Tootfinder

MastitisApp: a software for preventive diagnosis of mastitis in dairy cows
Italo Henrique Souza Mafra, Glauber da Rocha Balthazar
https://arxiv.org/abs/2508.14124 https://

MastitisApp: a software for preventive diagnosis of mastitis in dairy cows
Dairy farming has great economic value in Brazil, however, during production, diseases such as mastitis can occur in animals, which can reduce productivity and, consequently, economic profitability. When mastitis is present in animals, it can cause physical and chemical changes in the milk, affecting its quality, market value and also compromising the health of the animal. MastiteApp is a tool to help producers prevent mastitis in their herds by checking the temperature taken from the four teat…

Toward Sustainable Subterranean mMTC: Space-Air-Ground-Underground Networks Powered by LoRaWAN and Wireless Energy Transfer
Kaiqiang Lin, Mohamed-Slim Alouini
https://arxiv.org/abs/2508.15058

Toward Sustainable Subterranean mMTC: Space-Air-Ground-Underground Networks Powered by LoRaWAN and Wireless Energy Transfer
Wireless underground sensor networks (WUSNs), which enable real-time sensing and monitoring of underground resources by underground devices (UDs), hold great promise for delivering substantial social and economic benefits across various verticals. However, due to the harsh subterranean environment, scarce network resources, and restricted communication coverage, WUSNs face significant challenges in supporting sustainable massive machine-type communications (mMTC), particularly in remote, disast…

Evaluating Classical Software Process Models as Coordination Mechanisms for LLM-Based Software Generation
Duc Minh Ha, Phu Trac Kien, Tho Quan, Anh Nguyen-Duc
https://arxiv.org/abs/2509.13942

Evaluating Classical Software Process Models as Coordination Mechanisms for LLM-Based Software Generation
[Background] Large Language Model (LLM)-based multi-agent systems (MAS) are transforming software development by enabling autonomous collaboration. Classical software processes such asWaterfall, V-Model, and Agile offer structured coordination patterns that can be repurposed to guide these agent interactions. [Aims] This study explores how traditional software development processes can be adapted as coordination scaffolds for LLM based MAS and examines their impact on code quality, cost, and pr…

Korea seems to be a new epicenter for cyberattacks, with a bunch of luxury brands, a giant telco, and now a major insurance company hit with ransomware attacks. If it were an English-speaking company, it would all seem very Scattered Spider-esque.
https://www.koreatimes.co.kr/e…

SGI scrambles to recover from ransomware attack
Seoul Guarantee Insurance (SGI) plans to operate an around-the-clock response center and compensate all losses from a recent ransomware attack, the company said Wednesday. The measures are the latest...

Residual Test for the Third Gravitational-Wave Transient Catalog
Dicong Liang, Ning Dai, Yingjie Yang
https://arxiv.org/abs/2509.14924 https://arxiv.org/pd…

Residual Test for the Third Gravitational-Wave Transient Catalog
The residual test is commonly used to check the agreement between the gravitational wave signal and the theoretical waveform template. The basic idea of the residual test is to subtract the best-fit waveform from the data and then check whether the remaining data (i.e., the residuals) are consistent with the instrumental noise or not. We apply the Kolmogorov-Smirnov test, the Anderson-Darling test and the chi-squared test as goodness-of-fit test to examine the residuals of events in the third g…

As for “but it's great for coding!“…
…world-wide there's about 3.6 billion jobs or so, of which ~25 million are in software development; this means maybe about 0.7% of all jobs world-wide can use "great for coding".
Writing actual code amounts to maybe, if you're lucky, 10% of the work a software developer does.
The rest is meetings, high-level specifications, email and chat, more meetings, learning new things, updating stuff, lots of testing and debugging, etc.
The gist is, the supposed gains from "AI" are completely irrelevant (and indeed there's signs and studies that show it doesn't do anything for programmer productivity either).
tl;dr: This is the worst economic bubble in history, pushing a dream of a magical technology that unfortunately doesn't work, by appealing to investor greed.

