Tootfinder

PSA about food labeling in the US
We have a gluten detection service dog because many things that should be gluten free/say they’re gluten free are not actually gluten free.
Stuff gets contaminated when growing (e.g. next to wheat field), by shared equipment, in factories, from packaging, during transport and in-store.
Every US consumer should know:
1. The list of ingredients on food isn't exhaustive
2. Allergen labeling:
a) limited to just some allergens
b) manufacturers don't actually have to test
c) "certified" foods are tested—but not continuously
d) testing only works with enough contamination
Some certifications may require batch-testing, but usually they don't.
A "certified gluten free" product may e.g. contain oats which sometimes are contaminated with gluten—but as not every batch is tested it's impossible to know unless you test yourself (hence the service dog).
Even if the product is properly batch-tested, you might get a part of the product that has the allergen in it, whereas the tested part didn't.
Or the threshold was too low (our dog can detect gluten better than any available lab testing equipment; yes, dogs are amazing).
Food products also contain ingredients that do not have to be included on the label when they're "incidental" (included in an another ingredient) or if they're considered part of the manufacturing process but not of the final product (e.g. various coatings on factory equipment).
Don't need to list flavors or specific spices either. ¯\_(ツ)_/¯
As for allergens, only those responsible for ~90% of food allergies* have to be specifically declared, and they're not tested for as it's simply based on the ingredients list.
Good luck if you have other allergies.
*milk, egg, egg, fish, Crustacean shellfish, tree nuts, wheat, peanuts, soybeans

Servo 0.0.3 was released https://github.com/servo/servo/releases/tag/v0.0.3
update and test while it's hot. :p

Release v0.0.3 · servo/servo
v0.0.3 Servo 0.0.3 is a comparatively smaller release, since we synchronized our release cut-off with the monthly November blog post, meaning that 0.0.3 contains only about 2 weeks worth of changes...

🍄 Mushrooms may have been part of early human diets: Primate study explores who eats what, and when
https://phys.org/news/2025-09-mushrooms-early-human-diets-primate.html

Mushrooms may have been part of early human diets: Primate study explores who eats what, and when
Mushrooms may not be the first food that comes to mind when we imagine the diets of wild primates—or our early human ancestors. We tend to think of fruits and green leaves as the preferred foods for monkeys and apes.

So: just watched all the Topaz Video tutorials. Very interesting workflow. There are a whole bunch of different models in there that are trained on specific tasks, so you can try denoise by this or that one, upscaling by a different one altogether.
And you cannot import BRAW directly, so it seems I have to decide if I want my crops/stabilisation/colour grade first, then AI enhancement, since the source footage is 6K. For other clips I may need to upscale first.
My head already hu…

Towards an AI-Augmented Textbook
LearnLM Team, Google, :, Amy Wang, Anna Iurchenko, Anisha Choudhury, Alicia Mart\'in, Amir Globerson, Avinatan Hassidim, Ay\c{c}a \c{C}akmakli, Ayelet Shasha Evron, Charlie Yang, Courtney Heldreth, Diana Akrong, Gal Elidan, Hairong Mu, Ian Li, Ido Cohen, Katherine Chou, Komal Singh, Lev Borovoi, Lidan Hackmon, Lior Belinsky, Michael Fink, Niv Efron, Preeti Singh, Rena Levitt, Shashank Agarwal, Shay Sharon, Tracey Lee-Joe, Xiaohong Hao, Yael Gold-Z…

Towards an AI-Augmented Textbook
Textbooks are a cornerstone of education, but they have a fundamental limitation: they are a one-size-fits-all medium. Any new material or alternative representation requires arduous human effort, so that textbooks cannot be adapted in a scalable manner. We present an approach for transforming and augmenting textbooks using generative AI, adding layers of multiple representations and personalization while maintaining content integrity and quality. We refer to the system built with this approach…

🤔 Do mega-sporting events like the World Series pay off? Here's the economic reality behind them
#sports

Do mega-sporting events like the World Series pay off? Here's the economic reality behind them
Whether it's the World Series, the FIFA World Cup or the Olympic Games, the hope for hosting mega sporting events is that the economy will emerge as the true winner.

KnowledgeTrail: Generative Timeline for Exploration and Sensemaking of Historical Events and Knowledge Formation
Sangho Suh, Rahul Hingorani, Bryan Wang, Tovi Grossman
https://arxiv.org/abs/2510.12113 …

KnowledgeTrail: Generative Timeline for Exploration and Sensemaking of Historical Events and Knowledge Formation
The landscape of interactive systems is shifting toward dynamic, generative experiences that empower users to explore and construct knowledge in real time. Yet, timelines -- a fundamental tool for representing historical and conceptual development -- remain largely static, limiting user agency and curiosity. We introduce the concept of a generative timeline: an AI-powered timeline that adapts to users' evolving questions by expanding or contracting in response to input. We instantiate this conc…

One other thing, while we don't claim that our mixed-effects logit model is the perfect way to account for non-independence between languages, we don't think it's correct, as Xia & Lindell assert, to just claim that our results are "counterintuitive", the fix-eff estimates are "unreliable" and that the high model fits are "unrealistic." Whether a mix model better captures the data-generat. process is ultimately an empirical question, not one to be decided by assertion. Take, for instance, our finding that once random effects for either subregion or language family are included, the estimated effect of L1_population reverses direction—from the negative value reported by Xia & Lindell et al. to a positive one.

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