@keithjgrant@front-end.socialMy top #CSS tip is this: treat it like you treat other code.
* Once it works, clean it up before committing code
* Refactor as needed when you make changes
* Investigate & understand the root cause of bugs before fixing them
@thomasfuchs@hachyderm.ioThat some people find “text box that spits out a broken version of what they want and then you yell at it until it’s sufficing” more convenient than using existing tools says a lot more about existing tools than about the text box.
E.g. programming has long been extremely overwrought and complex for no reason other than enterprise wankery and gatekeeping, see the various frameworks from Big Tech.
The same companies now rent you expensive tools that “make programming easy”, while their overly complex frameworks and environments are free of course.
Instead of fixing the problem we’re now having two problems.
(The same is true in other industries.)
@jkohlmann@mastodon.socialHow funny would it be if this turned in to a second Silicon Valley industry-wide wage fixing scandal https://www.bloomberg.com/news/articles/2025-07-07/apple-loses-its-top-ai-models-executive-to-meta-s-hiring-spree
@tiotasram@kolektiva.socialShould 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
@keithjgrant@front-end.socialMy top #CSS tip is this: treat it like you treat other code.
* Once it works, clean it up before committing code
* Refactor as needed when you make changes
* Investigate & understand the root cause of bugs before fixing them
@mapto@qoto.orgAi-fixing jobs are well-paid, but they have a huge AI-education element in them.
The BBC writes about several stories of AI firefighting.
https://www.bbc.com/news/articles/cyvm1dyp9v2o
@simon_brooke@mastodon.scotMy bignum implementation is broken, but how broken is it? Understanding how it comes to be broken is the first stage towards fixing it.
( 10000000000000000000 10000000000000000000)
prints the same value as
(* 2 10000000000000000000)
which is
1,553,255,926,290,448,384
It's the wrong value, but... is it the arithmetic that's wrong? I've been stuck on this for a very long time, but I begin to think that it's not.
The reader is failin…
@tante@tldr.nettime.orgI wished I could disagree with this post but sadly I cannot. I had high hopes for Matrix and still use it but boy is it not fixing the core and conceptual issues.
https://shkspr.mobi/blog/2025/07/im-never-going-back-to-matrix/
@Gord1i@fosstodon.orgEvergreen post - the longer I work in government, the more I end up repeating bits verbatim
https://theatl.social/@ShmosKnows/114881277746999591
@nobodyinperson@fosstodon.orgToday I've been fighting again with this absolutely stupid :nixos: #NixOS bug that dates back nearly 20 years now, which prevents you from naming an executable 'log'. Yes, it is NOT possible on NixOS. 🤦 🤦 🤦 And working around it is beyond frustrating. Fixing it requires a mass rebuild, so it's not that simple. 😩 😂 It's so terrible but also hilarious...
Issue:
@markhburton@mstdn.socialThis from IPPR on the greatly increased costs of UK government debt servicing seems reasonable from IPPR.
1) Tax the big 4 banks on the windfall profits that the government is, effectively, funding.
2) Slow down the QE buy-back.
I would also think that the government/bank could, as an exceptional measure, buy back some of the long term gilts using 'monetary financing' aka 'printing money'.
Not something to be done willy-nilly, but here it could get govt ou…
@michabbb@social.vivaldi.net@… it's free, so use it for the mass, not for the difficult tasks... Fixing simple phpstan errors for example works very well...
@arXiv_quantph_bot@mastoxiv.pageTruncating loopy tensor networks by zero-mode gauge fixing
Ihor Sokolov, Yintai Zhang, Jacek Dziarmaga
https://arxiv.org/abs/2508.00338 https://arxiv.org/p…
@fanf@mendeddrum.orgfrom my link log —
Fixing the traffic jam in the science of technology safety.
https://citizensandtech.org/2025/04/fixing-science-tech-safety/
saved 2025-05-15
@rasterweb@mastodon.socialI wrote up a post about fixing a watch band because I really need to remember it's better to repair the things you have instead of binning it and buying a new one...
