By 8 January as many as 6,000 bikini demands were being made to the chatbot every hour, according to analysis conducted for the Guardian. By 8 January as many as 6,000 bikini demands were being made to the chatbot every hour, according to analysis conducted for the Guardian. 'Add blood, forced smile': how Grok's nudification tool went viral The'put her in a bikini' trend rapidly evolved into hundreds of thousands of requests to strip clothes from photos of women, horrifying those targeted Like thousands of women across the world, Evie, a 22-year-old photographer from Lincolnshire, woke up on New Year's Day, looked at her phone and was alarmed to see that fully clothed photographs of her had been digitally manipulated by Elon Musk's AI tool, Grok, to show her in just a bikini. The "put her in a bikini" trend began quietly at the end of last year before exploding at the start of 2026. Within days, hundreds of thousands of requests were being made to the Grok chatbot, asking it to strip the clothes from photographs of women.

This year, the right-to-repair movement got a boost from--surprisingly--big tech, tariffs, and economic downturn. It has been a big year for the right to repair, the movement of advocates pushing for people to be able to fix their own electronics and equipment without manufacturer approval. The issue has gathered broad support from technologists, farmers, military leaders, and politicians on both sides of the aisle. It is popular with just about everyone--except the companies who stand to gain if the parts, instructions, and tools necessary to fix their products remain under lock and key. Three US states passed right-to-repair laws this year, including in heavily Republican states like Texas where the measure received a unanimous vote in both the House and Senate.

W hen the billionaire chief executive of AI chipmaker Nvidia threw a party in central London for Donald Trump's state visit in September, the power imbalance between Silicon Valley and British politicians was vividly exposed. Jensen Huang hastened to the stage after meetings at Chequers and rallied his hundreds of guests to cheer on the power of AI. In front of a huge Nvidia logo, he urged the venture capitalists before him to herald "a new industrial revolution", announced billions of pounds in AI investments and, like Willy Wonka handing out golden tickets, singled out some lucky recipients in the room. "If you want to get rich, this is where you want to be," he declared. But his biggest party trick was a surprise guest waiting in the wings.

'It was extremely pornographic': Cara Hunter on the deepfake video that nearly ended her political career The Irish politician was targeted in 2022, in the final weeks of her run for office. When Cara Hunter, the Irish politician, looks back on the moment she found out she had been deepfaked, she says it is "like watching a horror movie". The setting is her grandmother's rural home in the west of Tyrone on her 90th birthday, April 2022. "Everyone was there," she says. "I was sitting with all my closest family members and family friends when I got a notification through Facebook Messenger." It was from a stranger.

Zohran Mamdani ran an online campaign based on real people and a real message. There are many fair questions following Zohran Mamdani's decisive victory. Will his campaign be a template for others? Will he be able or allowed to follow through on his campaign promises? Will the Democratic establishment accept that its future could look something like this proud 34-year-old democratic socialist?

Political language is deeply intertwined with social identities. While social identities are often shaped by specific cultural contexts and expressed through particular uses of language, existing datasets for group and identity detection are predominantly English-centric, single-label and focus on coarse identity categories. We introduce HebID, the first multilabel Hebrew corpus for social identity detection: 5,536 sentences from Israeli politicians' Facebook posts (Dec 2018-Apr 2021), manually annotated for twelve nuanced social identities (e.g. Rightist, Ultra-Orthodox, Socially-oriented) grounded by survey data. We benchmark multilabel and single-label encoders alongside 2B-9B-parameter generative LLMs, finding that Hebrew-tuned LLMs provide the best results (macro-$F_1$ = 0.74). We apply our classifier to politicians' Facebook posts and parliamentary speeches, evaluating differences in popularity, temporal trends, clustering patterns, and gender-related variations in identity expression. We utilize identity choices from a national public survey, enabling a comparison between identities portrayed in elite discourse and the public's identity priorities. HebID provides a comprehensive foundation for studying social identities in Hebrew and can serve as a model for similar research in other non-English political contexts.

We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative validity of political entities. We curate and manually annotate a novel Hebrew-language corpus of 10,410 sentences drawn from Knesset speeches (1993-2023), Facebook posts (2018-2021), and leading news outlets, of which 1,812 instances (17.4\%) exhibit PDD and 642 carry additional annotations for intensity, incivility, target type, and affective framing. We introduce a two-stage classification pipeline combining finetuned encoder models and decoder LLMs. Our best model (DictaLM 2.0) attains an F$_1$ of 0.74 for binary PDD detection and a macro-F$_1$ of 0.67 for classification of delegitimization characteristics. Applying this classifier to longitudinal and cross-platform data, we see a marked rise in PDD over three decades, higher prevalence on social media versus parliamentary debate, greater use by male than female politicians, and stronger tendencies among right-leaning actors - with pronounced spikes during election campaigns and major political events. Our findings demonstrate the feasibility and value of automated PDD analysis for understanding democratic discourse.

LLMs are remarkable artifacts that have revolutionized a range of NLP and AI tasks. A significant contributor is their factual knowledge, which, to date, remains poorly understood, and is usually analyzed from biased samples. In this paper, we take a deep tour into the factual knowledge (or beliefs) of a frontier LLM, based on GPTKB v1.5 (Hu et al., 2025a), a recursively elicited set of 100 million beliefs of one of the strongest currently available frontier LLMs, GPT-4.1. We find that the models' factual knowledge differs quite significantly from established knowledge bases, and that its accuracy is significantly lower than indicated by previous benchmarks. We also find that inconsistency, ambiguity and hallucinations are major issues, shedding light on future research opportunities concerning factual LLM knowledge.

Harry Jackson went into Kathmandu as a tourist. He ended up being one of the main international sources of news on Nepal's Gen Z protests. When Harry Jackson pulled his small motorcycle into Kathmandu on September 8, he had no idea the city was exploding in protests. He didn't even know there was a curfew. People in Nepal, largely driven by Gen Z youth, had taken to the streets, and that day riots broke out when nearly two dozen people were shot and killed by authorities.

Large Language Models (LLMs) display remarkable capabilities to understand or even produce political discourse, but have been found to consistently display a progressive left-leaning bias. At the same time, so-called persona or identity prompts have been shown to produce LLM behavior that aligns with socioeconomic groups that the base model is not aligned with. In this work, we analyze whether zero-shot persona prompting with limited information can accurately predict individual voting decisions and, by aggregation, accurately predict positions of European groups on a diverse set of policies. We evaluate if predictions are stable towards counterfactual arguments, different persona prompts and generation methods. Finally, we find that we can simulate voting behavior of Members of the European Parliament reasonably well with a weighted F1 score of approximately 0.793. Our persona dataset of politicians in the 2024 European Parliament and our code are available at https://github.com/dess-mannheim/european_parliament_simulation.