Tootfinder

Obesity & diet
I wouldn't normally share a positive story about the new diet drugs, because I've seen someone get obsessed with them who was at a perfectly acceptable weight *by majority standards* (surprise: every weight is in fact perfectly acceptable by *objective* standards, because every "weight-associated" health risk is its own danger that should be assessed *in individuals*). I think two almost-contradictory things:
1. In a society shuddering under the burden of metastasized fatmisia, there's a very real danger in promoting the new diet drugs because lots of people who really don't need them will be psychologically bullied into using them and suffer from the cost and/or side effects.
2. For many individuals under the assault of our society's fatmisia, "just ignore it" is not a sufficient response, and also for specific people for whom decreasing their weight can address *specific* health risks/conditions that they *want* to address that way, these drugs can be a useful tool.
I know @… to be a trustworthy & considerate person, so I think it's responsible to share this:
#Fat #Diet #Obesity

We have raspberries growing. When we lived in Seattle we grew kale and picked wild blackberries. All were so much better than anything you get in a grocery store.
I don't eat a lot of fruit these days because its so disappointing to take a bite out of a bland or bitter berry from a plastic carton..
https://m…

The FBI, headed by Trump acolyte Kash Patel, has reassigned the jobs of thousands of agents
and eviscerated parts of the bureau tasked with investigating rightwing extremists that are considered the most dangerous domestic security threat facing the US today.
Those same types, which includes a locus of fascist street-fighting gangs known as active clubs and accelerationist neo-Nazis,
increasingly view Trump as an enemy,
but are freer than ever to organize
– alm…

Panama’s ocean lifeline vanishes for the first time in 40 years #Panama

Panama’s ocean lifeline vanishes for the first time in 40 years
Every year, Panama’s Pacific coast benefits from powerful seasonal winds that drive nutrient-rich waters to the surface, sustaining fisheries and protecting coral reefs. But in 2025, for the first time in at least four decades, this crucial upwelling did not occur. Scientists suspect weakened trade winds linked to climate disruption played a role, leaving cooler waters absent and fisheries under stress.

Public records show C3 AI's Project Sherlock, company's flagship contract to speed up policing in San Mateo County, has struggled with usability issues and more (Thomas Brewster/Forbes)
https://www.forbes.com/sites/thomasbrewste

Billionaire Tom Siebel's C3 AI Was Supposed To Speed Up Policing. It’s Not Going Well.
San Mateo County pumped $12 million into a grand AI surveillance system that was supposed to level up its police work. Three years later, it still hasn’t seen the benefits.

None of this "won't feel the benefit" nonsense for your wombles. They've all turned up starkers other than hats & scarfs. #TOTP

Sub-nanosecond structural dynamics of the martensitic transformation in Ni-Mn-Ga
Yuru Ge, Fabian Ganss, Daniel Schmidt, Daniel Hensel, Mike J. Bruckhoff, Sakshath Sadashivaiah, Bruno Neumann, Mariana Brede, Markus E. Gruner, Peter Gaal, Klara L\"unser, Sebastian F\"ahler
https://arxiv.org/abs/2509.06513

Sub-nanosecond structural dynamics of the martensitic transformation in Ni-Mn-Ga
Martensitic transformations drive a multitude of emerging applications, which range from high stroke actuation and, mechanocaloric refrigeration, to thermoelastic energy harvesting. All these applications benefit from faster transformations, as a high cycle frequency is essential for achieving high power density. However, systematic investigations of the fast dynamics and fundamental speed limits of martensitic transformations are scarce. Especially for ultrashort time transformations, the temp…

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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How popular media gets love wrong
Had some thoughts in response to a post about loneliness on here. As the author emphasized, reassurances from people who got lucky are not terribly comforting to those who didn't, especially when the person who was lucky had structural factors in their favor that made their chances of success much higher than those is their audience. So: these are just my thoughts, and may not have any bearing on your life. I share them because my experience challenged a lot of the things I was taught to believe about love, and I think my current beliefs are both truer and would benefit others seeing companionship.
We're taught in many modern societies from an absurdly young age that love is not something under our control, and that dating should be a process of trying to kindle love with different people until we meet "the one" with whom it takes off. In the slightly-less-fairytale corners of modern popular media, we might fund an admission that it's possible to influence love, feeding & tending the fire in better or worse ways. But it's still modeled as an uncontrollable force of nature, to be occasionally influenced but never tamed. I'll call this the "fire" model of love.
We're also taught (and non-boys are taught more stringently) a second contradictory model of love: that in a relationship, we need to both do things and be things in order to make our partner love us, and that if we don't, our partner's love for us will wither, and (especially if you're not a boy) it will be our fault. I'll call this the "appeal" model of love.
Now obviously both of these cannot be totally true at once, and plenty of popular media centers this contradiction, but there are really very few competing models on offer.
In my experience, however, it's possible to have "pre-meditated" love. In other words, to decide you want to love someone (or at least, try loving them), commit to that idea, and then actually wind up in love with them (and them with you, although obviously this second part is not directly under your control). I'll call this the "engineered" model of love.
Now, I don't think that the "fire" and "appeal" models of love are totally wrong, but I do feel their shortcomings often suggest poor & self-destructive relationship strategies. I do think the "fire" model is a decent model for *infatuation*, which is something a lot of popular media blur into love, and which drives many (but not all) of the feelings we normally associate with love (even as those feelings have other possible drivers too). I definitely experienced strong infatuation early on in my engineered relationship (ugh that sounds terrible but I'll stick with it; I promise no deception was involved). I continue to experience mild infatuation years later that waxes and wanes. It's not a stable foundation for a relationship but it can be a useful component of one (this at least popular media depicts often).
I'll continue these thoughts in a reply, by it might take a bit to get to it.
#relationships

#ContemporaryContradictions #HashTagGames
Rules: include as many contradictions s you'd like. Can be profound or trivial. Each contradiction is stated via exactly 1 or 2 questions, no statements and not more than 2 questions. Try to group yours into a single post, rather than one post per contradiction, so that it's easier to see more voices when scrolling the hash tag.
Why does "race" work according to the "one drop rule" if you have Black ancestors, but according to "blood quantum" if you have Indigenous ancestors? Who benefits from this arrangement?
Why do we think of seeds as merely a reproduction mechanism for trees, instead of thinking of trees as merely a reproduction mechanism for seeds, especially since some plants can spend millennia as seeds but can survive for only part of a year after sprouting? Are metabolic activity or structural complexity really so important?
If Columbus discovered America, did Batu Khan discover Europe? What is an "Age of Discovery?"
Why don't corporations in the US try to lobby the government for a single-payer healthcare system where the government foots the bill for healthcare instead of companies paying to deeply subsidize their employees' healthcare? What benefit do they gain that's worth that cost, which in other countries is paid for via taxes?
Why is the cost of renting (which gets you zero equity) anywhere close to the cost of a mortgage (which eventually gets you ownership)? If the costs are similar but the benefits are so different, why does anyone ever rent?
Why do we obsess over the fruit/vegetable classification of tomatoes, but not corn, okra, cucumbers, zucchini, etc.?