https://rasterweb.net/raster/2025/08/17/fixing-a-watch-band/
@midtsveen@social.linux.pizzaMy hyperfixation on fixing this damn post is driving me completely insane right now. I’m just going to leave it as it is and call it done. Go boost!
https://social.linux.pizza/@midtsveen/114768454047682245
@marcus@hachyderm.ioSomething construction has in common with programming; tearing down and making from scratch always seems more tempting than fixing and restoring what exists. Surely we will get it right this time. Yet these rebuilds have an enormous cost. We need more of a continuous improvement mindset in both of these fields. For more sustainable development
@ruario@vivaldi.netAlso thanks to all the @… users who tested the RC. It is due to your feedback that 3 of the 4 fixes listed were prioritised for fixing in time for the release. 🙇
@arXiv_hepth_bot@mastoxiv.pageQuantum carrollian bosonic strings
Jos\'e Figueroa-O'Farrill, Emil Have, Niels A. Obers
https://arxiv.org/abs/2509.04397 https://arxiv.org/pdf/2509…
@tiotasram@kolektiva.socialShould we teach vibe coding? Here's why not.
2/2
To address the bigger question I started with ("should we teach AI-"assisted" coding?"), my answer is: "No, except enough to show students directly what its pitfalls are." We have little enough time as it is to cover the core knowledge that they'll need, which has become more urgent now that they're going to be expected to clean up AI bugs and they'll have less time to develop an understanding of the problems they're supposed to be solving. The skill of prompt engineering & other skills of working with AI are relatively easy to pick up on your own, given a decent not-even-mathematical understanding of how a neutral network works, which is something we should be giving to all students, not just our majors.
Reasonable learning objectives for CS majors might include explaining what types of bugs an AI "assistant" is most likely to introduce, explaining the difference between software engineering and writing code, explaining why using an AI "assistant" is likely to violate open-source licenses, listing at lest three independent ethical objections to contemporary LLMs and explaining the evidence for/reasoning behind them, explaining why we should expect AI "assistants" to be better at generating code from scratch than at fixing bugs in existing code (and why they'll confidently "claim" to have fixed problems they haven't), and even fixing bugs in AI generated code (without AI "assistance").
If we lived in a world where the underlying environmental, labor, and data commons issues with AI weren't as bad, or if we could find and use systems that effectively mitigate these issues (there's lots of piecemeal progress on several of these) then we should probably start teaching an elective on coding with an assistant to students who have mastered programming basics, but such a class should probably spend a good chunk of time on non-assisted debugging.
#AI #LLMs #VibeCoding
@catsalad@infosec.exchangeThe kind of IT support we deserve
@arXiv_mathPR_bot@mastoxiv.pageAn iterated $I$-projection procedure for solving the generalized minimum information checkerboard copula problem
Ivan Kojadinovic, Tommaso Martini
https://arxiv.org/abs/2509.02829
@arXiv_csSE_bot@mastoxiv.pageRePaCA: Leveraging Reasoning Large Language Models for Static Automated Patch Correctness Assessment
Marcos Fuster-Pena, David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez
https://arxiv.org/abs/2507.22580
@wyri@toot-toot.wyrihaxim.usSomething completely different, tho. The node on my desk is named television for a reason; the idea is to attach a screen to it to display relevant information for that moment. Finally took the time to finish the first step for that yesterday by fixing the #RaspberryPi hooked up to a screen.
@adulau@infosec.exchangeSomething that’s been bothering me for years in the security world: why do researchers demand bug bounties for vulnerabilities in open source projects, when the very contributors maintaining and fixing those issues get nothing, just goodwill?
It feels deeply unfair. The burden falls on unpaid maintainers, yet bounty hunters get rewarded. If you want a paid bounty, maybe help fund the people who actually fix the mess too.
@arXiv_csIT_bot@mastoxiv.pageAn Efficient Alternating Minimization Algorithm for Computing Quantum Rate-Distortion Function
Lingyi Chen, Deheng Yuan, Wenyi Zhang, Hao Wu, Huihui Wu
https://arxiv.org/abs/2507.19920
@memeorandum@universeodon.comTrump claims credit for fixing Social Security as it barrels to insolvency (Meryl Kornfield/Washington Post)
https://www.washingtonpost.com/politics/2025/08/14/trump-social-security-90th-annniversary/
http://www.memeorandum.com/250814/p133#a250814p133
@samir@functional.computer@… @… If you can’t fix the problem with culture, fixing it with a system seems like the next best method.
