NVIDIA created quality (but proprietary) drivers for Linux early on, when supporting Linux at all was not a given. They should get at least a tiny bit of credit for this.
Quality? Calling those "quality" is a bit of a stretch. Installing was and still is a gamble and so is every tiny system update. That hasn't changed a bit. And I'm saying that as someone who was first introduced to Linux on a Matrox GPU.
To be fair, those were the best that were there back then.
But that was more than 20 years ago. Since then Intel came to work on Mesa, later AMD and various embedded GPU vendors, and now Nvidia is one of the worst options out there.
As long as one didn't have to downgrade X11, they were straightforward to install. You are right that upgrades was a pain, but I believe the drivers themselves were on-par with the Windows drivers.
It's also very easy to dismiss everyone falling into the "AI trap" as being mediocre in the first place, though. But one just can't know this without having seen their work pre-AI.
I think the "meat proxy" people are mediocre regardless of whether or not the were brilliant pre-AI. They've reduced themselves to a copy-paste go-between for claude and slack (or github or jira or whatever) and are mediocre now. If only they could turn back the clock...
Some skills are easy re-pick-uppable, like bicycling. Mathematics is probably more challenging. Not sure about programming, especially "borint" business-like programming.
There's a reason games like Factorio or Exapunks are so popular among engineers!
Especially if you get a little burned out and can't bring yourself to contribute to a side project, but still want to do programming-ish things that get your brain moving
The fundamental skill can evolve. It used to be OOP mania as promoted by Uncle Bob, now it's Casey Muratori DOD. At least that's been my perception. I'm sure there was sometimes before Uncle Bob too, but I wasn't alive. I did however learn OOP in school, class collaboration cards or whatever they're called, and then learn why it all needs to be thrown out the window after finishing school.
Casey also pointed out three kinds of programmers: those who just want to get something done and will use an LLM because it's faster, those who actually like programming, and those who actually like LLMs. Last group always gets left out of these discussions.
(Just as importantly to note - Bob-style OOP was just fine on microprocessors of the 80s with no caches. The state of the industry has changed.)
I think people tend to forget that not only do we ourselves have different skillsets and can be amazing at one thing but horrible but another, but this also applies to other people in the world! Far all we know, there is an amazing developer out there who without LLMs, might have been the single best developer in the country, but even this person might not be able to figure out how to effectively work with LLMs. And vice-versa too.
Maybe this «spearheading AI person» just sucks at AI related stuff, as clearly that approach is bananas, but they could still be a OK developer.
> Far all we know, there is an amazing developer out there who without LLMs, might have been the single best developer in the country, but even this person might not be able to figure out how to effectively work with LLMs. And vice-versa too.
If working with LLMs effectively means accepting subpar results or be a reverse centaur, then I’d be glad not to be able to work with them.
I’ve never seen a good example where AI is a net positive to any development workflow. No one argues against compilers, build tools, IDEs, task runners, deploy and orchestration tools. Because they are great levers that lets you create more with less effort.
Many developers have narrow job descriptions, and the creative, productive uses of AI aren’t really obvious.
Where it shines is glueing systems together or building one-off automations that would take days, or weeks, to figure out. It’s for things you don’t have time to figure out or didn’t think were possible.
I'll argue against half that stuff because you probably don't need it. Do you need kubernetes or does "scp service.exe server: && ssh server systemctl restart service" work for you? GitHub Actions is the worst thing I've ever had to work with and I'd rather have a shell script. What is a task runner, is it an overcomplicated way to SSH?
> If working with LLMs effectively means accepting subpar results
Why would it mean that? That's one way of using them, sure. Personally, my code is better as I have more time to think about the software design than before, and I'm less avoidant of refactoring in my personal projects.
> I’ve never seen a good example where AI is a net positive to any development workflow
Alright, does that mean you also believe it's impossible then that anyone out there is using AI in a "net positive" way for their development workflow? Or just that you've never seen it, but you're open to it existing?
> Or just that you've never seen it, but you're open to it existing?
This one. Only a sith deals in absolute.
I don’t mind experiments to try to find methodologies for those tools. And I believe there are instances where they’ve been successfully used. The issue I have is the kind of generic statements that they are good enough to replace currently established methodologies. Like using AI is a panacea.
> Personally, my code is better as I have more time to think about the software design than before, and I'm less avoidant of refactoring in my personal projects
That’s a bit what I’m talking about. Have you investigated how it has helped you? And if there are other, more economical way to get the same result? Your statement seems more ritualistic than logical.
> Have you investigated how it has helped you? And if there are other, more economical way to get the same result?
No I haven't, but I'm happy to just freeform walk you through my thinking on it: I typically write (wrote?) software for two purposes: consulting/freelancing for others so building what others want, or for simplifying and making my own life easier and more enjoyable. "Stupid" stuff like Home Assistant for example, isn't really life-or-death, or Jellyfin for that matter, both things my family relies on now, but our daily life just gets easier all throughout the day when everything works in sync with what we're doing.
It used to be I had to make a decision what to spend time on, either I work on my professional stuff so we have enough money to survive (maybe more) and I get new challenges and all that, or I spend time improving and maintaining my home infrastructure, or whatever software I feel like I'd need to be better at doing my professional development.
