@lightweight@mastodon.nzoss.nzHoly cow, only 24 hrs after putting it out in the yard, the silvereyes ('tauhou' in te reo Māori, which means 'new arrivals' as they've migrated here to Aotearoa without human assistance) have already found the new sugar water feeder! I'm delighted 🤩🤗 Will try to get some better pictures.
@maxheadroom@hub.uckermark.socialAs usual we watched the "Stoertebekker" stage play at Ralswiek on Rügen Island. The stage setups and wardrobes were as awesome as always. They really try to improve on that every year and come up with clever mechanisms to transform the building for the various acts of the play. The script this year left room for desire ;) Nevertheless a happing worth attending
htt…
@mxp@mastodon.acm.org@… Thanks, good points! Yeah, you’re right, that's not what auto-revert-mode is for. I’ll have another look and try to narrow down the problem.
@mxp@mastodon.acm.org@… Thanks, good points! Yeah, you’re right, that's not what auto-revert-mode is for. I’ll have another look and try to narrow down the problem.
@muz4now@mastodon.worldScientists say your “mind” isn’t confined to your brain, or even your body
http://qz.com/866352/scientists-say-your-mind-isnt-confined-to-your-brain-or-even-your-bod…
@blakes7bot@mas.torpidity.netSeries C, Episode 04 - Dawn of the Gods
AVON: Our hero lives.
TARRANT: At least he didn't try to get into a spacesuit.
AVON: I look upon self-interest as my great strength. Vila.
TARRANT: One day, Avon, I may have to kill you.
https://blake.torpidity.net/m/304/107
@samvarma@fosstodon.orgAnd another thing for those of you that get discouraged trying to learn music:
my brain is a weird old thing… The first day I try something new it's just a massive struggle but give it 24 hours to percolate, and the next day it's like 100% easier
@niklaskorz@rheinneckar.social
@samir@functional.computer@… @… This looks so cool, and I’m up for more integration into nvim. Gonna try it. 😊
And now I have another cool blog to follow, so, bonus.
@detondev@social.linux.pizzasend me your favorite math jokes and shitposts, at any level, and ill try to understand them. im gonna take yet another shot at seriously getting better with math and need places to start
@penguin42@mastodon.org.ukMy JS Krups is resisting netbooting. I see it doing a DHCP discover, and I see Kea responding with an offer, but then it just sends another Discover. Hmph. I can get to the serial console and doing boot net from there doesn't help; none of the keyboard shortcuts for network diag etc seem to work (except the one that displays the help for it...). So I took the flash SIM out and now it boots to Net rather than flash by default; alas with the same DHCP behaviour. Time to try isc-dhcp.…
@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
@grifferz@social.bitfolk.comThere's no putting the genie back in the bottle, but education is [yet another part of our society that is] completely unprepared to deal with generative AI.
Teachers Are Not OK
https://www.404media.co/teachers-are-not-ok-ai-chatgpt/
@izzychambers@vivaldi.net@… @… or add a partition for data, in case you decide to try another distro.
@bobmueller@mastodon.world
@finlaydag33k@social.linux.pizza>Order something online.
>Mom and dad home downstairs, eating dinner.
>Tracker updates.
>"You weren't home, so we'll try again at another date"
Jepp... The driver literally just didn't even bother actually delivering the thing.
And ofc, DHL is so confident in their delivery people... That they removed their Twitter acc a while ago (so you can't publically shame them for it). : D
@jensilber@mastodon.socialThis week's #Monsterdon pick (which I'm not watching tonight) is a movie I remember seeing in the theatre as a child? I didn't understand it at all at the time and was kind of freaked out by it then, so I've never really given it another try.
@azonenberg@ioc.exchangeDid another round of strong nitric on the AWR6843 to remove the last bits of flip chip underfill followed by some dilute nitric to eat the solder bumps.
The die surface is now clean with only a little bit of damage from the many etch cycles (I'll have to try a different process next time around).
But the large die is slightly warped from stresses so it's proving to be a pain to image without focus stacking.
@UP8@mastodon.socialI found the discus to be challenging because of the busy net in the background but you see people throwing can look like Greek statues so I want to give it another try
#sports #photo #photography…
@blakes7bot@mas.torpidity.netSeries D, Episode 09 - Sand
SOOLIN: Keller was right again.
AVON: Vila, try and bring Tarrant back up.
VILA: [He tries.] Nonoperational.
AVON: Well now, none of us is going to faint with amazement at that. Are we?
SOOLIN: What's the next move?
https://blake.torpidity.net/m/409/147
@gedankenstuecke@scholar.socialAs we're getting more into hikes around our new home – and I record tracks of them to also improve #openstreetmap – I've made a #wanderer profile at @… to share some nice trails.
It's an open source trail/track sharing platform that's federated thanks to #ActivityPub, so if you can read this post, you can follow my profile there too!*
* that is except you read this through the bridge to BlueSky, then you're out of luck and should give the actual Fediverse another try 😉
@blakes7bot@mas.torpidity.netSeries D, Episode 11 - Orbit
VILA: This had better work.
AVON: Have you cleared the governors?
VILA: I think so. Try it now.
AVON: Switching to manual. Maximum power on all drives.
[Biodome]
PINDER: Egrorian.
https://blake.torpidity.net/m/411/397 B7B3
@blakes7bot@mas.torpidity.netSeries B, Episode 05 - Pressure Point
AVON: If I can fire at ground level, the beam radiation should be great enough to cut a narrow channel through the sensor mesh. That would give you eight seconds to get from here to the blockhouse. The mesh would start its repair cycle instantly. Take one step off the channel and you're dead. I know it's a slim chance, but it's the best I can come up with.
BLAKE: Let's try it.
@blakes7bot@mas.torpidity.net#Blakes7 Series D, Episode 11 - Orbit
VILA: I think so. Try it now.
AVON: Switching to manual. Maximum power on all drives.
[Biodome]
PINDER: Egrorian.
EGRORIAN: They've broken the autocontrol and switched to manual. We've allowed for that.
@blakes7bot@mas.torpidity.net#Blakes7 Series D, Episode 11 - Orbit
VILA: I think so. Try it now.
AVON: Switching to manual. Maximum power on all drives.
[Biodome]
PINDER: Egrorian.
EGRORIAN: They've broken the autocontrol and switched to manual. We've allowed for that.