@khalidabuhakmeh@mastodon.socialCrazy when you’re relieved that fixing your A/C is only going to cost you $500. I guess it could have been waaaaaay worse.
@rasterweb@mastodon.socialNot a great day for cycling! Had a flat so I spent 20 minutes fixing it. Hoping I’ll get better/faster at some point. 3D printed tire levers helped. Just rode around the neighborhood since I was short on time and it was windy.
Took the opportunity to upgrade my front tire to one @… gave me. Still need to deal with the fork issue. :(
@tml@urbanists.socialHaha, this was a rather fundamental misunderstanding (or more like brain glitch) of moment of inertia in Microsoft Flight Simulator, and only now they are fixing it. Well, better late than never. https://devsupport.flightsimulator.com/t/payload-moments-of-…
@jamesthebard@social.linux.pizzaMultipathing: it makes fixing cabling issues so much easier. Swapped out the 2x2m fiber patches to the NAS with 2x5m ones because the old ones were just a smidge strained. Had absolutely no issues swapping them out one by one and no downtime.
#homelab #multipath
@mxp@mastodon.acm.org@… @… And it shows how desperate they are to come up with something NEW and EXCITING. Fixing bugs is boring, let’s put all of our energy into making things different!
Of course, it’s an industry-wide problem, and why they’re going all i…
@mxp@mastodon.acm.org@… @… And it shows how desperate they are to come up with something NEW and EXCITING. Fixing bugs is boring, let’s put all of our energy into making things different!
Of course, it’s an industry-wide problem, and why they’re going all i…
@arXiv_grqc_bot@mastoxiv.pageSemiclassical polymer field with a cubic potential in cosmology
Ahsan Mujtaba, Syed Moeez Hassan
https://arxiv.org/abs/2508.16416 https://arxiv.org/pdf/250…
@bammerlaan@mastodon.nlMy favourite hammock for on our balcony broke and landed me on my ass, last month. Just got around to fixing it with parts of a new lashing strap that's rated for much more weight than me. The hammock itself is still in good shape, so this should last me some more years, now.
(I got the hammock from Kickstarter years ago, in a time when I was still fascinated by that website...
@rasterweb@mastodon.socialI was feeling pretty drained last night and wasn't sure if I should bike to work today. My knee was really bugging me going up steps (more than usual) and I decided I would wait til morning to decide, based on how I felt.
I felt totally fine this morning. Reset! I think it's because I walked nearly 5 miles yesterday, and also spent a lot of time on the floor fixing a laser cutter.
@arXiv_mathOC_bot@mastoxiv.pageOptimal Transport on Lie Group Orbits
Bahar Taskesen
https://arxiv.org/abs/2508.09377 https://arxiv.org/pdf/2508.09377
@arXiv_csSE_bot@mastoxiv.pageMapping NVD Records to Their VFCs: How Hard is it?
Huu Hung Nguyen, Duc Manh Tran, Yiran Cheng, Thanh Le-Cong, Hong Jin Kang, Ratnadira Widyasari, Shar Lwin Khin, Ouh Eng Lieh, Ting Zhang, David Lo
https://arxiv.org/abs/2506.09702
@jamesthebard@social.linux.pizzaThe first MS-A2 node is up and it only took the entire day. After fixing a ton of networking issues across the homelab I've got all the VMs running on the new node. The old nodes have been decomissioned which kinda marks a milestone. Still need to get the other two new nodes into the cluster, but should be fairly straightforward and can wait until tomorrow.
#homelab
@arXiv_grqc_bot@mastoxiv.pageCanonical Electrodynamics in Ashtekar-Barbero variables
Federica Fragomeno, Saeed Rastgoo
https://arxiv.org/abs/2507.06276 https://ar…
