Tootfinder

Subtooting since people in the original thread wanted it to be over, but selfishly tagging @… and @… whose opinions I value...
I think that saying "we are not a supply chain" is exactly what open-source maintainers should be doing right now in response to "open source supply chain security" threads.
I can't claim to be an expert and don't maintain any important FOSS stuff, but I do release almost all of my code under open licenses, and I do use many open source libraries, and I have felt the pain of needing to replace an unmaintained library.
There's a certain small-to-mid-scale class of program, including many open-source libraries, which can be built/maintained by a single person, and which to my mind best operate on a "snake growth" model: incremental changes/fixes, punctuated by periodic "skin-shedding" phases where make rewrites or version updates happen. These projects aren't immortal either: as the whole tech landscape around them changes, they become unnecessary and/or people lose interest, so they go unmaintained and eventually break. Each time one of their dependencies breaks (or has a skin-shedding moment) there's a higher probability that they break or shed too, as maintenance needs shoot up at these junctures. Unless you're a company trying to make money from a single long-lived app, it's actually okay that software churns like this, and if you're a company trying to make money, your priorities absolutely should not factor into any decisions people making FOSS software make: we're trying (and to a huge extent succeeding) to make a better world (and/or just have fun with our own hobbies share that fun with others) that leaves behind the corrosive & planet-destroying plague which is capitalism, and you're trying to personally enrich yourself by embracing that plague. The fact that capitalism is *evil* is not an incidental thing in this discussion.
To make an imperfect analogy, imagine that the peasants of some domain have set up a really-free-market, where they provide each other with free stuff to help each other survive, sometimes doing some barter perhaps but mostly just everyone bringing their surplus. Now imagine the lord of the domain, who is the source of these peasants' immiseration, goes to this market secretly & takes some berries, which he uses as one ingredient in delicious tarts that he then sells for profit. But then the berry-bringer stops showing up to the free market, or starts bringing a different kind of fruit, or even ends up bringing rotten berries by accident. And the lord complains "I have a supply chain problem!" Like, fuck off dude! Your problem is that you *didn't* want to build a supply chain and instead thought you would build your profit-focused business in other people's free stuff. If you were paying the berry-picker, you'd have a supply chain problem, but you weren't, so you really have an "I want more free stuff" problem when you can't be arsed to give away your own stuff for free.
There can be all sorts of problems in the really-free-market, like maybe not enough people bring socks, so the peasants who can't afford socks are going barefoot, and having foot problems, and the peasants put their heads together and see if they can convince someone to start bringing socks, and maybe they can't and things are a bit sad, but the really-free-market was never supposed to solve everyone's problems 100% when they're all still being squeezed dry by their taxes: until they are able to get free of the lord & start building a lovely anarchist society, the really-free-market is a best-effort kind of deal that aims to make things better, and sometimes will fall short. When it becomes the main way goods in society are distributed, and when the people who contribute aren't constantly drained by the feudal yoke, at that point the availability of particular goods is a real problem that needs to be solved, but at that point, it's also much easier to solve. And at *no* point does someone coming into the market to take stuff only to turn around and sell it deserve anything from the market or those contributing to it. They are not a supply chain. They're trying to help each other out, but even then they're doing so freely and without obligation. They might discuss amongst themselves how to better coordinate their mutual aid, but they're not going to end up forcing anyone to bring anything or even expecting that a certain person contribute a certain amount, since the whole point is that the thing is voluntary & free, and they've all got changing life circumstances that affect their contributions. Celebrate whatever shows up at the market, express your desire for things that would be useful, but don't impose a burden on anyone else to bring a specific thing, because otherwise it's fair for them to oppose such a burden on you, and now you two are doing your own barter thing that's outside the parameters of the really-free-market.

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

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

Time for another "review". This one's hard. While the book was quite interesting, it required me to be quite open-minded. Still, I think it's worth mentioning:
Robert Wright — Nonzero: The Logic of Human Destiny
The book basically focused on a thesis that both biological evolution and cultural evolution are a thing, they are directional and this directionality can be explained together using game theory — as eventually leading to more non-zero sum games.
It consists of three chapters. The first one is is focused on the history of civilization. It features many examples from different parts of the world, which makes it quite interesting. The author argues that the culture inevitably is evolving as information processing techniques improve — from writing to the Internet.
The second chapter is focused on biological evolution. Now, the argument is that it's not quite random, but actually directed towards greater complexity — eventually leading to the development of highly intelligent species, and a civilization.
The third chapter is quite speculative and metaphysical, and I'm just going to skip it.
The book is full of optimism. Capitalism creates freedom — because people are more productive when they're working for their own gain, so the free market eliminates slavery. Globalisation creates networks of interdependence that make wars uneconomic. Increased contacts between different cultures makes people more tolerant. And eventually, the humanity may be able to unite facing a common "external" enemy — the climate change.
What can I say? The examples are quite interesting, the whole theory seems self-consistent. Still, I repeatedly looked at the publication date (it's 1999), and wondered if author would write the same thing today (yes, I know I can search for his current opinions).
#books #bookstodon @…

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: @…

Burnout leave, day negative 13:
‣ Half of me wants to rip myself to shreds with self-criticism for things I could have done better
‣ Half of me resents that, through my life so far (independent of recent events), the world hasn't been the best place for me to grow and be the best person I can be
‣ Half of me wants to accept both of the above and find the most realistic (and inevitably imperfect) path forward (ACT therapy style)
‣ Half of me is grumbling that these are…

Slightly manic looking selfie you say? Bloody right mate - I donated plasma for the first time today at Launceston and my arm is sore!
It takes a bit of time to sort out optimal arm positions for the needle as it's a 45 minute thang, the awesome staff help make the time fly..
Next plasma donation will be in two weeks because #BloodIsLife

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.
1/2

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.
@… @…

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.