Technology Best of What's New 5 personal care products that solved real problems in 2025 We may earn revenue from the products available on this page and participate in affiliate programs. In a market saturated with wellness products that promise to fix your whole life but rarely deliver much of anything, this year's personal care winners stand out for actually solving real problems. The 2025 class represents genuine inclusivity and thoughtful design--from a breast pump that goes old school to level up its wearability, to world-class headphones that double as hearing aids and workout coaches. Instead, they address overlooked challenges with smart engineering: making fragrance bottles easier to grip, transforming sleep routines for exhausted parents, and rethinking recovery gear so athletes can soothe strained muscles while on the move. Each winner proves that meaningful innovation happens when companies consider users' actual needs--and use that knowledge to make good products great.

Consumer research costs companies billions annually yet suffers from panel biases and limited scale. Large language models (LLMs) offer an alternative by simulating synthetic consumers, but produce unrealistic response distributions when asked directly for numerical ratings. We present semantic similarity rating (SSR), a method that elicits textual responses from LLMs and maps these to Likert distributions using embedding similarity to reference statements. Testing on an extensive dataset comprising 57 personal care product surveys conducted by a leading corporation in that market (9,300 human responses), SSR achieves 90% of human test-retest reliability while maintaining realistic response distributions (KS similarity > 0.85). Additionally, these synthetic respondents provide rich qualitative feedback explaining their ratings. This framework enables scalable consumer research simulations while preserving traditional survey metrics and interpretability.

The approach converts the toxic nonstick coating into harmless ingredients. Breakthroughs, discoveries, and DIY tips sent every weekday. The common coating known as Teflon can keep food from sticking to cookware, but it's notoriously difficult to break down safely. Now, researchers in the United Kingdom have discovered a simple and cost-effective solution to the problem. The results aren't simply eco-friendly--they can also be upcycled into helpful toothpaste and drinking water additives.

Large language models (LLMs) have shown remarkable capabilities in commonsense reasoning; however, some variations in questions can trigger incorrect responses. Do these models truly understand commonsense knowledge, or just memorize expression patterns? To investigate this question, we present the first extensive robustness evaluation of LLMs in commonsense reasoning. We introduce HellaSwag-Pro, a large-scale bilingual benchmark consisting of 11,200 cases, by designing and compiling seven types of question variants. To construct this benchmark, we propose a two-stage method to develop Chinese HellaSwag, a finely annotated dataset comprising 12,000 instances across 56 categories. We conduct extensive experiments on 41 representative LLMs, revealing that these LLMs are far from robust in commonsense reasoning. Furthermore, this robustness varies depending on the language in which the LLM is tested. This work establishes a high-quality evaluation benchmark, with extensive experiments offering valuable insights to the community in commonsense reasoning for LLMs.

Plenty of personal care products--the treatments and gadgets that fill our medicine cabinets, home gyms, and vanities--promise innovation. Companies that craft cosmetics, supplements, fitness tools, and other wellness aids tend to go hard on buzzwords without putting in the research to make something truly new. That doesn't mean there aren't worthwhile, forward-thinking personal care products available, though, and this year brought some notable offerings. From high-tech sleep and activity trackers that make peak performance possible to cutting-edge hair dryers that give your scalp a break from burns, these five beauty and wellness products actually back up their big promises. Be sure to read the full list of the 50 greatest innovations of 2024.)

Existing task-oriented AI agents often depend on explicit instructions or external rewards, limiting their ability to be driven by intrinsic motivations like humans. In this paper, we present a desire-driven autonomy framework to guide a Large Language Model-based (LLM-based) agent to simulate human-like daily activities. In contrast to previous agents, our Desire-driven Autonomous Agent (D2A) operates on the principle of intrinsic desire, allowing it to propose and select tasks that fulfill its motivational framework autonomously. Inspired by the Theory of Needs, the motivational framework incorporates an understanding of human-like desires, such as the need for social interaction, personal fulfillment, and self-care. Utilizing a desire-driven task generation mechanism, the agent evaluates its current state and takes a sequence of activities aligned with its intrinsic motivations. Through simulations, we demonstrate that our Desire-driven Autonomous Agent (D2A) generates coherent, contextually relevant daily activities while exhibiting variability and adaptability similar to human behavior. A comparative analysis with other LLM-based frameworks demonstrates that our approach significantly enhances the rationality of the simulated activities.

Robotic dentistry is becoming a reality. Your dentist may remind you to brush, floss and mouthwash – but what is the "right" order to do it? While all steps of oral hygiene can benefit dental health, Dr. Mike Wei, DDS, of New York City, shared with Fox News Digital that he'd recommend the below order. Starting with floss helps to remove food debris and plaque between the teeth and along the gumline, which a toothbrush "may not reach effectively," according to Wei. Dr. Ellie Phillips (not pictured) recommends using xylitol gum and mints to promote healthy salivary flow.

It's a perfect time to start focusing a little more on self-care. As beautiful fall turns into winter, we tend to hunker down in our houses, getting less sun and fresh air and spending more time under the covers. But between scares, you might want to browse these deals on gadgets that should help make you feel good all around. Special offer for Gear readers: Get a 1-year Subscription to WIRED for $5 ($25 off). This includes unlimited access to WIRED.com and our print magazine (if you'd like). Subscriptions help fund the work we do every day.

For many of us, our microwaves and dishwashers aren't the first thing that come to mind when trying to glean health information, beyond that we should (maybe) lay off the Hot Pockets and empty the dishes in a timely way. But we may soon be rethinking that, thanks to new research from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL). The system, called "Sapple," analyzes in-home appliance usage to better understand our health patterns, using just radio signals and a smart electricity meter. Taking information from two in-home sensors, the new machine learning model examines use of everyday items like microwaves, stoves, and even hair dryers, and can detect where and when a particular appliance is being used. For example, for an elderly person living alone, learning appliance usage patterns could help their health-care professionals understand their ability to perform various activities of daily living, with the goal of eventually helping advise on healthy patterns.

Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus has been on theoretical studies in the context of monopolistic firms and assumed product relationships, e.g., complementarity in usage. We develop a new machine-learning-driven methodology for designing bundles in a large-scale, cross-category retail setting. We leverage historical purchases and consideration sets created from clickstream data to generate dense continuous representations of products called embeddings. We then put minimal structure on these embeddings and develop heuristics for complementarity and substitutability among products. Subsequently, we use the heuristics to create multiple bundles for each product and test their performance using a field experiment with a large retailer. We combine the results from the experiment with product embeddings using a hierarchical model that maps bundle features to their purchase likelihood, as measured by the add-to-cart rate. We find that our embeddings-based heuristics are strong predictors of bundle success, robust across product categories, and generalize well to the retailer's entire assortment.