Tootfinder

Google unveils the $899 Pixel 11, $1,099 11 Pro, and $1,299 11 Pro XL, with Tensor G6, new Gemini features, Magic Capture to pick the best frames, and more (Ivan Mehta/TechCrunch)
https://techcrunch.com/2026/08/12/pixel-11-has-few-hardware-changes-and-…

Google's Pixel 11 lineup offer fewer hardware changes, but much more Gemini | TechCrunch
Google Pixel 11 Series starts $100 costlier than the last year, but offers 256GB base storage

✅ Weekly errands done (our trailer was quite packed this time)
✅ Physical exercise after an office day
✅ Avoided afterwork traffic jams
✅ Fresh air
✅ Saved fuel / car cost
✅ Got some Interested looks from ppl around 😀
If you wonder if buying such a trailer pays off. I would say: absolutely!
Especially the exercise is priceless!
#cycling

Ooh, that's getting there.... This is a BBC B captured with the RGB wired directly to a PICO GPIO and advertising USB video; now hmm, wtf is it backwards. (It's yellow because I ahem need to put the blue pin in the right place). I've not properly done sync yet and it's not bad!

#viernesdeescritorio En @… con Libreoffice, el navegador Lagrange y varias aplicaciones minimizadas, entre ellas Firefox

Fine-Tuning Large Language Models for Codebook-Guided Coding of Students' Mathematics Metaphor Responses
Liang Zhang, Stephen Hwang, Yue Ma, Jinfa Cai
https://arxiv.org/abs/2608.10276 https://arxiv.org/pdf/2608.10276 https://arxiv.org/html/2608.10276
arXiv:2608.10276v1 Announce Type: new
Abstract: Student-generated metaphors about mathematics can reveal students' attitudes, beliefs, identities, and experiences, but human expert coding of these thematically and semantically complex open-ended responses is time-intensive and difficult to scale. This study examines whether LoRA-based supervised fine-tuning of large language models (LLMs) can improve their performance on codebook-guided coding tasks for student mathematics metaphors. We used a human-coded corpus of 2,265 Grade 6-8 responses to food- and animal-based metaphor prompts and instructed the LLMs to perform two coding tasks: valence-intensity coding to capture the direction and strength of students' affective orientations toward mathematics, and thematic coding to capture students' framings of mathematics as expressed through their metaphors. We compared two proprietary models, GPT-4o mini and GPT-5 mini, under prompt-only conditions with two open-weight models, DeepSeek-R1 1.5B and Mistral 7B, evaluated before and after fine-tuning. Results show that fine-tuning substantially improved the performance and run-to-run reliability of the open-weight models across both tasks relative to their base versions. The fine-tuned compact open-weight models became competitive with, and often outperformed, the proprietary prompt-only models. These findings suggest that compact open-weight LLMs can support scalable, locally controllable, and privacy-conscious AI-assisted measurement of students' metaphor responses in mathematics education.
toXiv_bot_toot

This #Dreamcast project looks super cool https://bsky.app/profile/eliasdaler.bsky.social/post/3mu6awwwmoc26

I am reminded of the time AudioEye filed a SLAPP against me and its attorneys used untagged PDFs to do so.
IMO [added the ‘IMO’ because AudioEye are litigious bastards and might claim I’m implying a legal ruling happened and that I’m more of a filthy liar than they are], an indictment of AudioEye’s accessibility capability and both its and its attorney’s ethics.
Anyway, useful nuggets are littered throughout the book, in case you don’t have it:

Countable Functionally Balanced Groups
Li-Hong Xie, Jiang Yang
https://arxiv.org/abs/2608.12494 https://arxiv.org/pdf/2608.12494 https://arxiv.org/html/2608.12494
arXiv:2608.12494v1 Announce Type: new
Abstract: We prove that every countable Hausdorff functionally balanced topological group is balanced, answering Question~13 in the problem list of Bouziad and Troallic. The proof does not assume metrizability, first countability, sequentiality, or a non-Archimedean local base. Its non-locally-precompact part combines an infinite-fibre amplification, a simultaneous Bernoulli colouring lemma for countably many arbitrary self-maps, a McShane extension with respect to a continuous left-invariant pseudometric, and the Protasov--Saryev criterion. We also prove a countable-union form of Question~4: in an \(\FSIN\) group, every left uniformly discrete family whose union is countable is right uniformly discrete. Consequently, Questions~7 and~8 have positive answers for \(\aleph_0\)-bounded \(\FSIN\) groups. We reconstruct all thirteen questions and their logical relations. The original problem chapter states, without proof, that a positive answer to Question~13 would imply Question~4 for all \(\aleph_0\)-bounded groups. We isolate the ambient reflection principle needed for that transfer and explain why neither subgroup reduction nor the present colouring proof establishes it. Therefore Question~12 is \emph{not} claimed as solved here: the precise boundary between the proved countable theorem and the asserted \(\aleph_0\)-bounded transfer is recorded explicitly.
toXiv_bot_toot

AI-Generated Interactive Fiction for Educational Use: A Pilot Study of Perceived Comprehensibility, Coherence, and Engagement
Finn Rogosch, Andreas Schrader
https://arxiv.org/abs/2608.10818 https://arxiv.org/pdf/2608.10818 https://arxiv.org/html/2608.10818
arXiv:2608.10818v1 Announce Type: new
Abstract: Generative artificial intelligence (AI) can produce educational content at scale, including interactive and narrative learning experiences, but technical generation alone is not sufficient: scenarios that are confusing, narratively inconsistent, or unengaging are unlikely to be useful in practice. This paper presents a pilot user-centred evaluation of AI-generated interactive fiction (IF) for educational use in higher education. Using a previously described domain-agnostic pipeline and a shared STEM content base, we generated a controlled pool of scenarios and asked participants (N = 22, STEM higher-education) to play one generated episode and rate it on narrative clarity, story-content coherence, engagement, and length acceptance. A free-text prompt captured open feedback. Narrative clarity and length acceptance were rated positively, engagement sat near the neutral mid-point of the scale, and story-content coherence was the weakest dimension by a clear margin. Qualitative feedback points to quiz integration as the bottleneck. Artificial in-fiction motivation for quiz prompts and abrupt setting changes were reported. Feedback also pointed to missing story-level consequences for wrong answers. From these observations, we derive concrete design implications that can inform larger follow-up studies, including later work on learning effectiveness.
toXiv_bot_toot

#Google rompe #España y cambia la frontera de España con Marruecos en torno a las ciudades autónomas de Ceuta y Melilla. Venga, partriotas, daros de baja de Google.