Tootfinder

There, I fixed the study title:
“[White Guy] Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights”
https://arxiv.org/abs/2509.00462
PDF:
https://…

AI Self-preferencing in Algorithmic Hiring: Empirical Evidence and Insights
As artificial intelligence (AI) tools become widely adopted, large language models (LLMs) are increasingly involved on both sides of decision-making processes, ranging from hiring to content moderation. This dual adoption raises a critical question: do LLMs systematically favor content that resembles their own outputs? Prior research in computer science has identified self-preference bias -- the tendency of LLMs to favor their own generated content -- but its real-world implications have not be…

Some thoughts from Claude about military use of AI in targeting.
https://www.instagram.com/reel/DYIHIM6ihVJ/?igsh=cmM1ZmdjaDl3bzJ5

Future of Privacy Forum Releases Comprehensive Report On Algorithmic Personalization in Youth Online Experiences
https://fpf.org/press-releases/future-of-privacy-forum-releases-comprehensive-repor…

from my link log —
The design and implementation of the Berkeley Internet Name Domain (BIND) servers.
https://www2.eecs.berkeley.edu/Pubs/TechRpts/1984/5962.html
saved 2020-08-27

Back when I was diagnosed with #diabetes, I've found two apps to help me. Both were proprietary.
The first one came from the glucometer's manufacturer, and it featured the ability to copy its readings over Bluetooth. It also had a pretty useful bolus calculator. However, it was also annoying in a number of ways: it required an account with all the implied data sharing, was premiumware (though using their glucometer implied a free "pro" version), was quite childish in design, didn't respect disabled animations or dark theme.
The second one was an independent Polish app to compute carbs from meals. It wasn't perfect, but it had a rich database of products and quite a few convenient features (like copying meals or calculating carbs based on recipes). Unfortunately, the authors went full way into AI hype, and while I didn't use any of the "AI" features (they were premiumware anyway), the app itself was becoming increasingly crappy.
Eventually, this motivated me to look for a new app. I've settled for #Diaguard. It's just got the basics: meal composition and computing carbs, plus letting me log blood sugar and insulin doses manually. It used to have a bolus calculator before I used it, but it was removed over legal concerns. Still, it's open source, it's nice, it's got no crap and it respects the dark theme. And honestly, given that I've already reached the point of overriding bolus calculator, I've figured out it's enough for me.
In fact, it's so "enough" that I'm not even using it regularly. I mean, if my blood sugar is predictable and my meals are predictable, there's no reason to waste time logging them. The app is there to assist me when I need it, not force me to use it.
I still keep the two other apps installed. The first one in case I had doubts over insulin doses and wanted to use the bolus calculator. The second one over my database of meals; though I had already exported it, so I just need to figure out if the export is complete enough.
#Android

from my link log —
MLIR: a compiler infrastructure for the end of Moore's Law.
https://arxiv.org/abs/2002.11054
saved 2020-02-27 https://dotat.at/:…

MLIR: A Compiler Infrastructure for the End of Moore's Law
This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together. MLIR facilitates the design and implementation of code generators, translators and optimizers at different levels of abstraction and also across application domains, hardware targets …

Partitioned Iterative Quantum Scheduling of Satellites for Urgent Disaster Response: Case study of Wildfire
Lucas T. Braydwood, Taejin Park, Hirofumi Hashimoto, Zoe Gonzalez Izquierdo, Andrew Michaelis, Eleanor Rieffel, Shon Grabbe
https://arxiv.org/abs/2606.12310 https://arxiv.org/pdf/2606.12310 https://arxiv.org/html/2606.12310
arXiv:2606.12310v1 Announce Type: new
Abstract: The standard in Earth-observation tasks today is having near real-time access to surface images in response to changing conditions. For instance, as urban environments interface more with wildlands and wildfires become less predictable, their tracking with satellite resources becomes essential. This requires the coordination of increasingly large constellations of satellites, giving rise to challenging computational problems. With wildfire detection and tracking as a backdrop, we investigate the power of special purpose and novel computing paradigms to tackle the ensuing satellite scheduling problems, making a compelling case for quantum algorithms. We bring quantum scheduling algorithms closer to implementation by examining both the emerging iterative quantum algorithm framework, which comes with analytic guarantees compared to some classical algorithms, and distributed quantum computing methods whose relevance is on the rise as utility-scale problems begin to get solved with quantum computers. Drawing strength from several computing fronts, we develop a distributed/parallelization scheme in conjunction with the quantum algorithm design and apply these techniques to real-world datasets for wildfire detection. While our quantum subprocesses are currently too small to see significant quantum advantage, our results validate the utility of these techniques, and continue forging the path toward distributed quantum computing.
toXiv_bot_toot

Bad for health and the environment: Lung experts highlight environmental impact of tobacco product waste https://phys.org/news/2026-06-bad-health-environment-lung-experts.html

Bad for health and the environment: Lung experts highlight environmental impact of tobacco product waste
Ahead of World No Tobacco Day May 31, the American Thoracic Society and our Forum of International Respiratory Societies (FIRS) partners reiterate the need for countries to urgently implement decisions made at the 11th Conference of the Parties (COP11) to the WHO Framework Convention on Tobacco Control.

Agent libOS: A Library-OS-Inspired Runtime for Long-Running, Capability-Controlled LLM Agents
Yingqi Zhang
https://arxiv.org/abs/2606.03895 https://arxiv.org/pdf/2606.03895 https://arxiv.org/html/2606.03895
arXiv:2606.03895v1 Announce Type: new
Abstract: Large language model (LLM) agents are evolving from request-response assistants into long-running software actors: they maintain state across model calls, fork subtasks, wait for external events, request human authority, generate tools, and perform side effects that must be resumed and audited. This paper presents Agent libOS, a library-OS-inspired runtime substrate for LLM agents. Agent libOS runs above a conventional host operating system; it does not implement hardware drivers, kernel-mode isolation, or a POSIX-compatible operating system. Instead, it treats an agent as an AgentProcess: a schedulable execution subject with process identity, parent-child lineage, lifecycle state, a tool table derived from an AgentImage, typed Object Memory, explicit capabilities, human queues, checkpoints, events, and audit records. Its central design rule is tools are libc-like wrappers; runtime primitives are the authority boundary. Filesystem access, object access, sleeps, human approval, JIT tool registration, and external side effects are checked at primitive boundaries under explicit capabilities and policy.
We describe the design, threat model, Python prototype, and safety-oriented evaluation. The current prototype implements async scheduling, namespace-local Object Memory, runtime-integrated human approval, one-shot permission grants, per-process working directories, shell and image-registration primitives, Deno/TypeScript JIT tools over a libOS syscall broker, filesystem/object bridge tools, an injectable Resource Provider Substrate, deterministic demos, real-model smoke scripts, and 123 regression tests at the time of writing. Rather than improving planner accuracy, Agent libOS demonstrates a runtime substrate in which long-running LLM agents can be scheduled, authorized, resumed, and audited without treating tool dispatch as the trust boundary.
toXiv_bot_toot

Apropos of this second-latest comment on this fascinating thread, if your argument boils down to stuff that was removed from a draft spec three years ago, then your argument might be moot:
https://github.com/w3c/wcag3/issues/642#issuecomment-4360244086
Because…