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.
When a single entity reaps all of the rewards of that cooperation, people are much less motivated to do that.
Some people are politically motivated, there are tons of reasons, but it’s a two way interaction in all of these cases.
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.
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.
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.
I write Java mainly, not python, but I understand why python specifically might be easier with docker even though virtual env exists and works fine on Windows. To be clear, docker is a fantastic tool, and it has its use cases, but a local dev environment outside of Linux is a recipe for pain.
Every time I try to install it, I have to spend a bunch of time figuring out how to make it work, not to mention VPNs breaking it, and the tooling to run it changing every six months. The VM that it runs in on Docker desktop is largely undocumented, so you dont know how you can even log into it and troubleshoot, much less fix the issues that arise. This isn’t a robust tool, this is a hodgepodge of technologies that are duct taped together into something that can work, but is extremely difficult to fix if it breaks.
I don’t know why your experience is different, but don’t assume that people don’t know what they’re doing when they say stuff like this. Just because it works on your machine doesn’t mean it works on others. You can find lots of developers with similar experiences, it’s not just me.
If you don’t want to insult me, then believe what I’m saying rather than speculating about my abilities.
That matches my experience as well. Nothing kills a developers motivation like spending time dealing with and debugging weird failures on their dev environments as opposed to actually working on the things they care about.
The fact that you have to operate a VM on your machine in order to use it is a no go for me. Either use Linux as your OS in that case, or spend the time to make a dev environment that actually works. Even just mounting a directory is a painful exercise, and something that comes with performance limitations. I’ve had to deal with networking problems, drive problems, all sorts of issues that you really don’t want to waste your time fixing when you have a deadline coming up.
Multiple levels of virtualization, meaning you have the containerization part which is virtualizing the userspace of the VM, and the virtualization of the VM itself. Your development environment consists of multiple layers of complex, not to mention fragile technologies which is an incredibly bad idea for something you rely on heavily for your day to day work.
The people I know who have to use it run a fully fledged Linux VM and do all their work on that. That is how bad the developer experience is on Windows.
Also I’m a bit insulted that you immediately assumed that I’m speaking from a lack of experience, as opposed to years of experience supporting docker dev environments on windows and failing to find a solution that doesn’t come with severe drawbacks.
This works great, assuming you’re using Linux. On Windows this kind of a setup is a nightmare to maintain, since if something goes wrong you have to troubleshoot through several layers of virtualization and weird OS integrations to understand what’s happening. Venv is a much better solution for that.
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.