Just read this post by @… on an optimistic AGI future, and while it had some interesting and worthwhile ideas, it's also in my opinion dangerously misguided, and plays into the current AGI hype in a harmful way.
https://social.coop/@eloquence/114940607434005478
My criticisms include:
- Current LLM technology has many layers, but the biggest most capable models are all tied to corporate datacenters and require inordinate amounts of every and water use to run. Trying to use these tools to bring about a post-scarcity economy will burn up the planet. We urgently need more-capable but also vastly more efficient AI technologies if we want to use AI for a post-scarcity economy, and we are *not* nearly on the verge of this despite what the big companies pushing LLMs want us to think.
- I can see that permacommons.org claims a small level of expenses on AI equates to low climate impact. However, given current deep subsidies on place by the big companies to attract users, that isn't a great assumption. The fact that their FAQ dodges the question about which AI systems they use isn't a great look.
- These systems are not free in the same way that Wikipedia or open-source software is. To run your own model you need a data harvesting & cleaning operation that costs millions of dollars minimum, and then you need millions of dollars worth of storage & compute to train & host the models. Right now, big corporations are trying to compete for market share by heavily subsidizing these things, but it you go along with that, you become dependent on them, and you'll be screwed when they jack up the price to a profitable level later. I'd love to see open dataset initiatives SBD the like, and there are some of these things, but not enough yet, and many of the initiatives focus on one problem while ignoring others (fine for research but not the basis for a society yet).
- Between the environmental impacts, the horrible labor conditions and undercompensation of data workers who filter the big datasets, and the impacts of both AI scrapers and AI commons pollution, the developers of the most popular & effective LLMs have a lot of answer for. This project only really mentions environmental impacts, which makes me think that they're not serious about ethics, which in turn makes me distrustful of the whole enterprise.
- Their language also ends up encouraging AI use broadly while totally ignoring several entire classes of harm, so they're effectively contributing to AI hype, especially with such casual talk of AGI and robotics as if embodied AGI were just around the corner. To be clear about this point: we are several breakthroughs away from AGI under the most optimistic assumptions, and giving the impression that those will happen soon plays directly into the hands of the Sam Altmans of the world who are trying to make money off the impression of impending huge advances in AI capabilities. Adding to the AI hype is irresponsible.
- I've got a more philosophical criticism that I'll post about separately.
I do think that the idea of using AI & other software tools, possibly along with robotics and funded by many local cooperatives, in order to make businesses obsolete before they can do the same to all workers, is a good one. Get your local library to buy a knitting machine alongside their 3D printer.
Lately I've felt too busy criticizing AI to really sit down and think about what I do want the future to look like, even though I'm a big proponent of positive visions for the future as a force multiplier for criticism, and this article is inspiring to me in that regard, even if the specific project doesn't seem like a good one.

A statistician's guide to weak-instrument-robust inference in instrumental variables regression with illustrations in Python
Malte Londschien
https://arxiv.org/abs/2508.12474

A statistician's guide to weak-instrument-robust inference in instrumental variables regression with illustrations in Python
We provide an overview of results relating to estimation and weak-instrument-robust inference in instrumental variables regression. Methods are implemented in the ivmodels software package for Python, which we use to illustrate results.

Crash Report Enhancement with Large Language Models: An Empirical Study
S M Farah Al Fahim (Peter), Md Nakhla Rafi (Peter), Zeyang Ma (Peter), Dong Jae Kim (Peter), Tse-Hsun (Peter), Chen
https://arxiv.org/abs/2509.13535

Crash Report Enhancement with Large Language Models: An Empirical Study
Crash reports are central to software maintenance, yet many lack the diagnostic detail developers need to debug efficiently. We examine whether large language models can enhance crash reports by adding fault locations, root-cause explanations, and repair suggestions. We study two enhancement strategies: Direct-LLM, a single-shot approach that uses stack-trace context, and Agentic-LLM, an iterative approach that explores the repository for additional evidence. On a dataset of 492 real-world cras…

ChangePrism: Visualizing the Essence of Code Changes
Lei Chen, Michele Lanza, Shinpei Hayashi
https://arxiv.org/abs/2508.12649 https://arxiv.org/pdf/2508.1…

ChangePrism: Visualizing the Essence of Code Changes
Understanding the changes made by developers when they submit a pull request and/or perform a commit on a repository is a crucial activity in software maintenance and evolution. The common way to review changes relies on examining code diffs, where textual differences between two file versions are highlighted in red and green to indicate additions and deletions of lines. This can be cumbersome for developers, making it difficult to obtain a comprehensive overview of all changes in a commit. Mor…