Tootfinder

I strongly object (with peace to @…!) to that first sentence.
This is self-destruction. This is NOT how the US “ensures the US maintains a leading role in the AI race.” Please. It is no such thing. Even if you are all roses and rainbows about AI, this isn’t how any kind of leadership or innovation works.
This — every part of this, the top-down ideological policing, the war on inclusion, the authoritarian jag, all of it — is destroying the very things that put the US in its tech leadership position in the first place. “Leadership role?!” Spare me.
1/2 https://toad.social/@KimPerales/114908466460356102

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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Ok #mastoadmin #selfhost #S3 aficionados. I don’t have any experience with object storage providers. I am looking to move my self-host mastodon account here from local storage on my 2007 #footimac to my account at leaseweb. Could you help me with the answers to their queries? I suggested, completely off the top of my head and without experience, i would need 1.5TB and low bandwidth. Not knowing if that makes any sense.
“ for a 1.5 TB provision with low bandwidth usage, we can provide an estimate once we have more context on access patterns, regions, and any specific requirements.”
Thanks for any help!
#askfedi #fedihelp
ping: @…

2/2 I continued blogging Alberniweather and on FB and Twitter but I gradually removed my personal self from Facebook and eventually during the Pandemic, I decided the Facebook environment was just too toxic even for weather stuff and I shut down my page and left Facebook completely.
The impact on traffic to Alberniweather.ca and its prominence in the community was, and still is, significant.
I have diehard followers, many who have become friends over the years, I still get the odd call from media, or even the public about random weather things.
I have good connections with a few folks at Environment Canada (though their staff have become thinner and more transient :(
and major events still get spikes of local traffic but I since about 2022, and after I removed myself from Twitter that year, I don’t blog nearly as much. I would do a few posts in a week, and then go months without posting. I just got out of the habit I guess.
But I am still interested in the weather. I still feel like Alberniweather is a useful service for people in my community. I still feel a willing obligation to inform people about the weather and I believe I am trusted to do so by the public and local leaders. I’ve never made any money at it, I sold ad space on the website for a few years but it wasn’t worth the hassle and I didn’t feel comfortable taking the money when I was councillor. I have had some generous spontaneous donations at times.
But mainly I do it because it’s interesting, and I hope it is useful for people especially when people are looking for information during a major event.
The highest traffic I have ever had on Alberniweather pre-FB exit was the local Dog Mountain forest fire in 2015.
post-FB exit: the #underwoodfire
People want easy access to reliable local, trusted, information.
Large media orgs have mostly given up on this.
I am grateful we still have an active local newspaper and radio and that both trust me and I trust them.
@… @…

"They’re unknowingly becoming the bad guys”: AI-powered bounty hunters think they’re helping, but their fabricated bug reports are overwhelming solo maintainers like cURL’s Daniel Stenberg—who’s paid $92K for real flaws and now may scrap the program.
https://www.

Addiction (Speculatve)
Kind of a fucked-up metaphor, but I was thinking yesterday that parenting is a lot like addiction. If you separate me from my child, I'll take completely irrational and desperate actions to get them back, driven by a deep instinct that goes well beyond "love." I'll also make self-disadvantageous long-term decisions like forgoing sleep, working an extra job, or quitting a job to do some combination of providing for and/or being present with my child.
Even in parenting situations where love is absent, and beyond, I think, the possessiveness that sometimes festers in those situations, there's often (although not always) a craving for simple presence of the child.
In a healthy relationship, there's a whole lot more than this, but it's interesting to me that the same obsessive craving and absolute priority that we think of as diseased and/or monstrous in someone addicted to a hard drug can be healthy in the right context (that is, when it doesn't contribute to abusive or twisted parental relationships but instead exists alongside a healthy amount of love and respect).
Makes me wonder if there are ways to have a truly healthy drug addiction, although I recognize the answer might well be "no" and that even if it's "technically/theoretically yes" it might still be harmful to hype up or even merely discuss that possibility since it might help addicted people in harmful addictions more easily justify inaction. At minimum I think any "yes" answer here involves assuming utopian-level differences from our current society.
#Parenting #Addiction

Series A, Episode 01 - The Way Back
BLAKE: No drugs!
HAVANT: A mild sedative to help you to sleep. You must rest.
BLAKE: No! No drugs.
HAVANT: All right, no drugs. Now try not to think anymore. Don't worry, we'll get it sorted out.
https://blake.torpidity.net/m/101/117

Library Comic
librarycomic.com
#Librarians are awesome.

Just saw this:
#AI can mean a lot of things these days, but lots of the popular meanings imply a bevy of harms that I definitely wouldn't feel are worth a cute fish game. In fact, these harms are so acute that even "just" playing into the AI hype becomes its own kind of harm (it's similar to blockchain in that way).
@… noticed that the authors claim the code base is 80% AI generated, which is a red flag because people with sound moral compasses wouldn't be using AI to "help" write code in the first place. The authors aren't by some miracle people who couldn't build this app without help, in case that influences your thinking about it: they have the skills to write the code themselves, although it likely would have taken longer (but also been better).
I was more interested in the fish-classification AI, and how much it might be dependent on datacenters. Thankfully, a quick glance at the code confirms they're using ONNX and running a self-trained neural network on your device. While the exponentially-increasing energy & water demands of datacenters to support billion-parameter models are a real concern, this is not that. Even a non-AI game can burn a lot of cycles on someone's phone, and I don't think there's anything to complain about energy-wise if we're just using cycles on the end user's device as long as we're not having them keep it on for hours crunching numbers like blockchain stuff does. Running whatever stuff locally while the user is playing a game is a negligible environmental concern, unlike, say, calling out to ChatGPT where you're directly feeding datacenter demand. Since they claimed to have trained the network themselves, and since it's actually totally reasonable to make your own dataset for this and get good-enough-for-a-silly-game results with just a few hundred examples, I don't have any ethical objections to the data sourcing or training processes either. Hooray! This is finally an example of "ethical use of neutral networks" that I can hold up as an example of what people should be doing instead of the BS they are doing.
But wait... Remember what I said about feeding the AI hype being its own form of harm? Yeah, between using AI tools for coding and calling their classifier "AI" in a way that makes it seem like the same kind of thing as ChatGPT et al., they're leaning into the hype rather than helping restrain it. And that means they're causing harm. Big AI companies can point to them and say "look AI enables cute things you like" when AI didn't actually enable it. So I'm feeling meh about this cute game and won't be sharing it aside from this post. If you love the cute fish, you don't really have to feel bad for playing with it, but I'd feel bad for advertising it without a disclaimer.

It's time to lower your inhibitions towards just asking a human the answer to your question.
In the early nineties, effectively before the internet, that's how you learned a lot of stuff. Your other option was to look it up in a book. I was a kid then, so I asked my parents a lot of questions.
Then by ~2000 or a little later, it started to feel almost rude to do this, because Google was now a thing, along with Wikipedia. "Let me Google that for you" became a joke website used to satirize the poor fool who would waste someone's time answering a random question. There were some upsides to this, as well as downsides. I'm not here to judge them.
At this point, Google doesn't work any more for answering random questions, let alone more serous ones. That era is over. If you don't believe it, try it yourself. Between Google intentionally making their results worse to show you more ads, the SEO cruft that already existed pre-LLMs, and the massive tsunami of SEO slop enabled by LLMs, trustworthy information is hard to find, and hard to distinguish from the slop. (I posted an example earlier: #AI #LLMs #DigitalCommons #AskAQuestion