There is something similar happening with AI and most people don't understand it and we don't have enough long term studies for data to make conclusions on this yet. Engineers sounding an alarm here but they also don't have the data to support their worry. Certainly some of the lower level understanding of coding languages aren't being acquired and maintained as much as before. Whether that underlying knowledge actually needs to take up the learning bandwidth that it was taking is the real question. My hunch is that it doesn't and that dropping it makes room for higher level concepts and understanding that are actually more important given modern tooling, the same way we don't actually need to know binary and assembly to be effective engineers, but time will tell.

Automated triage can filter obvious spam, which was already fast and easy for humans to do. The hard part is independently reproducing a plausible finding and assessing its actual impact. If LLMs could already do that reliably, then the slop report problem wouldn't exist in the first place.

Disappointing to see this is the future they are trying to invest in.

This is the kind of thing that loses cases, even if they were legitimate at first. Seems like the prosecutor is desperate charging for the phone wipe cause they didn't have any evidence of terrorism, child-pornography, etc. The problem they have now is given he was in custody and agents pressured him to provide the passcode that they then incompetently put into the phone, the fact that they denied him a lawyer multiple times means there is a very strong argument that his rights were violated. Typically, courts suppress any evidence when there is a violation like this with someone in custody. So the compelled passcode, the phone's reaction when that passcode was entered, and the agents' testimony describing the supposed wipe would be thrown out by most judges. What's left for the prosecution after this is jack and shit, but jack left town.

Your examples show that verification is sometimes inadequate, not that it requires full productive competence. The conclusion doesn't generalize.

It present delegation as absence of power/agency. While I think there is some kernel of truth in here, the claim "it's not empowering to hand off the details" quickly falls apart when we consider real examples. If it were true, it would mean any leader who coordinates an organization isn't empowered through delegation. We know that's false. The general claim doesn't hold at all. Somewhere in there is an argument for engineers losing something in the hand off, but it isn't clearly articulated.

How would they establish that a project "mostly" consists of AI-written code? Lines, commits, tokens, or architectural importance? There is no reliable detector, and metadata only catches people who honestly disclose their tool use. In practice, this only creates an incentive to conceal AI usage.

Authorship is also a poor proxy for quality. Human-written codebases routinely contain copied patterns, unnecessary abstractions, stale comments, superficial tests, security vulnerabilities, and large subsystems nobody seems to understand. Many mature projects exhibit the exact same problems attributed to LLM-generated code with the technical debt they've built up over time. Slop isn't new or unique to generated code. It is extremely common, especially in beginner, hobby, and abandoned projects which have historically served as a critical part of the open source software community.

The copyright justification is particularly weak. Lack of copyright protection does not make code non-free. Public domain source is still free software. A particular output might reproduce protected third-party code, but that requires evidence about that output. It isn't established merely by showing that an LLM was involved. If provenance were the true concern, then allowing some undefined minority of AI-generated code doesn't solve it. A single copied component can create a real licensing problem. So, the argument immediately falls apart when they qualify it with "mostly".

I don't use Codeberg, so I have no personal stake in this, but I'm surprised its community adopted such a vague and practically unenforceable policy. The argument that Codeberg has limited resources and cannot host endless disposable projects sounds legitimate until you consider how this policy could be enforced. Either someone must investigate repositories and infer their production history from circumstantial evidence, which is expensive and unreliable, or enforcement will be selective and complaint-driven.

If resource consumption, abandonment, or low-effort projects are the actual problems, Codeberg should regulate those observable problems directly though I'd argue targeting "low-effort" projects is as problematic as this policy suffering from many of the same issues. As written, the policy seems more likely to punish honest disclosure than to actually prevent harmful or infringing code.

Academia has a long history of lagging behind acceptance of new cognitive tools where they claim to want to defend the students, but instead defend the assignments of the past at the expense of the students. Calculators were treated as threats to learning, even though they ultimately freed students to focus on higher-level math and provably improved their abilities across many different studies. Internet sources were dismissed as less legitimate than books, as if “published in an outdated book from the 70s” magically made it more trustworthy than the most scrutinized reference sources online.

It is not clear from the article exactly how much of this course falls into that category, but if the answers can be produced trivially with a prompt and chatgpt, then maybe memorizing that material is no longer the right educational target. Academia desperately needs to redesign itself around AI as a cognitive tool students should be trained to leverage. If a question is trivially answered by a prompt with it, then you need harder questions that actually require students to push beyond that. Simply removing AI from the equation, calling it cheating, and pretending that it isn't an ever-present asset people are expected to leverage in real life is naive and just repeats the mistakes of the past.

Oh really? If LLMs were as good as almost any security researcher then you wouldn't be getting flooded by bullshit reports from them. You'd be receiving legitimate reports instead.