Tootfinder

Retracting their financial commitment to original content, streamers simply aren't spending money on series like this any more.
▶️ Alan Tudyk on why ANDOR was such a special show & why it will be so hard to replicate
https://youtube.com/watch?v=999tjKh1y5g&s…

Having a studio near a piano teacher's studio makes me realise how most kids don't practice this instrument at all, just come and struggle through the same boring tunes every week.
Please don't make your children learn an instrument they don't actual enjoy playing, it's a counter-productive waste of time.
All that stuff about classical music making kids more intelligent is pure nonsense, either experimental results don't replicate or replicate just as we…

""[…] one message came through clearly: as we move forward, we should take the opportunity to improve the [#Fedora packager workflow] model rather than simply replicate it. The conversation focused on three key areas: package ownership, artifact storage, and Packit integration.
[…]
Key takeaways include:
* A Shift to Merge Requests: There is strong momentum to make …

A Rogue New Life Form
A tiny microbe discovered by accident challenges the definition of cellular life.
Very small amount of genetic material.
"...between archaea and virus...unlike a virus, Sukunaarchaeum has its own ribosomes, cellular structures that synthesize proteins, and it can replicate itself without the help of a host."

A Rogue New Life Form
A tiny microbe discovered by accident challenges the definition of cellular life

LLM coding is the opposite of DRY
An important principle in software engineering is DRY: Don't Repeat Yourself. We recognize that having the same code copied in more than one place is bad for several reasons:
1. It makes the entire codebase harder to read.
2. It increases maintenance burden, since any problems in the duplicated code need to be solved in more than one place.
3. Because it becomes possible for the copies to drift apart if changes to one aren't transferred to the other (maybe the person making the change has forgotten there was a copy) it makes the code more error-prone and harder to debug.
All modern programming languages make it almost entirely unnecessary to repeat code: we can move the repeated code into a "function" or "module" and then reference it from all the different places it's needed. At a larger scale, someone might write an open-source "library" of such functions or modules and instead of re-implementing that functionality ourselves, we can use their code, with an acknowledgement. Using another person's library this way is complicated, because now you're dependent on them: if they stop maintaining it or introduce bugs, you've inherited a problem, but still, you could always copy their project and maintain your own version, and it would be not much more work than if you had implemented stuff yourself from the start. It's a little more complicated than this, but the basic principle holds, and it's a foundational one for software development in general and the open-source movement in particular. The network of "citations" as open-source software builds on other open-source software and people contribute patches to each others' projects is a lot of what makes the movement into a community, and it can lead to collaborations that drive further development. So the DRY principle is important at both small and large scales.
Unfortunately, the current crop of hyped-up LLM coding systems from the big players are antithetical to DRY at all scales:
- At the library scale, they train on open source software but then (with some unknown frequency) replicate parts of it line-for-line *without* any citation [1]. The person who was using the LLM has no way of knowing that this happened, or even any way to check for it. In theory the LLM company could build a system for this, but it's not likely to be profitable unless the courts actually start punishing these license violations, which doesn't seem likely based on results so far and the difficulty of finding out that the violations are happening. By creating these copies (and also mash-ups, along with lots of less-problematic stuff), the LLM users (enabled and encouraged by the LLM-peddlers) are directly undermining the DRY principle. If we see what the big AI companies claim to want, which is a massive shift towards machine-authored code, DRY at the library scale will effectively be dead, with each new project simply re-implementing the functionality it needs instead of every using a library. This might seem to have some upside, since dependency hell is a thing, but the downside in terms of comprehensibility and therefore maintainability, correctness, and security will be massive. The eventual lack of new high-quality DRY-respecting code to train the models on will only make this problem worse.
- At the module & function level, AI is probably prone to re-writing rather than re-using the functions or needs, especially with a workflow where a human prompts it for many independent completions. This part I don't have direct evidence for, since I don't use LLM coding models myself except in very specific circumstances because it's not generally ethical to do so. I do know that when it tries to call existing functions, it often guesses incorrectly about the parameters they need, which I'm sure is a headache and source of bugs for the vibe coders out there. An AI could be designed to take more context into account and use existing lookup tools to get accurate function signatures and use them when generating function calls, but even though that would probably significantly improve output quality, I suspect it's the kind of thing that would be seen as too-baroque and thus not a priority. Would love to hear I'm wrong about any of this, but I suspect the consequences are that any medium-or-larger sized codebase written with LLM tools will have significant bloat from duplicate functionality, and will have places where better use of existing libraries would have made the code simpler. At a fundamental level, a principle like DRY is not something that current LLM training techniques are able to learn, and while they can imitate it from their training sets to some degree when asked for large amounts of code, when prompted for many smaller chunks, they're asymptotically likely to violate it.
I think this is an important critique in part because it cuts against the argument that "LLMs are the modern compliers, if you reject them you're just like the people who wanted to keep hand-writing assembly code, and you'll be just as obsolete." Compilers actually represented a great win for abstraction, encapsulation, and DRY in general, and they supported and are integral to open source development, whereas LLMs are set to do the opposite.
[1] to see what this looks like in action in prose, see the example on page 30 of the NYTimes copyright complaint against OpenAI (#AI #GenAI #LLMs #VibeCoding

