It seems like only a few years ago that Anker made nothing more than batteries and chargers . But 15 years into its history, the company's CES portfolio continues to illustrate how much it's expanded. Among other announcements, the company has a new robot vacuum, video doorbell, outdoor light and smart lock. The company hopes its Eufy Clean Robot Vacuum Omni S2 will be your next robovac. Anker claims the vac works on shag carpets up to about 2 inches (5 cm) in pile height.

Amazon announced at CES 2026 that Alexa+ for web will come to Early Access customers first. You no longer need an Amazon device to summon Alexa since the AI assistant will be available on the Alexa.com Amazon will roll out the web client to its Alexa+ Early Access customers first, still featuring the company's new and improved AI assistant that was announced in February. The web model won't be much different from the existing Alexa+ that's already available on Echo devices, Fire TV and Fire tablets. However, instead of buying into the Amazon ecosystem, Early Access customers can use any web browser to get Alexa+'s help with everything from basic questions to complex tasks, like controlling your smart home devices, customizing a recipe to account for dietary restrictions or making restaurant reservations if you don't feel like cooking. The Alexa+ web version will also feature a navigation sidebar that will be home to your most-used Alexa features.

They're earbuds built for the voice command age. There's a good chance you spend more time talking to your phone's virtual assistant, or dictating text with your voice, instead of actually calling people these days. But, as convenient as voice input can be, you don't want to be the obnoxious person shouting commands to Siri or ChatGPT in a quiet library. And you probably won't have much luck dictating an email in a room with toddlers screaming and Peppa Pig blaring on the TV. At CES 2026, the startup Subtle is unveiling its solution to those issues: Voicebuds.

Why Maduro dated wife for TWENTY years before marrying in secret... as shamed first lady's incestuous party links laid bare Maduro's brazen gestures to onlookers as he arrives at rat-infested'hell hole' prison in Brooklyn Trump says Venezuelan opposition leader who beat him to Nobel prize'does not have support' to be president Inside the CIA's astonishing coup of Caracas: SETH HARP reveals the planning... the traitor... and how it's about to blow up in American faces Inside Operation'Absolute Resolve': The meticulous months-long plan to seize Maduro and the harrowing escape under fire Leonardo DiCaprio forced to skip film festival in California by Trump's Venezuela airstrikes as he accepts acting prize via video Inside the glamorous Dubai life of Daniel Kinahan, head of the notorious crime family and Europe's top cocaine kingpin... as GUY ADAMS reveals how net is finally closing in on the gangster after golden decade in the Middle East Explosive warning from America's house price Nostradamus: New forecast tracking over 300 markets reveals'rare' shift... the boomtowns set to sink... and surprising winners I thought I'd found the perfect boyfriend. Then I discovered what he really wanted... Wild moment NYC jeweler attacks'scammer' rival and claims he is impersonating him I'm 38 and hotter than ever after finally silencing food noise with my simple new method... you don't need drugs or diets Meghan McCain, 41, joyously announces birth of third child and reveals baby's unconventional name Female college football fan labeled a Karen for furious reaction to rival in the stands... but was she right all along? Snitch reveals the TRUTH about Tom Brady and Alix Earle... after handsy video leaked out of St Barts The'ideal man' is now a 5'7 architect, dating app analysis reveals READ MORE: Dating apps are designed to keep singles'swiping and spending' The saying goes that'beauty is in the eye of the beholder'. But have you ever wondered what the'perfect' man looks like? Now, just in time for'Dating Sunday' - the busiest day of the year on dating apps - happn has uncovered what the most popular men on its dating app in 2025 look like.

