Tootfinder

In addition to the other EV reports published by BEUC, the European Consumer Association, today, there is also this one on improving the public EV charging experience. It addresses price transparency, competition, unfair pricing practices, and the need to accelerate dynamic pricing and smart charging on public charging points.

Recommendations for a better charging experience for electric car drivers
A consumer-friendly charging ecosystem is a precondition to the mass adoption of battery electric vehicles (BEVs). A growing number of charging stations are popping up in our cities, in parking lots and along EU’s roads. Yet, frustrations are still there when it comes to knowing the price of your charging session in advance, trying to compare offers, juggling between payment methods and facing high prices in certain locations.

Adobe launches AI agents tailored for B2B marketers within its Adobe Experience Platform, after launching AI agents aimed at consumer marketing in September (Mike Wheatley/SiliconANGLE)
https://siliconangle.com/2025/10/09/adobe-takes-aim-business-…

Adobe takes aim at business buyers with dedicated B2B AI agents - SiliconANGLE
Adobe takes aim at business buyers with dedicated B2B AI agents - SiliconANGLE

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
5 Team, Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, Kedong Wang, Lucen Zhong, Mingdao Liu, Rui Lu, Shulin Cao, Xiaohan Zhang, Xuancheng Huang, Yao Wei, Yean Cheng, Yifan An, Yilin Niu, Yuanhao Wen, Yushi Bai, Zhengxiao Du, Zihan Wang, Zilin Zhu, Bohan Zhang, Bosi Wen, Bowen Wu, Bowen Xu, Can Huang, Casey Zhao, Changpeng Cai, Chao Yu, Chen Li, Chendi …

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64…

Reinforcement Learning with Action Chunking
Qiyang Li, Zhiyuan Zhou, Sergey Levine
https://arxiv.org/abs/2507.07969 https://arxiv.org/pdf/2507.07969 https://arxiv.org/html/2507.07969
arXiv:2507.07969v1 Announce Type: new
Abstract: We present Q-chunking, a simple yet effective recipe for improving reinforcement learning (RL) algorithms for long-horizon, sparse-reward tasks. Our recipe is designed for the offline-to-online RL setting, where the goal is to leverage an offline prior dataset to maximize the sample-efficiency of online learning. Effective exploration and sample-efficient learning remain central challenges in this setting, as it is not obvious how the offline data should be utilized to acquire a good exploratory policy. Our key insight is that action chunking, a technique popularized in imitation learning where sequences of future actions are predicted rather than a single action at each timestep, can be applied to temporal difference (TD)-based RL methods to mitigate the exploration challenge. Q-chunking adopts action chunking by directly running RL in a 'chunked' action space, enabling the agent to (1) leverage temporally consistent behaviors from offline data for more effective online exploration and (2) use unbiased $n$-step backups for more stable and efficient TD learning. Our experimental results demonstrate that Q-chunking exhibits strong offline performance and online sample efficiency, outperforming prior best offline-to-online methods on a range of long-horizon, sparse-reward manipulation tasks.
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How much damage are ultraprocessed foods really doing to your health? #nutrition

How much damage are ultraprocessed foods really doing to your health?
Many foods we consume today are ultraprocessed, packed with unhealthy ingredients, and linked to major health risks. As consumption of these foods rises, so do chronic health issues, especially among lower-income groups. Experts are calling for clearer guidelines, better research, and systemic changes to reduce the impact of ultraprocessed foods on public health.

Series D, Episode 05 - Animals
BORR: That's against Bureau standing orders, Commissioner.
SERVALAN: Who do you think wrote those orders, Borr? I did. So do as I say and do it now.
BORR: At once, Commissioner Sleer.
https://blake.torpidity.net/m/405/180 B7B4

Been experimenting with Shortwave, a frontend to Gmail with serious AI capabilities. So far it impresses me and is able to do the things that Gmail Gemini was failing me at. Main drawback is it's fairly expensive for a consumer product.

Computational and Experimental Investigation of Chiral and Achiral 2D Organic Lead Bromide Perovskites: Octahedral Distortions and Electronic and Optical Properties
Md Mehdi Masud, Jarek Viera, Azza Ben-Akacha, Biwu Ma, David A. Strubbe
https://arxiv.org/abs/2509.07152

Computational and Experimental Investigation of Chiral and Achiral 2D Organic Lead Bromide Perovskites: Octahedral Distortions and Electronic and Optical Properties
We present a computational investigation, in conjunction with synthesis and experimental characterization, into the structural, electronic, and optical properties of layered 2D organic lead bromide perovskites. We contrast materials based on the chiral (R/S)-4-fluoro-$α$-methylbenzylammonium (R/S-FMBA), which have been shown to lead to bright room-temperature circularly polarized luminescence, with the similar achiral 4-fluorobenzylammonium (FBA). Using density functional theory (DFT) with van…

How human is the machine? Evidence from 66,000 Conversations with Large Language Models
Antonios Stamatogiannakis, Arsham Ghodsinia, Sepehr Etminanrad, Dilney Gon\c{c}alves, David Santos
https://arxiv.org/abs/2510.07321

How human is the machine? Evidence from 66,000 Conversations with Large Language Models
When Artificial Intelligence (AI) is used to replace consumers (e.g., synthetic data), it is often assumed that AI emulates established consumers, and more generally human behaviors. Ten experiments with Large Language Models (LLMs) investigate if this is true in the domain of well-documented biases and heuristics. Across studies we observe four distinct types of deviations from human-like behavior. First, in some cases, LLMs reduce or correct biases observed in humans. Second, in other cases, …

In addition to the other EV reports published by BEUC, the European Consumer Association, today, there is also this one on improving the public EV charging experience. It addresses price transparency, competition, unfair pricing practices, and the need to accelerate dynamic pricing and smart charging on public charging points.