Building high-energy silicon-containing batteries using off-the-shelf materials
Marco-Tulio F. Rodrigues, Stephen E. Trask, Alison R. Dunlop, Yi-Chen Lan, Joseph Kubal, Devashish Salpekar, Andressa Y. R. Prado, Evelyna Wang, Charles McDaniel, Eliot F. Woods, Lily A. Robertson, Ryan J. Tancin, Maxwell C. Schulze, Nicolas Folastre, Baris Key, Zhengcheng Zhang, Wenquan Lu, Daniel P. Abraham, Andrew N. Jansen

Building high-energy silicon-containing batteries using off-the-shelf materials
The technology of silicon anodes appears to be reaching maturity, with high-energy Si cells already in pilot-scale production. However, the performance of these systems can be difficult to replicate in academic settings, making it challenging to translate research findings into solutions that can be implemented by the battery industry. Part of this difficulty arises from the lack of access to engineered Si particles and anodes, as electrode formulations and the materials themselves have become …

Might I suggest that the D-party form a shadow government with shadow agencies to replicate at least the policy making aspects of the gov't that FFOTUS is tearing down.
For example, begin with a medical advisory panel to give us trustworthy policy regarding vaccinations and drugs.
Etc.
It need not be large or expensive, just cherry pick, at least at the start, the most critical places to give the public information that it most needs.

Cowboys' $80M man recognized with NFLPA award for giving back to community https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/09/12/cowboys-osa-odighizuwa-nflpa-community-award/86116925007/

NoMachine is a solution for Linux that allows one to take over an existing desktop session while preventing the computer from being unlocked locally at the same time. In fact, it unlocks the computer, but blanks the screen and disables input. I think UltraVNC had similar options over 15 years ago, but only for Windows.
In principle, it should be possible to replicate this behaviour for any remote desktop tool (?). 🤔
Re:

Breaking the mould of Social Mixed Reality -- State-of-the-Art and Glossary
Marta Bie\'nkiewicz, Julia Ayache, Panayiotis Charalambous, Cristina Becchio, Marco Corragio, Bertram Taetz, Francesco De Lellis, Antonio Grotta, Anna Server, Daniel Rammer, Richard Kulpa, Franck Multon, Azucena Garcia-Palacios, Jessica Sutherland, Kathleen Bryson, St\'ephane Donikian, Didier Stricker, Beno\^it Bardy

Breaking the mould of Social Mixed Reality -- State-of-the-Art and Glossary
This article explores a critical gap in Mixed Reality (MR) technology: while advances have been made, MR still struggles to authentically replicate human embodiment and socio-motor interaction. For MR to enable truly meaningful social experiences, it needs to incorporate multi-modal data streams and multi-agent interaction capabilities. To address this challenge, we present a comprehensive glossary covering key topics such as Virtual Characters and Autonomisation, Responsible AI, Ethics by Desi…

Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generation
Lydia Kin Ching Chau, Zhi Yu, Ruo Wei Jiang
https://arxiv.org/abs/2509.02445 ht…

Towards High-Fidelity, Identity-Preserving Real-Time Makeup Transfer: Decoupling Style Generation
We present a novel framework for real-time virtual makeup try-on that achieves high-fidelity, identity-preserving cosmetic transfer with robust temporal consistency. In live makeup transfer applications, it is critical to synthesize temporally coherent results that accurately replicate fine-grained makeup and preserve user's identity. However, existing methods often struggle to disentangle semitransparent cosmetics from skin tones and other identify features, causing identity shifts and raising…

How Well Do LLMs Imitate Human Writing Style?
Rebira Jemama, Rajesh Kumar
https://arxiv.org/abs/2509.24930 https://arxiv.org/pdf/2509.24930

How Well Do LLMs Imitate Human Writing Style?
Large language models (LLMs) can generate fluent text, but their ability to replicate the distinctive style of a specific human author remains unclear. We present a fast, training-free framework for authorship verification and style imitation analysis. The method integrates TF-IDF character n-grams with transformer embeddings and classifies text pairs through empirical distance distributions, eliminating the need for supervised training or threshold tuning. It achieves 97.5\% accuracy on academ…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
Kazuumi Fujioka, Rui Sun
https://arxiv.org/abs/2510.05398 https://arxiv.o…

GMTHRASHpy: Forward Convolutions of Crossed Molecular Beams Experiments in Python
We introduce GMTHRASHpy, a Python-based application to do forward convolution fits of crossed molecular beams experiments. The code is designed to be easy-to-use and widely-available, so as to be of value to anyone wanting to reproduce data or fits from these experiments. GMTHRASHpy has been benchmarked to replicate the original GMTHRASH executable for a wide variety of published reactions. The inputs and algorithm guiding it are explained. The code is open-source and can be downloaded at: gith…

Transforming Fashion with AI: A Comparative Study of Large Language Models in Apparel Design
Nusrat Jahan Lamia, Sadia Afrin Mim
https://arxiv.org/abs/2509.04705 https://…

Transforming Fashion with AI: A Comparative Study of Large Language Models in Apparel Design
Fashion has evolved from handcrafted designs to automated production over the years, where AI has added another dimension to it. Nowadays, practically every industry uses artificial models to automate their operations. To explore their role, we examined three prominent LLMs (OpenAI, GeminiAI, Deepseek) in multiple stages of textile manufacturing (e.g., sustainable choice, cost effectiveness, production planning, etc.). We assessed the models' ability to replicate garment design using certain pa…