I no longer am making that choice, I'm spending less time in front of the computer, yet the output and quality of my work remains the same, and the code and design when I look at it, even stuff I shipped 6 months ago, I'm still happy with how the code is, which for me I guess is the way I validate if what I produce is good enough.
Nowadays, my entire home-lab is configured with Nix and almost everything except my workstation and some random stuff, runs NixOS. Everything is hosted on a local Forgejo instance, which also has it's own (custom "written" of course) agent acting on issues and PRs, and I have my harness basically maintain my entire home lab at this point. Now I just open issues, have a conversation until everything is 100% clear, end up with a PR to review and merge if it looks good, and I can do this while juggling other things.
I agree with you that there are tons of people who are selling LLMs as a panacea to lots of things, and there is so much over-hype in the industry and ecosystem, I also feel like every "new thing" kind of comes with this type of almost scamming, which sucks, and makes it hard to discern from real positive opinions vs just regurgitated opinions someone read somewhere. I'm not sure what the answer to that is, except perhaps as what you say, only a sith deals in absolutes.
What are the exact "currently established methodologies" you're talking about that cannot be replaced by LLMs + a harness today, just as some examples? You're probably right that those exists, but I'm curious to hear what you think would be the most difficult to replace today.
> What are the exact "currently established methodologies" you're talking about that cannot be replaced by LLMs + a harness today, just as some examples? You're probably right that those exists, but I'm curious to hear what you think would be the most difficult to replace today.
I was explaining [0] under another post that programming is mostly translation works. You take a specs and you formalize it using code, like going from sketch to a proper engineering drawing. Software design is more creative, where you take a problem and then comes up with a solution (creating the specs). Software Engineering is ensuring that those two are done well enough while consuming the least resources.
So a program is always a formal system. It's also static. It will be executed by a computer which will actually have a tangible effect in the real world. That effect is what's valuable. The program is the seed which let us control that effect. Aka it's the map that let us plan the journey, but it's not the territory that we will have to travel in.
The issue I keep pointing in most of my comment is thinking that the map is the territory. That the novel are the words and not the story so we need more words. Or that the code is more important than the user' workflows, se we are adding more buggy code, while not ensuring that the workflows are undisturbed.
> Nowadays, my entire home-lab is configured with Nix and almost everything except my workstation and some random stuff, runs NixOS. Everything is hosted on a local Forgejo instance, which also has it's own (custom "written" of course) agent acting on issues and PRs, and I have my harness basically maintain my entire home lab at this point.
It's also highlighted here where you focus more on the process than the output here. The goal is to have a working homelab. NixOS managing it is only the process (accidental complexity). If it's where truly about the goal and not NixOS and using AI, by this point, adding new nodes (software, devices,...) should be as easy as selecting it and adding it to the current system, like a strategy game.
You can see that philosophy in OpenBSD, where the focus is to have a working OS, not to work on developing an OS. A lot of software are done and it's mostly just bug fixing every once in a while. You can also see the same attitude in industrial engineering where you develop a solution and then use it for years. You don't spend all your time tweaking it and thus disturbing the production flow.
So yes, when I see a LLM methodology, it's mostly about the work itself, not the output of the work. There is no definition of done or even the idea of having one. It's work for the purpose of working.
I think many people did argue against these things when they were new. Compilers, for instance, were seen as a waste of the computer's resources and produced less than optimal code.
That argument still stands, and is still right. Compilers do allow for some programmers to remain uninformed about the actual behaviour of the code they write and this has allowed for a number of actual harms in the outcome of code. (see: Fujitsu computers floating point errors and the British Postal System's persecution of her own Postmasters as one example among many cases) Also, the most performant code either has to be written such that a compiler doesn't incorrectly unroll it's loops or otherwise mangle the intent, or it has to be fine tuned after the fact to correct such mangling. Even Linus does this for the kernel in some cases.
So, drawing the parallel, 'these are the new garbage' but 'old garbage became acceptable so we should accept the new garbage' as an argument in support of being a meat proxy for Markovian stochastic lossy compression-decompression chatbots is isn't very persuasive, especially among this community with a greater concentration of systems level programmers than the general population.
That being said, i think both chatbots and compilers have a specific level of utility; neither of them should have unrestricted access to production filesystems or networks. That way be dragons.
They were right. Only after quite a lot of evolution did they start being wrong. And they're still not that good at SIMD. ffmpeg still uses assembly-code kernels.
Eh. The useful LLM usage I see is in internal tools (bugs don’t matter because the output or UI is the only thing that counts and they are throw away) or personal projects that otherwise wouldn’t exist.
Both of these can be quite invisible. But the benefit is there.
Having long time perspective, seeing the wider context, talking to people, having vision and taste. Curating and sanity-checking the results. Security and trust.
I believe that the assumption that customers reviewing the output artifacts is "the final boss" is wrong. If AI use spreads, customers are also likely to use AI to review the artifacts. Vision, taste and curation remains, though.
Calculators and computers are creating engineers that can't think without them either. There are many problems with AI, but from my point of view, the title has not thought things through.
