Plus: Aura's new digital photo frame goes wireless, a mood-morphing watch, Wyze and TP-Link unveil solar-powered outdoor security cameras, and Intel will open "AI Experience Stores" in five cities. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. What are the odds that AI browsers launch in one week? OpenAI announced Atlas on Wednesday, a ChatGPT-powered Chromium browser, but a tiny startup called Nimo also debuted Nimo Infinity, a canvas-style AI browser with a generative user interface.

How Long Do Vacuums Last? Here are the signs to look for, and how to help your vacuum live longer. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Be honest: how long have you been lugging around your current vacuum?

LLMs have recently demonstrated strong potential in simulating online shopper behavior. Prior work has improved action prediction by applying SFT on action traces with LLM-generated rationales, and by leveraging RL to further enhance reasoning capabilities. Despite these advances, current approaches rely on text-based inputs and overlook the essential role of visual perception in shaping human decision-making during web GUI interactions. In this paper, we investigate the integration of visual information, specifically webpage screenshots, into behavior simulation via VLMs, leveraging OPeRA dataset. By grounding agent decision-making in both textual and visual modalities, we aim to narrow the gap between synthetic agents and real-world users, thereby enabling more cognitively aligned simulations of online shopping behavior. Specifically, we employ SFT for joint action prediction and rationale generation, conditioning on the full interaction context, which comprises action history, past HTML observations, and the current webpage screenshot. To further enhance reasoning capabilities, we integrate RL with a hierarchical reward structure, scaled by a difficulty-aware factor that prioritizes challenging decision points. Empirically, our studies show that incorporating visual grounding yields substantial gains: the combination of text and image inputs improves exact match accuracy by more than 6% over text-only inputs. These results indicate that multi-modal grounding not only boosts predictive accuracy but also enhances simulation fidelity in visually complex environments, which captures nuances of human attention and decision-making that text-only agents often miss. Finally, we revisit the design space of behavior simulation frameworks, identify key methodological limitations, and propose future research directions toward building efficient and effective human behavior simulators.

Online shopping giant Temu has agreed to work with the greeting card industry to remove copied designs from its site more quickly. Designers told the BBC the process for getting the plagiarised listings removed has been like the fairground game'whack-a-mole' with copied products re-appearing within days. Temu said protecting intellectual property was a top priority and that it was encouraging sellers to join the trial of a new takedown process specifically for the greetings card industry. Amanda Mountain, the co-founder of York-based Lola Design, discovered the catalogue of designs she had built up over a decade had nearly all been copied. She found the images she had created had been lifted and were being advertised by other sellers on cards and other products like t-shirts.

What Should Be in Your Emergency Kit Before Disaster Strikes? We consulted preparedness experts and WIRED's team of testers on the essential bug-out gear to keep your family safe during an unplanned exit. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. You never know when you're going to have to bug out on short notice.

Text embedding models play a cornerstone role in AI applications, such as retrieval-augmented generation (RAG). While general-purpose text embedding models demonstrate strong performance on generic retrieval benchmarks, their effectiveness diminishes when applied to private datasets (e.g., company-specific proprietary data), which often contain specialized terminology and lingo. In this work, we introduce BMEmbed, a novel method for adapting general-purpose text embedding models to private datasets. By leveraging the well-established keyword-based retrieval technique (BM25), we construct supervisory signals from the ranking of keyword-based retrieval results to facilitate model adaptation. We evaluate BMEmbed across a range of domains, datasets, and models, showing consistent improvements in retrieval performance. Moreover, we provide empirical insights into how BM25-based signals contribute to improving embeddings by fostering alignment and uniformity, highlighting the value of this approach in adapting models to domain-specific data. We release the source code available at https://github.com/BaileyWei/BMEmbed for the research community.

The £5.30 orange juice that tells the story of why supermarket prices are sky high There has been more than a bitter twang in the glasses at British breakfast tables. Only five years ago, a typical supermarket own-label carton of orange juice could be bought for 76p for 1 litre. One colleague was outraged to be sent a bill for £9 for a glass of hangover-busting orange juice and lemonade at an unassuming little restaurant in Kent. Asked why so much, she was told that the orange juice - albeit freshly squeezed - accounted for £5.30 of the price. Yet as costs have surged, the taste is changing too, with certain manufacturers substituting oranges for mandarins to cut costs.

This holiday season, more shoppers are expected to use chatbots to figure out what to buy. This holiday season, rather than searching on Google, more Americans will likely be turning to large language models to find gifts, deals, and sales. Retailers could see up to a 520 percent increase in traffic from chatbots and AI search engines this year compared to 2024, according to a recent shopping report from Adobe . OpenAI is already moving to capitalize on the trend: Last week, the ChatGPT maker announced a major partnership with Walmart that will allow users to buy goods directly within the chat window. As people start relying on chatbots to discover new products, retailers are having to rethink their approach to online marketing.

Simulating step-wise human behavior with Large Language Models (LLMs) has become an emerging research direction, enabling applications in various practical domains. While prior methods, including prompting, supervised fine-tuning (SFT), and reinforcement learning (RL), have shown promise in modeling step-wise behavior, they primarily learn a population-level policy without conditioning on a user's persona, yielding generic rather than personalized simulations. In this work, we pose a critical question: how can LLM agents better simulate personalized user behavior? We introduce Customer-R1, an RL-based method for personalized, step-wise user behavior simulation in online shopping environments. Our policy is conditioned on an explicit persona, and we optimize next-step rationale and action generation via action correctness reward signals. Experiments on the OPeRA dataset emonstrate that Customer-R1 not only significantly outperforms prompting and SFT-based baselines in next-action prediction tasks, but also better matches users' action distribution, indicating higher fidelity in personalized behavior simulation.

In just seven minutes, the thieves took off with crown jewels containing with thousands of diamonds along with other precious gems. Police stand outside the Louvre after a brazen theft. Could the French TV series have been prophetic? The show envisioned a heist at the Louvre, an event that became reality on the morning of October 19, when a group of professional thieves managed to break into the world-famous Paris museum . In just seven minutes, they stole a host of priceless French crown jewels.