Tootfinder

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
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I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
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Reducing the weight of low exam scores may raise average grades but does not appear to impact equity gaps
Nicholas T. Young, Rebecca L. Matz, Eric F. Bell, Caitlin Hayward
https://arxiv.org/abs/2508.09906

Reducing the weight of low exam scores may raise average grades but does not appear to impact equity gaps
Students interpret grades as signals of their strengths, and grades inform students' decisions about majors and courses of study. Differences in grades that are not related to learning can impact this judgment and have real-world impact on course-taking and careers. Existing work has examined how an overemphasis on high-stakes exams can create equity gaps where female students and Black, Hispanic, and Native American students earn lower grades than male students and Asian and white students, re…

Verwarrend bericht in NRC, de kern is naar mijn mening dat we op geen enkele manier beloftes of toezeggingen van Meta kunnen controleren als het gaat om Whatsapp. Zeker niet als het gaat om informatiebeveiliging of privacy.
https://www.

Leest de nieuwe chatbot van WhatsApp mee met alle berichten in de groepschat? Meta zegt zelf van niet
Privacy: Onder Nederlandse gebruikers van berichtendienst WhatsApp ging deze week een alarmerend bericht rond. Heeft Meta echt toegang tot de inhoud van groepchats – inclusief telefoonnummers van gebruikers?

My Personal Privacy - Your resource for achieving better online privacy
Why privacy:
The right to privacy is a well-established, fundamental human right. It is necessary for a safe and just society. It is especially important in regions where intolerance and discrimination are backed by autocratic regimes. While you may not feel the need for privacy in all of your online activity, you should be able to achieve a high level of privacy when you need it. This website and the document…

#Blakes7 Series D, Episode 06 - Headhunter
ORAC: The survival of organic humanoid life is improbable.
SOOLIN: Are you talking about Vila and Tarrant?
ORAC: All organic humanoid life throughout the galaxy is at hazard.
https://

New results from fast timing iLGAD sensor on Timepix4
Daan Oppenhuis (on behalf of the Timepix4 telescope group)
https://arxiv.org/abs/2509.09308 https://a…

New results from fast timing iLGAD sensor on Timepix4
With the High-Luminosity Large Hadron Collider (HL-LHC) the number of collisions per bunch crossing increases. To cope with these high rates in the pixel trackers, per-pixel time measurements are required, which implies the need for fast sensors. The inverse Low-Gain Avalanche Detector (iLGAD) is one of the fast sensor options that is being investigated. This paper will show the results of an inverse Low-Gain Avalanche Detector (iLGAD) with a pitch of 55 \textmu m, a thickness of 250~\textmu m …

H-Net : Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages
Mehrdad Zakershahrak, Samira Ghodratnama
https://arxiv.org/abs/2508.05628

H-Net++: Hierarchical Dynamic Chunking for Tokenizer-Free Language Modelling in Morphologically-Rich Languages
Byte-level language models eliminate fragile tokenizers but face computational challenges in morphologically-rich languages (MRLs), where words span many bytes. We propose H-NET++, a hierarchical dynamic-chunking model that learns linguistically-informed segmentation through end-to-end training. Key innovations include: (1) a lightweight Transformer context-mixer (1.9M parameters) for cross-chunk attention, (2) a two-level latent hyper-prior for document-level consistency, (3) specialized handl…

Taiwan says TSMC will not have to pay 100% tariffs on US chip imports, as it has set up US plants; UMC may see reduced impact through its cooperation with Intel (Bloomberg)
https://www.bloomberg.com/news/articles/20

