From finding a boyfriend on Hinge to cruising on Sniffies, these are our top picks for different kinds of connections. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and . Editors and writers independently select products unless marked Sponsored or Promoted. Sponsored content is a paid ad, while content marked Promoted is chosen by Ziff Davis merchandising team and does not include input from individual authors. We may earn an affiliate commission if you buy through our links. These are the tech, tools, and products -- from laptops to e-readers, from earbuds to robovacs, and more -- that Mashable ranks best in class. Online dating as a gay man is a rite of passage. From the Craigslist m4m era to today's endless scroll, it's been a part of gay culture since the early days of the web. But for all the high rewards, even the best dating apps come with a unique set of challenges for LGBTQ men: ghosting, catfishing, straight guys who shouldn't be in your feed, and even some not-so-subtle fetishization. It's a lot to take in when you're just trying to find a cute guy. Yet, despite the BS, the best gay dating apps are still one of the main ways to meet new guys. In fact, a 2026 report by SSRS found that 63 percent of LGBTQIA+ adults have used a dating site or app, including 14 percent who were current users, compared to just 34 percent of non-LGBTQIA+ adults.

Bestselling author and former politician Jeffrey Archer has died at the age of 86. Archer sold more than 300 million books in his five-decade writing career, including the Clifton Chronicles and Kane and Abel, and often set the novels in his home town of Wellington, Somerset. In a statement, Archer's publisher HarperCollins said the author died suddenly and peacefully at home on Monday between writing sessions. The former Conservative MP was jailed for perjury in 2001 after being found guilty of lying and cheating in his 1987 libel case against the Daily Star. Archer was born in north London on 15 April 1940 but would quickly move to Weston-super-Mare in Somerset, where his family bought a house from John Cleese's mother.

As many have observed, Jeffrey Archer, who has died aged 86, led a life as colourful as the plot in one of his novels. He was, at one time or other, a senior Conservative politician, a best-selling author, a peer of the realm, and a convicted perjurer. Possessed of huge drive, vast wealth and immense personal charm, Archer bounced back from disaster and, more than once, rebuilt a shattered life. But for all his talents, he will be remembered as a man affected by a defective moral compass, whose public humiliation was entirely self-inflicted. Jeffrey Howard Archer was born in north London on 15 April 1940. Within two weeks, his parents moved to Weston-super-Mare in Somerset, where they bought a house from John Cleese's mother.

Archer Aviation acquires Hawthorne Airport for $126 million to create an air taxi hub for LA28 Olympics, despite local residents' noise concerns.

AI models such as ChatGPT routinely misrepresent news events, providing faulty responses to questions almost half the time, a study has found. The study published on Wednesday by the European Broadcasting Union (EBU) and the BBC assessed the accuracy of more than 2,700 responses given by OpenAI's ChatGPT, Google's Gemini, Microsoft's Copilot, and Perplexity. Overall, 45 percent of responses had at least one "significant" issue, according to the research. Sourcing was the most common problem, with 31 percent of responses including information not supported by the cited source, or incorrect or unverifiable attribution, among other issues. A lack of accuracy was the next biggest contributor to faulty answers, affecting 20 percent of responses, followed by the absence of appropriate context, with 14 percent.

Large Language Models (LLMs) have demonstrated remarkable performance across various tasks. A promising but largely under-explored area is their potential to facilitate human coordination with many agents. Such capabilities would be useful in domains including disaster response, urban planning, and real-time strategy scenarios. In this work, we introduce (1) a real-time strategy game benchmark designed to evaluate these abilities and (2) a novel framework we term HIVE. HIVE empowers a single human to coordinate swarms of up to 2,000 agents using natural language dialog with an LLM. We present promising results on this multi-agent benchmark, with our hybrid approach solving tasks such as coordinating agent movements, exploiting unit weaknesses, leveraging human annotations, and understanding terrain and strategic points. However, our findings also highlight critical limitations of current models, including difficulties in processing spatial visual information and challenges in formulating long-term strategic plans. This work sheds light on the potential and limitations of LLMs in human-swarm coordination, paving the way for future research in this area. The HIVE project page, which includes videos of the system in action, can be found here: hive.syrkis.com.

Reduced-order models (ROMs) provide lower dimensional representations of complex systems, capturing their salient features while simplifying control design. Building on previous work, this paper presents an overarching framework for the integration of ROMs and control barrier functions, enabling the use of simplified models to construct safety-critical controllers while providing safety guarantees for complex full-order models. To achieve this, we formalize the connection between full and ROMs by defining projection mappings that relate the states and inputs of these models and leverage simulation functions to establish conditions under which safety guarantees may be transferred from a ROM to its corresponding full-order model. The efficacy of our framework is illustrated through simulation results on a drone and hardware demonstrations on ARCHER, a 3D hopping robot.

A broad use case of large language models (LLMs) is in goal-directed decision-making tasks (or "agent" tasks), where an LLM needs to not just generate completions for a given prompt, but rather make intelligent decisions over a multi-turn interaction to accomplish a task (e.g., when interacting with the web, using tools, or providing customer support). Reinforcement learning (RL) provides a general paradigm to address such agent tasks, but current RL methods for LLMs largely focus on optimizing single-turn rewards. By construction, most single-turn RL methods cannot endow LLMs with the ability to intelligently seek information over multiple turns, perform credit assignment, or reason about their past actions -- all of which are critical in agent tasks. This raises the question: how can we design effective and efficient multi-turn RL algorithms for LLMs? In this paper, we develop a framework for building multi-turn RL algorithms for fine-tuning LLMs, that preserves the flexibility of existing single-turn RL methods for LLMs (e.g., proximal policy optimization), while accommodating multiple turns, long horizons, and delayed rewards effectively. To do this, our framework adopts a hierarchical RL approach and runs two RL algorithms in parallel: a high-level off-policy value-based RL algorithm to aggregate reward over utterances, and a low-level RL algorithm that utilizes this high-level value function to train a token policy within each utterance or turn. Our hierarchical framework, Actor-Critic Framework with a Hierarchical Structure (ArCHer), can also give rise to other RL methods. Empirically, we find that ArCHer significantly improves efficiency and performance on agent tasks, attaining a sample efficiency of about 100x over existing methods, while also improving with larger model capacity (upto the 7 billion scale that we tested on).

We present Archer, a challenging bilingual text-to-SQL dataset specific to complex reasoning, including arithmetic, commonsense and hypothetical reasoning. It contains 1,042 English questions and 1,042 Chinese questions, along with 521 unique SQL queries, covering 20 English databases across 20 domains. Notably, this dataset demonstrates a significantly higher level of complexity compared to existing publicly available datasets. Our evaluation shows that Archer challenges the capabilities of current state-of-the-art models, with a high-ranked model on the Spider leaderboard achieving only 6.73% execution accuracy on Archer test set. Thus, Archer presents a significant challenge for future research in this field.

Assassin's Creed Mirage is a dream for stealth kings. People who loved Sam Fisher in Splinter Cell or simply the old Assassin's Creeds will have a tremendous fun in beautiful 9th century Baghdad, our recent hands-on with the game revealed. We throw coins, briefly distract a guard, dart around corners. In that game, we are a bear of a man, with arms like tree trunks as we swing the axe and make the English army tremble. Valhalla also had its moments, but in Mirage there is much more of a hand-built feel.