, and i have spent a significant chunk of my career developing expertise in it. Its not something I would recommend locally unless you have no other choice, or you’re running Linux and are able to use it natively.