The main confusing (or confused?) actor in the FOSS space in relation to this probably really is the Linux kernel.

I’ve seen so many projects reference significant LLM patches as acceptable “because the kernel does it”. It continues to blow my mind that the kernel would accept such a risk.

Sad to see that it’s accepted outside of the test cases at all. But good to see they’re severely limiting it, at least.

Why do I find even minor LLM changes sad?

• Because it moves the goalpost about what is okay to copy. If you brought a one-line change to a FOSS project in the past that you took out of the leaked MS Windows source code, you would have been scolded for risking such an explosive origin for such low gain. Nowadays with LLMs people seem to be trying to make that the new normal. I don’t think that’s a good path to take for the ecosystem.

• And because no project should have to think about what’s “copyright significant”. If you reach that state, I feel like you should perhaps just reject it and have somebody rewrite it cleanly.

I feel like it depends on the project.

Typically in the ones I work on, whenever I spot an inconsistency I fix it no matter how much of a mess that causes. Yes it hurts at first, but it may hurt more down the line when the weirdnesses accumulate to the point where it impacts operations and it’ll be a much bigger problem to fix.

However, I can totally see a more chill strategy work better for a project that is mostly just relatively simple code, like perhaps some website deployments. In my opinion it depends a lot on whether the code is generally already complicated, which is when you’ll typically want to refactor earlier than later.

No worries, the amount of new slop code with hidden bugs is entering the ring to try to keep the balance and to ensure the overall security doesn’t improve by too much: https://www.neowin.net/news/linus-torvalds-declares-massive-ai-fueled-code-surges-as-the-new-normal-for-linux/

I think this is the report it talks about: https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report Does this link work better?

My apologies, I think this is the report it is talking about: https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report

I’ve had success, for example, having them remove pointless, confusing try…except blocks surrounding imports at work.

And you may have introduced some dangerous hidden bug that way, which you may not have doing it manually.

(I’m not saying that makes it not worth it, this is just what the studies are saying. I personally think it’s not worth it, but I realize there is some subjectivity here.)

They excel at specific tasks that are built for them

They are however widely known to be terrible at code, at least compared to an advanced coder. They introduce not only more bugs even after human review, but new kinds of more insideous bugs.

I like to say the main problems with most projects were already the code quality and the bugs, and not that we somehow needed even more low quality lines of code.

(Disclaimer: not talking about passive AI bug analysis here, just using AI to write actual code.)

Quoting studies to actually back up one’s point is in my opinion far less of an echo chamber and a fantasy than anecdotes of “but for me it feels faster”. Especially when AI is known to slow people down while making them feel faster.

Are you asking me to reject my professional daily reality?!

Can you point me to a single field study that shows programmers become faster and not just feel faster, and that doesn’t come with some caveat like they haven’t tested AI coders vs non-AI coders, or coders without significant AI exposure before (since otherwise it won’t rule out simply becoming dependent)?

Even if you could find one, and I was unable to so far, it doesn’t change that:

1. you are probably faster by verbatim plagiarizing somebody’s other project at a large scale, and

2. by making yourself addicted and reliant on the AI where your own skill is eroding: https://www.404media.co/software-developers-say-ai-is-rotting-their-brains/ (if you get a paywall: https://archive.is/tHq80 ) and

3. by having a higher rate of bugs in your code no matter how carefully you review it https://www.coderabbit.ai/blog/state-of-ai-vs-human-code-generation-report which especially for security sensitive projects may have dire long term consequences, and

4. by encouraging the environmental destruction brought on in particular by the training of new models.

Two caveats:

1. Keep in mind more lines of code is not a useful metric for faster project completion and faster maintenance task completion, especially for code bases that are already large.

2. I’m merely speaking about using LLM code in your project, so for example LLM auto completion or copy&pasting code from a chatbot. I’m mot talking about LLM code reviews that point out issues in natural language.

https://machinelearning.apple.com/research/illusion-of-thinking It’s not surprising LLMs keep messing up in what seem to be the most braindead ways.

LLMs seem to be inherently dumb: https://machinelearning.apple.com/research/illusion-of-thinking

And from what I can find in recent studies, no, they didn’t suddenly get smart. They just plagiarize slightly better: https://www.sciencedirect.com/science/article/pii/S2949719123000213#b7

We found that the models that consistently output the highest-quality text are also the ones that have the highest memorization rate.

AI code is pretty unusably bad for long term use anyway https://medium.com/@dumaysacha/i-saw-the-horror-of-ai-and-coderabbit-ai-did-too-a09622ac85de so best solution is to just to handwrite proper code as before. It’s not like we ever had much of an output problem in most coding industries, it was always a quality and bugs problem.

https://cssdoom.wtf/

My condolences.

Github seems to be down a lot, too, although perhaps not their pages part. Perhaps you could try to have just the pages in some other place?

I like that I can read this as you stating you use Atlassian yet hate Gitlab, and the statement still works either way 😅