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Have you used AI to code? You don't say "hey, write this file" and then commit it as "AI Bot 123 [email protected]".
You start writing a method and get auto-completes that are sometimes helpful. Or you ask the bot to write out an algorithm. Or to copy something and modify it 30 times.
You're not exactly keeping track of everything the bots did.
Or to copy something and modify it 30 times.
This seems like a very bad idea. I think we just need more lisp and less AI.
It’s not good because it has no context on what is correct or not. It’s constantly making up functions that don’t exist or attributing functions to packages that don’t exist. It’s often sloppy in its responses because the source code it parrots is some amalgamation of good coding and terrible coding. If you are using this for your production projects, you will likely not be knowledgeable when it breaks, it’ll likely have security flaws, and will likely have errors in it.
So you're saying I've got a shot?
If humans are so good at coding, how come there are 8100000000 people and only 1500 are able to contribute to the Linux kernel?
I hypothesize that AI has average human coding skills.
Average drunk human coding skils
Well according to microsoft mildly drunk coders work better
Can Open Source defend against copyright claims for AI contributions?
If I submit code to ReactOS that was trained on leaked Microsoft Windows code, what are the legal implications?
what are the legal implications?
It would be so fucking nice if we could use AI to bypass copyright claims.
"No officer, i did not write this code. I trained AI on copyright material and it wrote the code. So im innocent"
I am not a programmer and I think it's silly to think that AI will replace developers.
But I was working through a math problem in Moscow Puzzles with my kiddo.
We had solved it, but I wasn't sure he got it at a deep level. So I figured I'd do something in Excel or maybe just do cut outs. But I figured I'd try to find a web app that would do this better. Nothing really came up that was a good match. But then thought, let's see how bad AI programming can be. I'd fought with it over some excel functions and it's been mainly useful in pointing me in the right direction, but only occasionally getting me over the finish line.
After about 6 to 8 hours of work, a little debugging, havinf teach and quiz me occasionally, and some real frustration of pointing out that the feature previously changed and re-emeged, I eventually had something that worked.
The Shooting Range Simulator is a web-based application designed to help users solve a logic puzzle involving scoring points by placing blocks on vertical number lines.
A buddy developer friend of mine said: "I took a quick scroll through the code. Looks pretty clean, but I didn't dive in enough to really understand it. Definitely all that css BS would take me ages to do without AI."
I don't take credit for this and don't pretend that this was my work, but I know my kiddo is excited to try the tool. I hope he learns from it and we bond over a math problem.
I know that everyone is worried about this tool, but moments like those are not nothing. Personally, I'm a Luddite and think the new tools should be deployed by the people's livelihood it will effect and not the business owners.
Personally, I’m a Luddite and think the new tools should be deployed by the people’s livelihood it will effect and not the business owners.
Thank you for correctly describing what a Luddite wants and does not want.
AI is at its most useful in the early stages of a project. Imagine coming to the fucking ssh project with AI slop thinking it has anything of value to add 😂
The early stages of a project is exactly where you should really think hard and long about what exactly you do want to achieve, what qualities you want the software to have, what are the detailed requirements, how you test them, and how the UI should look like. And from that, you derive the architecture.
AI is fucking useless at all of that.
In all complex planned activities, laying the right groundwork and foundations is essential for success. Software engineering is no different. You won't order a bricklayer apprentice to draw the plan for a new house.
And if your difficulty is in lacking detailed knowledge of a programming language, it might be - depending on the case ! - the best approach to write a first prototype in a language you know well, so that your head is free to think about the concerns listed in paragraph 1.
the best approach to write a first prototype in a language you know well
Ok, writing a web browser in POSIX shell using yad now.
If AI was good at coding, my game would be done by now.
FTA: The user considered it was the unpaid volunteer coders’ “job” to take his AI submissions seriously. He even filed a code of conduct complaint with the project against the developers. This was not upheld. So he proclaimed the project corrupt. [GitHub; Seylaw, archive]
This is an actual comment that this user left on another project: [GitLab]
As a non-programmer, I have zero understanding of the code and the analysis and fully rely on AI and even reviewed that AI analysis with a different AI to get the best possible solution (which was not good enough in this case).