How popular media gets love wrong
Okay, so what exactly are the details of the "engineered" model of love from my previous post? I'll try to summarize my thoughts and the experiences they're built on.
1. "Love" can be be thought of like a mechanism that's built by two (or more) people. In this case, no single person can build the thing alone, to work it needs contributions from multiple people (I suppose self-love might be an exception to that). In any case, the builders can intentionally choose how they build (and maintain) the mechanism, they can build it differently to suit their particular needs/wants, and they will need to maintain and repair it over time to keep it running. It may need winding, or fuel, or charging plus oil changes and bolt-tightening, etc.
2. Any two (or more) people can choose to start building love between them at any time. No need to "find your soulmate" or "wait for the right person." Now the caveat is that the mechanism is difficult to build and requires lots of cooperation, so there might indeed be "wrong people" to try to build love with. People in general might experience more failures than successes. The key component is slowly-escalating shared commitment to the project, which is negotiated between the partners so that neither one feels like they've been left to do all the work themselves. Since it's a big scary project though, it's very easy to decide it's too hard and give up, and so the builders need to encourage each other and pace themselves. The project can only succeed if there's mutual commitment, and that will certainly require compromise (sometimes even sacrifice, though not always). If the mechanism works well, the benefits (companionship; encouragement; praise; loving sex; hugs; etc.) will be well worth the compromises you make to build it, but this isn't always the case.
3. The mechanism is prone to falling apart if not maintained. In my view, the "fire" and "appeal" models of love don't adequately convey the need for this maintenance and lead to a lot of under-maintained relationships many of which fall apart. You'll need to do things together that make you happy, do things that make your partner happy (in some cases even if they annoy you, but never in a transactional or box-checking way), spend time with shared attention, spend time alone and/or apart, reassure each other through words (or deeds) of mutual beliefs (especially your continued commitment to the relationship), do things that comfort and/or excite each other physically (anywhere from hugs to hand-holding to sex) and probably other things I'm not thinking of. Not *every* relationship needs *all* of these maintenance techniques, but I think most will need most. Note especially that patriarchy teaches men that they don't need to bother with any of this, which harms primarily their romantic partners but secondarily them as their relationships fail due to their own (cultivated-by-patriarchy) incompetence. If a relationship evolves to a point where one person is doing all the maintenance (& improvement) work, it's been bent into a shape that no longer really qualifies as "love" in my book, and that's super unhealthy.
4. The key things to negotiate when trying to build a new love are first, how to work together in the first place, and how to be comfortable around each others' habits (or how to change those habits). Second, what level of commitment you have right now, and what how/when you want to increase that commitment. Additionally, I think it's worth checking in about what you're each putting into and getting out of the relationship, to ensure that it continues to be positive for all participants. To build a successful relationship, you need to be able to incrementally increase the level of commitment to one that you're both comfortable staying at long-term, while ensuring that for both partners, the relationship is both a net benefit and has manageable costs (those two things are not the same). Obviously it's not easy to actually have conversations about these things (congratulations if you can just talk about this stuff) because there's a huge fear of hearing an answer that you don't want to hear. I think the range of discouraging answers which actually spell doom for a relationship is smaller than people think and there's usually a reasonable "shoulder" you can fall into where things aren't on a good trajectory but could be brought back into one, but even so these conversations are scary. Still, I think only having honest conversations about these things when you're angry at each other is not a good plan. You can also try to communicate some of these things via non-conversational means, if that feels safer, and at least being aware that these are the objectives you're pursuing is probably helpful.
I'll post two more replies here about my own experiences that led me to this mental model and trying to distill this into advice, although it will take me a moment to get to those.
#relationships #love

Overly academic/distanced ethical discussions
Had a weird interaction with @/brainwane@social.coop just now. I misinterpreted one of their posts quoting someone else and I think the combination of that plus an interaction pattern where I'd assume their stance on something and respond critically to that ended up with me getting blocked. I don't have hard feelings exactly, and this post is only partly about this particular person, but I noticed something interesting by the end of the conversation that had been bothering me. They repeatedly criticized me for assuming what their position was, but never actually stated their position. They didn't say: "I'm bothered you assumed my position was X, it's actually Y." They just said "I'm bothered you assumed my position was X, please don't assume my position!" I get that it's annoying to have people respond to a straw man version of your argument, but when I in response asked some direct questions about what their position was, they gave some non-answers and then blocked me. It's entirely possible it's a coincidence, and they just happened to run out of patience on that iteration, but it makes me take their critique of my interactions a bit less seriously. I suspect that they just didn't want to hear what I was saying, while at the same time they wanted to feel as if they were someone who values public critique and open discussion of tricky issues (if anyone reading this post also followed our interaction and has a different opinion of my behavior, I'd be glad to hear it; it's possible In effectively being an asshole here and it would be useful to hear that if so).
In any case, the fact that at the end of the entire discussion, I'm realizing I still don't actually know their position on whether they think the AI use case in question is worthwhile feels odd. They praised the system on several occasions, albeit noting some drawbacks while doing so. They said that the system was possibly changing their anti-AI stance, but then got mad at me for assuming this meant that they thought this use-case was justified. Maybe they just haven't made up their mind yet but didn't want to say that?
Interestingly, in one of their own blog posts that got linked in the discussion, they discuss a different AI system, and despite listing a bunch of concrete harms, conclude that it's okay to use it. That's fine; I don't think *every* use of AI is wrong on balance, but what bothered me was that their post dismissed a number of real ethical issues by saying essentially "I haven't seen calls for a boycott over this issue, so it's not a reason to stop use." That's an extremely socially conformist version of ethics that doesn't sit well with me. The discussion also ended up linking this post: https://chelseatroy.com/2024/08/28/does-ai-benefit-the-world/ which bothered me in a related way. In it, Troy describes classroom teaching techniques for introducing and helping students explore the ethics of AI, and they seem mostly great. They avoid prescribing any particular correct stance, which is important when teaching given the power relationship, and they help students understand the limitations of their perspectives regarding global impacts, which is great. But the overall conclusion of the post is that "nobody is qualified to really judge global impacts, so we should focus on ways to improve outcomes instead of trying to judge them." This bothers me because we actually do have a responsibility to make decisive ethical judgments despite limitations of our perspectives. If we never commit to any ethical judgment against a technology because we think our perspective is too limited to know the true impacts (which I'll concede it invariably is) then we'll have to accept every technology without objection, limiting ourselves to trying to improve their impacts without opposing them. Given who currently controls most of the resources that go into exploration for new technologies, this stance is too permissive. Perhaps if our objection to a technology was absolute and instantly effective, I'd buy the argument that objecting without a deep global view of the long-term risks is dangerous. As things stand, I think that objecting to the development/use of certain technologies in certain contexts is necessary, and although there's a lot of uncertainly, I expect strongly enough that the overall outcomes of objection will be positive that I think it's a good thing to do.
The deeper point here I guess is that this kind of "things are too complicated, let's have a nuanced discussion where we don't come to any conclusions because we see a lot of unknowns along with definite harms" really bothers me.