@aral@mastodon.ar.al2025-10-24 07:12:50
Hi everyone, I just wanted to say thank you again for your amazing support for our fundraiser to fund @…’s private scholarship at the University of Milan and get her evacuated from Gaza in November.
Thanks to you, we were able to raise over $15,000 in under 24 hours and meet our goal.
Joy can’t thank you herself at the moment as her family has mov…
@netzschleuder@social.skewed.de2025-09-24 09:00:04
faculty_hiring_us: Faculty hiring networks in the US (2022)
Networks of faculty hiring for all PhD-granting US universities over the decade 2011–2020. Each node is a PhD-granting institution, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2020). This dataset is divided into separate networks for all 107 fields, as well as aggregate networks for 8 domains, and an overall network for …
@yaya@jorts.horse2025-10-23 18:50:46
we are the clapton, always anti-fascist, fuck the terf FA, LET THEM PLAY
#claptoncfc #ccfc #ftfa #fedifc
@arXiv_astrophSR_bot@mastoxiv.page2025-09-23 10:03:30
Magnetic flux ropes within reconnection exhausts close to the centers of heliospheric current sheets near the Sun
Dae-Young Lee, Dooyoung Choi, Kyung-Eun Choi, Sung Jun Noh
https://arxiv.org/abs/2509.16849
@tiotasram@kolektiva.social2025-08-04 15:49:00
Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
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.
1/2
@arXiv_astrophHE_bot@mastoxiv.page2025-09-24 09:43:54
Three-dimensional Search for Annihilating Dark Matter in CBe dSph with the MAGIC Telescopes
Stefan Fr\"ose (for the MAGIC Collaboration), Dominik Martin El\"asser (for the MAGIC Collaboration), Hendrik Hildebrandt, Elisa Pueschel
https://arxiv.org/abs/2509.18861
@hex@kolektiva.social2025-10-20 08:05:15
Some leftists have criticized #NoKingsDay2 as useless. Though it was the largest protest in US history, it didn't change anything. I would go further to say that protests like these generally won't change anything. Dictators aren't forced to step down by 2% of the population coming out for one day. If they're forced to step down by protests, those protests are sustained. They are every single day. They are accompanied by general strikes.
We've been watching that happen all over the world. Portland in 2020 gave us a taste of that in the US. The George Floyd Rebellion was the type of resistance that actually brings down dictators like Trump. Occasional protests, no matter how large, can simply be ignored. That is precisely the reason the US developed a militarized police force in the first place. You need more, more than the largest protests in US history, more than Occupy, more than the resistance of the 60's and 70's, more than, and different from, anything we've seen in our lives.
And yet... Each protest has grown, and grown bolder. Some have grown more persistent. If you think of protest as the path to achieve change, you will lose. It is not. But it is a path to escalate. Some people, some otherwise comfortable white folks, came out for their first time. Some people got pepper sprayed for the first time. Some people questioned authority, stood up for the first time, and have had an experience that will radicalize them for the rest of their lives.
Protest is not useful in and of itself. It is training. It's making connections. Authoritarian regimes rely on the illusion of compliance, so visual resistance does actually undermine their power.
Liberals like to teach that non-violence is all about staying peaceful no matter what, that there's some way that morality simply overwhelms an enemy. I remember reading Langston Hughes' A Dream Deferred in high school. I said it was a threat. My teacher said, "you're wrong, he was a pacifist." Pacifism is a threat. If you can spit at me, beat me, shoot me, and I will not move, if I have the strength to absorb violence without flinching, without even rising to violence, what will happen when you push me too far?
What happens to a dream deferred?
Does it dry up
like a raisin in the sun?
Or fester like a sore—
And then run?
Does it stink like rotten meat?
Or crust and sugar over—
like a syrupy sweet?
Maybe it just sags
like a heavy load.
Or does it explode?
For peaceful resistance to work, there must be ambiguity. It must not be clear if or when the resistance will stop being peaceful. Peaceful resistance with no possibility of escalation is just cowardice.
My critique then is not so harsh as some other anarchists. If you think that protest alone will work, you're probably going to lose. If you are prepared to escalate, if you are prepared to absorb violence without flinching, then it could be possible for protest alone to topple the dictator. The cracks are already beginning to show.
And then what?
The problems that lead to the George Floyd uprising were never resolved. The problems that lead to Occupy where never resolve. The DAPL was built, protesters were maimed, it leaked multiple times (exactly as predicted). Segregation never went away, it only changed forms. The fact that immigrants have different courts and different rights means that anyone can be arbitrarily kidnaped and renditioned to an arbitrary country. We never did anything about the torture black site. FFS, people can still be stripped of their voting rights and slavery is still legal in the US. The people who control both parties in the US are killing our children and grand children with oil wars and climate change.
Toppling the dictator does nothing to resolve all of the problems that existed before him.
No, #NoKingsDay was absolutely not useless. #NoKings and related protests are extremely useful but they aren't sufficient. But, I think we still need to challenge the movement on two points:
How do you escalate after you're ignored or brutalized?
What do you demand after you win?
#USPol
@v_i_o_l_a@openbiblio.social2025-08-19 20:37:10
"Subscribe-to-Open Is Doomed. Here's Why" @ The Scholarly Kitchen: https://scholarlykitchen.sspnet.org/2025/08/18/subscribe-to-open-is-doomed-heres-why/
@netzschleuder@social.skewed.de2025-08-21 23:00:06
faculty_hiring_us: Faculty hiring networks in the US (2022)
Networks of faculty hiring for all PhD-granting US universities over the decade 2011–2020. Each node is a PhD-granting institution, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2020). This dataset is divided into separate networks for all 107 fields, as well as aggregate networks for 8 domains, and an overall network for …