Microsoft has set up copilot to make contributions for the dotnet runtime https://github.com/dotnet/runtime/pull/115762 I'm sure maintainers spends more time to review and interact with copilot than it would have to write it themselves
My theory is not a lot of people like this AI crap. They just lean into it for the fear of being left behind. Now you all think it's just gonna fail and it's gonna go bankrupt. But a lot of ideas in America are subsidized. And they don't work well, but they still go forward. It'll be you, the taxpayer, that will be funding these stupid ideas that don't work, that are hostile to our very well-being.
who makes a contribution made by aibot514. noone. people use ai for open source contributions, but more in a 'fix this bug' way not in a fully automated contribution under the name ai123 way
Counter-argument: If AI code was good, the owners would create official accounts to create contributions to open source, because they would be openly demonstrating how well it does. Instead all we have is Microsoft employees being forced to use and fight with Copilot on GitHub, publicly demonstrating how terrible AI is at writing code unsupervised.
Bingo
Microsoft is doing this today. I can't link it because I'm on mobile. It is in dotnet. It is not going well :)
Ask Daniel Stenberg.
AI is just the lack of privacy, Authoritarian Dragnet, remote control over others computers, web scraping, The complete destruction of America's art scene, The stupidfication of America and copyright infringement with a sprinkling of baby death.
As a dumb question from someone who doesn't code, what if closed source organizations have different needs than open source projects?
Open source projects seem to hinge a lot more on incremental improvements and change only for the benefit of users. In contrast, closed source organizations seem to use code more to quickly develop a new product or change that justifies money. Maybe closed source organizations are more willing to accept slop code that is bad but can barely work versus open source which won't?
Baldur Bjarnason (who hates AI slop) has posited precisely this:
My current theory is that the main difference between open source and closed source when it comes to the adoption of “AI” tools is that open source projects generally have to ship working code, whereas closed source only needs to ship code that runs.
Maybe closed source organizations are more willing to accept slop code that is bad but can barely work versus open source which won’t?
Because most software is internal to the organisation (therefore closed by definition) and never gets compared or used outside that organisation: Yes, I think that when that software barely works, it is taken as good enough and there's no incentive to put more effort to improve it.
My past year (and more) of programming business-internal applications have been characterised by upper management imperatives to “use Generative AI, and we expect that to make you nerd faster” without any effort spent to figure out whether there is any net improvement in the result.
Certainly there's no effort spent to determine whether it's a net drain on our time and on the quality of the result. Which everyone on our teams can see is the case. But we are pressured to continue using it anyway.
I'd argue the two aren't as different as you make them out to be. Both types of projects want a functional codebase, both have limited developer resources (communities need volunteers, business have a budget limit), and both can benefit greatly from the development process being sped up. Many development practices that are industry standard today started in the open source world (style guides and version control strategy to name two heavy hitters) and there's been some bleed through from the other direction as well (tool juggernauts like Atlassian having new open source alternatives made directly in response)
No project is immune to bad code, there's even a lot of bad code out there that was believed to be good at the time, it mostly worked, in retrospect we learn how bad it is, but no one wanted to fix it.
The end goals and proposes are for sure different between community passion projects and corporate financial driven projects. But the way you get there is more or less the same, and that's the crux of the articles argument: Historically open source and closed source have done the same thing, so why is this one tool usage so wildly different?
When did you last time decide to buy a car that barely drives?
And another thing, there are some tech companies that operate very short-term, like typical social media start-ups of which about 95% go bust within two years. But a lot of computing is very long term with code bases that are developed over many years.
The world only needs so many shopping list apps - and there exist enough of them that writing one is not profitable.
I'll admit I did used AI for code before, but here's the thing. I already coded for years, and I usually try everything before last resort things. And I find that approach works well. I rarely needed to go to the AI route. I used it like for .11% of my coding work, and I verified it through stress testing.