Why Maduro dated wife for TWENTY years before marrying in secret... as shamed first lady's incestuous party links laid bare Maduro's brazen gestures to onlookers as he arrives at rat-infested'hell hole' prison in Brooklyn Brutal Venezuelan dictator Nicolás Maduro dramatically'seized by US as he tried to flee to panic room' Inside the CIA's astonishing coup of Caracas: SETH HARP reveals the planning... the traitor... and how it's about to blow up in American faces Thousands trapped in paradise INDEFINITELY as Trump's Maduro arrest halts all flights to and from 19 Caribbean islands Eagle eyed viewers spot odd detail in background of Trump's Mar-a-Lago war room during Maduro strike in Venezuela Inside the glamorous Dubai life of Daniel Kinahan, head of the notorious crime family and Europe's top cocaine kingpin... as GUY ADAMS reveals how net is finally closing in on the gangster after golden decade in the Middle East Explosive warning from America's house price Nostradamus: New forecast tracking over 300 markets reveals'rare' shift... the boomtowns set to sink... and surprising winners I thought I'd found the perfect boyfriend. Then I discovered what he really wanted... Emergency exit was'always locked', bartender claims amid probe into deadly Swiss ski resort inferno I'm 38 and hotter than ever after finally silencing food noise with my simple new method... you don't need drugs or diets Female college football fan labeled a Karen for furious reaction to rival in the stands... but was she right all along? Meghan McCain, 41, joyously announces birth of third child and reveals baby's unconventional name Snitch reveals the TRUTH about Tom Brady and Alix Earle... after handsy video leaked out of St Barts NFL fans go crazy as Buccaneers player Elijah Roberts' mom crushes national anthem before huge Panthers game Taylor Swift's wedding dress at BFF Este Haim's nuptials has fans spotting secret Easter Egg Today is the busiest day of the YEAR for dating apps - here's the best time to go online If you're hoping to find love in 2026, today could be a good day to get started. January 4 is the busiest day of the year for dating app activity, with the highest number of messages and likes sent. This is largely driven by New Year's resolutions and a desire for fresh starts, leading to huge spikes in users swiping, matching and messaging.

When you purchase through links in our articles, we may earn a small commission. From overheating smart beds and privacy snafus to generative AI foul-ups and bricked smart thermostats, we had a bumper crop of smart home fails this year. Sometimes the smart home doesn't seem so smart, like when a cloud outage renders your devices useless or your smart bed starts roasting you in your sleep. We see these kinds of smart-home snafus every year, but 2025 was a tad different thanks to the arrival of generative AI assistants like Alexa+ and Google Gemini, which arrived with great fanfare but failed to truly blow our minds. So without further ado, we present the biggest smart home flops, fails, and foul-ups of the year, ranked in ascending order.

However, one significant challenge researchers face is the lack of comprehensive datasets to explore and advance the field of smart home rule recommendations.

In this paper, we introduce a novel technique for constrained submodular maximization, inspired by barrier functions in continuous optimization. This connection not only improves the running time for constrained submodular maximization but also provides the state of the art guarantee. More precisely, for maximizing a monotone submodular function subject to the combination of a $k$-matchoid and $\ell$-knapsack constraints (for $\ell\leq k$), we propose a potential function that can be approximately minimized. Once we minimize the potential function up to an $\epsilon$ error, it is guaranteed that we have found a feasible set with a $2(k+1+\epsilon)$-approximation factor which can indeed be further improved to $(k+1+\epsilon)$ by an enumeration technique. We extensively evaluate the performance of our proposed algorithm over several real-world applications, including a movie recommendation system, summarization tasks for YouTube videos, Twitter feeds and Yelp business locations, and a set cover problem.

Graph-based Collaborative Filtering (GCF) is widely used in personalized recommendation systems. However, GCF suffers from a fundamental problem where features tend to occupy the embedding space inefficiently (by spanning only a low-dimensional subspace). Such an effect is characterized in GCF by the embedding space being dominated by a few of popular items with the user embeddings highly concentrated around them. This enhances the so-called Matthew effect of the popularity bias where popular items are highly recommend whereas remaining items are ignored. In this paper, we analyze the above effect in GCF and reveal that the simplified graph convolution operation (typically used in GCF) shrinks the singular space of the feature matrix. As typical approaches (i.e., optimizing the uniformity term) fail to prevent the embedding space degradation, we propose a decorrelation-enhanced GCF objective that promotes feature diversity by leveraging the so-called principle of redundancy reduction in embeddings. However, unlike conventional methods that use the Euclidean geometry to relax hard constraints for decorrelation, we exploit non-Euclidean geometry. Such a choice helps maintain the range space of the matrix and obtain small condition number, which prevents the embedding space degradation. Our method outperforms contrastive-based GCF models on several benchmark datasets and improves the performance for unpopular items.

Graph contrastive learning (GCL) has emerged as a potent technology for numerous graph learning tasks. It has been successfully applied to real-world recommender systems, where the contrastive loss and the downstream recommendation objectives are always combined to form the overall objective function. Such a strategy is inconsistent with the original GCL paradigm, where graph embeddings are pre-trained without involving downstream training objectives. In this paper, we innovatively propose a prompt-enhanced framework for GCL-based recommender systems, namely CPTPP, which can fully leverage the advantages of the original GCL protocol through prompt tuning. Specifically, we first summarise user profiles in graph recommender systems to automatically generate personalized user prompts. These prompts will then be combined with pre-trained user embeddings to conduct prompt-tuning in downstream tasks, thereby narrowing the distinct targets between pre-training and downstream tasks.