Tootfinder

YouTube is like heroin because it pulls you in, hooks you, and slowly drains you. It is designed to be addictive and destructive, and that is exactly why it feels so hard to step away from it.
And yes, I know the excuse, maybe your favorite dealer has not set up shop on PeerTube. But that does not change the truth of the comparison.
PeerTube is like weed, it grows more naturally, it is gentler on you, and it still gives you a high without tearing apart your health and your lif…

SPIDER: Scalable Probabilistic Inference for Differential Earthquake Relocation
Zachary E. Ross, John D. Wilding, Kamyar Azizzadenesheli, Aitaro Kato
https://arxiv.org/abs/2508.12117

SPIDER: Scalable Probabilistic Inference for Differential Earthquake Relocation
Seismicity catalogs are larger than ever due to an explosion of techniques for enhanced earthquake detection and an abundance of high-quality datasets. Bayesian inference is a popular framework for locating earthquakes due to its ability to propagate and quantify uncertainty into the inversion results, but traditional methods do not scale well to high-dimensional parameter spaces, making them unsuitable for double-difference relocation where the number of parameters can reach the millions. Here…

EVOSCAT: Exploring Software Change Dynamics in Large-Scale Historical Datasets
Souhaila Serbout, Diana Carolina Mu\~noz Hurtado, Hassan Atwi, Edoardo Riggio, Cesare Pautasso
https://arxiv.org/abs/2508.10852

EVOSCAT: Exploring Software Change Dynamics in Large-Scale Historical Datasets
Long lived software projects encompass a large number of artifacts, which undergo many revisions throughout their history. Empirical software engineering researchers studying software evolution gather and collect datasets with millions of events, representing changes introduced to specific artifacts. In this paper, we propose EvoScat, a tool that attempts addressing temporal scalability through the usage of interactive density scatterplot to provide a global overview of large historical dataset…

🕷️ Spiderweb silks and architectures reveal millions of years of evolutionary ingenuity
https://theconversation.com/spiderweb-silks-and-architectures-reveal-millions-of-years-of-evolutionary-ingenuity-261928…

Spiderweb silks and architectures reveal millions of years of evolutionary ingenuity
7 distinct types of silk allow different spider species to build webs with various jobs – including to catch prey, tether themselves and protect their eggs.

Integrated subtraction terms and finite remainders for arbitrary processes with massless partons at colliders in the nested soft-collinear subtraction scheme
Federica Devoto, Kirill Melnikov, Raoul R\"ontsch, Chiara Signorile-Signorile, Davide Maria Tagliabue, Matteo Tresoldi
https://arxiv.org/abs/2509.08594

Integrated subtraction terms and finite remainders for arbitrary processes with massless partons at colliders in the nested soft-collinear subtraction scheme
We present integrated subtraction terms and finite remainders for arbitrary processes with massless partons at hadron and lepton colliders in the context of the nested soft-collinear subtraction scheme. These results provide the very last ingredients needed to make this scheme a fully local, analytic and process-independent framework for treating infrared singularities at next-to-next-to-leading order in perturbative QCD. The explicit infrared finiteness of all required contributions, as well a…

SpiderNets: Estimating Fear Ratings of Spider-Related Images with Vision Models
Dominik Pegler, David Steyrl, Mengfan Zhang, Alexander Karner, Jozsef Arato, Frank Scharnowski, Filip Melinscak
https://arxiv.org/abs/2509.04889

SpiderNets: Estimating Fear Ratings of Spider-Related Images with Vision Models
Advances in computer vision have opened new avenues for clinical applications, particularly in computerized exposure therapy where visual stimuli can be dynamically adjusted based on patient responses. As a critical step toward such adaptive systems, we investigated whether pretrained computer vision models can accurately predict fear levels from spider-related images. We adapted three diverse models using transfer learning to predict human fear ratings (on a 0-100 scale) from a standardized da…

A profile of vibe coding startup Lovable, which became the fastest-growing software startup in history, reaching $100M in annualized revenue in eight months (Iain Martin/Forbes)
https://www.forbes.com/sites/iainmartin/20

Vibe Coding Unicorn Lovable Is The Fastest Growing Software Startup Ever
Stockholm-based Lovable hit over $100 million in annualized revenue in just eight months by using AI to enable millions of non-coders to instantly turn their ideas into websites, apps and online side hustles.

SuperUROP: An FPGA-Based Spatial Accelerator for Sparse Matrix Operations
Rishab Parthasarathy
https://arxiv.org/abs/2509.11529 https://arxiv.org/pdf/2509.…

SuperUROP: An FPGA-Based Spatial Accelerator for Sparse Matrix Operations
Solving sparse systems of linear equations is a fundamental problem in the field of numerical methods, with applications spanning from circuit design to urban planning. These problems can have millions of constraints, such as when laying out transistors on a circuit, or trying to optimize traffic light timings, making fast sparse solvers extremely important. However, existing state-of-the-art software-level solutions for solving sparse linear systems, termed iterative solvers, are extremely ine…

#ElonMusk has spent over a decade making misleading promises about Tesla's "self-driving" software.
A federal jury found #Tesla partially liable for a fatal 2019 crash involving #Autopilot

Lawyer Who Defeated Tesla in Court Posts "Damn It Feels Good to Be a Gangsta" by Geto Boys
The lawyer behind a verdict, requiring Tesla to pay hundreds of millions to victims of a crash involving Autopilot, is understandably smug.

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering
Jielin Qiu, Zuxin Liu, Zhiwei Liu, Rithesh Murthy, Jianguo Zhang, Haolin Chen, Shiyu Wang, Ming Zhu, Liangwei Yang, Juntao Tan, Zhepeng Cen, Cheng Qian, Shelby Heinecke, Weiran Yao, Silvio Savarese, Caiming Xiong, Huan Wang
https://arxiv.org/abs/2…

LoCoBench: A Benchmark for Long-Context Large Language Models in Complex Software Engineering
The emergence of long-context language models with context windows extending to millions of tokens has created new opportunities for sophisticated code understanding and software development evaluation. We propose LoCoBench, a comprehensive benchmark specifically designed to evaluate long-context LLMs in realistic, complex software development scenarios. Unlike existing code evaluation benchmarks that focus on single-function completion or short-context tasks, LoCoBench addresses the critical e…