Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Creator Playbook Look Up Back to School Mashable Selects Say More Trending Now Good Connection: Uplifting stories for a digital age Switch Off Mashable Voices Safety Net Versus All Series Apple wants the iPhone Duo to be your alarm clock. It may look pretty on your nightstand, but it doesn't have to stay there while you sleep. Rebecca Ruiz is a Senior Reporter at Mashable. She frequently covers mental health, digital culture, and technology. Her areas of expertise include suicide prevention, screen use and mental health, parenting, youth well-being, and meditation and mindfulness.

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What to do with your feelings about the pop star's appearance. Rebecca Ruiz is a Senior Reporter at Mashable. She frequently covers mental health, digital culture, and technology. Her areas of expertise include suicide prevention, screen use and mental health, parenting, youth well-being, and meditation and mindfulness. Rebecca's experience prior to Mashable includes working as a staff writer, reporter, and editor at NBC News Digital and as a staff writer at Forbes.

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As the Institute's first VP for energy and climate, Evelyn Wang '00 is marshaling MIT's expertise to meet the greatest challenge of our age. Professor Evelyn Wang '00 sits beside a compact, portable water-harvesting device that she developed in collaboration with Professor Rohit Karnik of MIT and Krista Walton, then a professor at Georgia Tech. It's designed for portable and emergency use. Water shortages in Southern California made an indelible impression on Evelyn Wang '00 when she was growing up in Los Angeles. "I was quite young, perhaps in first grade," she says. "But I remember we weren't allowed to turn our sprinklers on. And everyone in the neighborhood was given disinfectant tablets for the toilet and encouraged to keep flushing to a minimum. I didn't understand exactly what was happening. But I saw that everyone in the community was affected by the scarcity of this resource."

Seeking advice is a core human behavior that the Internet has reinvented twice: first through forums and Q\&A communities that crowdsource public guidance, and now through large language models (LLMs) that deliver private, on-demand counsel at scale. Yet the quality of this synthesized LLM advice remains unclear. How does it compare, not only against arbitrary human comments, but against the wisdom of the online crowd? We conducted two studies (N = 210) in which experts compared top-voted Reddit advice with LLM-generated advice. LLMs ranked significantly higher overall and on effectiveness, warmth, and willingness to seek advice again. GPT-4o beat GPT-5 on all metrics except sycophancy, suggesting that benchmark gains need not improve advice-giving. In our second study, we examined how human and algorithmic advice could be combined, and found that human advice can be unobtrusively polished to compete with AI-generated comments. Finally, to surface user expectations, we ran an exploratory survey with undergraduates (N=148) that revealed heterogeneous, persona-dependent preferences for agent qualities (e.g., coach-like: goal-focused structure; friend-like: warmth and humor). We conclude with design implications for advice-giving agents and ecosystems blending AI, crowd input, and expert oversight.

Amid the growing prevalence of human-AI interaction, large language models and other AI-based entities increasingly provide forms of companionship to human users. Such AI companionship -- i.e., bonded relationships between humans and AI systems that resemble the relationships people have with family members, friends, and romantic partners -- might substantially benefit humans. Yet such relationships can also do profound harm. We propose a framework for analyzing potential negative impacts of AI companionship by identifying specific harmful traits of AI companions and speculatively mapping causal pathways back from these traits to possible causes and forward to potential harmful effects. We provide detailed, structured analysis of four potentially harmful traits -- the absence of natural endpoints for relationships, vulnerability to product sunsetting, high attachment anxiety, and propensity to engender protectiveness -- and briefly discuss fourteen others. For each trait, we propose hypotheses connecting causes -- such as misaligned optimization objectives and the digital nature of AI companions -- to fundamental harms -- including reduced autonomy, diminished quality of human relationships, and deception. Each hypothesized causal connection identifies a target for potential empirical evaluation. Our analysis examines harms at three levels: to human partners directly, to their relationships with other humans, and to society broadly. We examine how existing law struggles to address these emerging harms, discuss potential benefits of AI companions, and conclude with design recommendations for mitigating risks. This analysis offers immediate suggestions for reducing risks while laying a foundation for deeper investigation of this critical but understudied topic.

Using 2.6 billion geolocated social-media posts (2014-2022) and a fine-tuned generative language model, we construct county-level indicators of life satisfaction and happiness for the United States. We document an apparent rural-urban paradox: rural counties express higher life satisfaction while urban counties exhibit greater happiness. We reconcile this by treating the two as distinct layers of subjective well-being, evaluative vs. hedonic, showing that each maps differently onto place, politics, and time. Republican-leaning areas appear more satisfied in evaluative terms, but partisan gaps in happiness largely flatten outside major metros, indicating context-dependent political effects. Temporal shocks dominate the hedonic layer: happiness falls sharply during 2020-2022, whereas life satisfaction moves more modestly. These patterns are robust across logistic and OLS specifications and align with well-being theory. Interpreted as associations for the population of social-media posts, the results show that large-scale, language-based indicators can resolve conflicting findings about the rural-urban divide by distinguishing the type of well-being expressed, offering a transparent, reproducible complement to traditional surveys.

Quantifying human flourishing, a multidimensional construct including happiness, health, purpose, virtue, relationships, and financial stability, is critical for understanding societal well-being beyond economic indicators. Existing measures often lack fine spatial and temporal resolution. Here we introduce the Human Flourishing Geographic Index (HFGI), derived from analyzing approximately 2.6 billion geolocated U.S. tweets (2013-2023) using fine-tuned large language models to classify expressions across 48 indicators aligned with Harvard's Global Flourishing Study framework plus attitudes towards migration and perception of corruption. The dataset offers monthly and yearly county- and state-level indicators of flourishing-related discourse, validated to confirm that the measures accurately represent the underlying constructs and show expected correlations with established indicators. This resource enables multidisciplinary analyses of well-being, inequality, and social change at unprecedented resolution, offering insights into the dynamics of human flourishing as reflected in social media discourse across the United States over the past decade.