Tootfinder

So I think I'll need to read up on it a bit. I understand that "Passkeys" try to do something similar as SSH pubkeys.
But do you know a good technical explainer of what's going on and how it works?
(Yes, I could search myself but I am looking for recommendations of articles you have read that you found helpful and clear.)
EDIT: https://

Passkeys.io – A Real-World Passkey Demo & Info Page
Try a Realistic Passkey Demo Login and User Profile. Browse the Passkey Directory and Find Websites With Passkey Support. Learn About Device Compatibility and Technical Details.

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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Kinematic patterns of the enriched gas phase in the Local Group {\sc Hestia} simulations
L. Biaus, S. E. Nuza, C. Scannapieco, F. G. Iza, E. Lozano
https://arxiv.org/abs/2508.08455

Kinematic patterns of the enriched gas phase in the Local Group {\sc Hestia} simulations
Observations of intergalactic absorbers in the Local Group suggest the existence of a velocity dipole in the general barycentre--antibarycentre direction which can be interpreted as evidence of a general flow of material towards the group's centre of mass. In this work, we study the kinematics of gas in the Local Group using one of the high-resolution realisations of the {\sc Hestia} simulations with a particular focus on the evidence left by different ionic species. Our simulation includes the…

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
Zhanhong Jiang, Aditya Balu, Soumik Sarkar
https://arxiv.org/abs/2508.08413 https://

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
$L_0$-smoothness, which has been pivotal to advancing decentralized optimization theory, is often fairly restrictive for modern tasks like deep learning. The recent advent of relaxed $(L_0,L_1)$-smoothness condition enables improved convergence rates for gradient methods. Despite centralized advances, its decentralized extension remains unexplored and challenging. In this work, we propose the first general framework for decentralized gradient descent (DGD) under $(L_0,L_1)$-smoothness by introd…

On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification
Faried Abu Zaid, Tim Katzke, Emmanuel M\"uller, Daniel Neider
https://arxiv.org/abs/2510.09452

On Uniformly Scaling Flows: A Density-Aligned Approach to Deep One-Class Classification
Unsupervised anomaly detection is often framed around two widely studied paradigms. Deep one-class classification, exemplified by Deep SVDD, learns compact latent representations of normality, while density estimators realized by normalizing flows directly model the likelihood of nominal data. In this work, we show that uniformly scaling flows (USFs), normalizing flows with a constant Jacobian determinant, precisely connect these approaches. Specifically, we prove how training a USF via maximum…

Three people have changed the way I see the world in ways I can never undo: Karl Marx, Friedrich Nietzsche, and Rudolf Rocker.
Each of them, through their own political and philosophical struggles, stripped away the false comforts I once clung to. They exposed the machinery of power, the illusions of morality, and the myths of authority, leaving me face to face with the bare truth of how the world works.
Coming to that realization, one that cannot be undone, life appears more viv…

Culturally-Aware Conversations: A Framework & Benchmark for LLMs
Shreya Havaldar, Sunny Rai, Young-Min Cho, Lyle Ungar
https://arxiv.org/abs/2510.11563 https://

Culturally-Aware Conversations: A Framework & Benchmark for LLMs
Existing benchmarks that measure cultural adaptation in LLMs are misaligned with the actual challenges these models face when interacting with users from diverse cultural backgrounds. In this work, we introduce the first framework and benchmark designed to evaluate LLMs in realistic, multicultural conversational settings. Grounded in sociocultural theory, our framework formalizes how linguistic style - a key element of cultural communication - is shaped by situational, relational, and cultural …

Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality
Chao Wang, Michael Gienger, Fan Zhang
https://arxiv.org/abs/2508.08999 h…

Generation of Real-time Robotic Emotional Expressions Learning from Human Demonstration in Mixed Reality
Expressive behaviors in robots are critical for effectively conveying their emotional states during interactions with humans. In this work, we present a framework that autonomously generates realistic and diverse robotic emotional expressions based on expert human demonstrations captured in Mixed Reality (MR). Our system enables experts to teleoperate a virtual robot from a first-person perspective, capturing their facial expressions, head movements, and upper-body gestures, and mapping these b…

Two-Photon Induced Coherence without Induced Emission
Dong-Gil Im, Seung-Yeun Yoo, Chung-Hyun Lee, Jongheon Suh, Yoon-Ho Kim
https://arxiv.org/abs/2510.09392 https://

Two-Photon Induced Coherence without Induced Emission
At the heart of recent advances in quantum imaging and spectroscopy with undetected photons is the quantum optical effect known as induced coherence without induced emission. This quantum interference effect has enabled access to challenging wavelength regimes for imaging and spectroscopic applications. However, the potential of undetected photon quantum metrology, particularly the enhanced phase sensitivity offered by Fock or N00N states, has not yet been realized, as prior work focused only o…

Day 16: Mayra Cuevas & Marie Marquardt
Okay so this is cheating, but they're co-authors of multiple books together, and there's no way for me to separate their contributions... I've already got too many authors I'd like to list, so why not?
I read their book "Does My Body Offend You?" and absolutely loved it; it's a celebration of teen activism while also being a deep exploration of feminist issues through practical situations that bring out the complicated side of things, which the authors refuse to reduce back to a simple formulaic answer. It has a supporting cast of appropriately-complex male characters that help in exploring the nuances of issues like the line between female empowerment & male gratification, and it brings race and macho culture into the conversion as well.
CW for sexual harassment & deep discussion of the resultant trauma.
I'll cheat again here to sneak in mention of two male authors whose work resonates with theirs: Mark Oshiro's "Anger is a Gift" has a more pessimistic/complex take on teen activism along with a gay romance (CW for racist cop murder), while Jeremy Whitley's graphic novel "Navigating With You" deals with queer romance & disability, while having a main character pairing that echoes those from "Does My Body Offend You?" in a lot of ways. Another connection (to non-men authors this time) is with "Go With the Flow" by Lily Williams and Karen Schneemann. Their graphic novel about teen activism and periods is a bit more didactic and has a much lighter tone, but it does necessarily have some overlapping themes.
To bring it back to Cuevas & Marquhardt, their writing is great and their ability to discuss such complex topics with such nuance, all wrapped up in a story that feels completely natural, is amazing to me, and makes their book feel like one of the most valuable to recommend to others.
In writing this I've realized a grave oversight in the list so far that I'll have to correct tomorrow, but I'm quickly running out of days. The didn't-quite-make-it list is going to be full of more excellent authors, and I'm honestly starting to wonder whether it might actually be harder to name 20 male authors I respect now that I've found the sense to be mostly somewhere between disgusted and disappointed with so many of the male authors I enjoyed as a teen.
#20AuthorsNoMen (cheating a bit)