Warner Bros. rejects Paramount's hostile bid But will it ask them to buy stuff? Amazon just introduced a . This lets Alexa+ answer the doorbell and converse with visitors, which certainly sounds futuristic in a gated community as dystopia kind of way. There are several caveats here. First of all, it only works with certain newer Ring video doorbell models.

Amazon has launched a web portal for Alexa+ at Alexa.com, providing early access users with a ChatGPT-like interface featuring suggested prompts and easy text copying capabilities. PCWorld reports the portal allows file uploads for document analysis and offers improved usability compared to previous voice-only interactions with Amazon's AI assistant. Alexa+ remains free during early access, with future plans to offer it free to Prime members while charging non-Prime users a monthly fee. It's been nearly a year since Amazon first launched the new, AI-enhanced Alexa+, but until now, a key feature has been missing: the ability to access and chat with Alexa+ on the web. Now it appears Amazon has fulfilled that promise, with an Alexa+ web portal finally going live--for at least some Alexa+ early access users, anyway--at Alexa.com.

Kindle's in-book AI assistant can answer all your questions without spoilers But the catch is authors and publishers can't opt out of having this feature in their works. If you're several chapters into a novel and forgot who a character was, Amazon is hoping its new Kindle feature will jog your memory without ever having to put the e-reader down. This feature, called Ask this Book, was announced during Amazon's hardware event in September, but is finally available for US users on the Kindle iOS app. According to Amazon, the feature can currently be found on thousands of English best-selling Kindle titles and only reveals information up to your current reading position for spoiler-free responses. To use it, you can highlight a passage in any book you've bought or borrowed and ask it questions about plot, characters or other crucial details, and the AI assistant will offer immediate, contextual, spoiler-free information.

I thought I'd struck lucky on a dating app but invited a monster into my life Handsome, charming, a gentle giant - Katie Yates believed Jason Smith was a real catch after meeting him on a dating app. But within months he had subjected her to relentless physical and mental abuse before raping her and attempting to drown her in the bath just before Christmas. Katie, 42 and from Cardiff, has waived her anonymity as a victim of sexual assault to warn women to be wary of strangers they meet on dating apps who may pose as nice guys in an attempt to lure them in. You scroll on all the profiles with smiling photos and slick words but there are some people who should be looking for a therapist, not a girlfriend, she said. Katie had been single for five years when she signed up to a dating app in February 2018.

When you purchase through links in our articles, we may earn a small commission. I switched to Gemini for Home. Here's the Google Assistant feature I miss You'll need to pay up to get this key Google Assistant feature back. I recently made the switch to Gemini on my Google smart speakers and displays, and for the most part, I'm liking it. Gemini is chatty without being a blabbermouth, and it capably controls my smart home while also delivering detailed weather reports and answers to my other queries.

Studying how recommendation systems reshape social networks is difficult on live platforms: confounds abound, and controlled experiments risk user harm. We present an agent-based simulator where content production, tie formation, and a graph attention network (GAT) recommender co-evolve in a closed loop. We calibrate parameters using Mastodon data and validate out-of-sample against Bluesky (4--6\% error on structural metrics; 10--15\% on held-out temporal splits). Across 18 configurations at 100 agents, we find that \emph{activation timing} affects outcomes: introducing recommendations at $t=10$ vs.\ $t=40$ decreases transitivity by 10\% while engagement differs by $<$8\%. Delaying activation increases content diversity by 9\% while reducing modularity by 4\%. Scaling experiments ($n$ up to 5,000) show the effect persists but attenuates. Jacobian analysis confirms local stability under bounded reactance parameters. We release configuration schemas and reproduction scripts.

Ghislaine Maxwell's ultimate humiliation: Epstein's sex trafficker girlfriend poses in outrageous outfits and exposes herself in dozens of photos released from the billionaire paedophile's files I was falsely accused of being the Brown University shooter... Silent Trump flees growing storm over Epstein'cover-up' as he jets off for holidays without ANY comment Truth about THIS photo of Karoline Leavitt's face... and why if she was non-binary and disabled, Vanity Fair would never have done this: KENNEDY Why Conan O'Brien'stopped party guests calling 911' on Nick Reiner: Insiders reveal disturbing new details of final hours before Rob and Michele murders After 27 years as a TV anchor I was suddenly pulled off screens. My boss's explanation was a brutal lesson in loyalty Emily in Paris cast left'aghast' and'walking on eggshells' as off-camera drama becomes overwhelming... and whispers swirl about a CURSE Doctors said my hip pain was just tendinitis from sitting all day at work.

Language identification is a crucial first step in multilingual systems such as chatbots and virtual assistants, enabling linguistically and culturally accurate user experiences. Errors at this stage can cascade into downstream failures, setting a high bar for accuracy. Yet, existing language identification tools struggle with key cases -- such as music requests where the song title and user language differ. Open-source tools like LangDetect, FastText are fast but less accurate, while large language models, though effective, are often too costly for low-latency or low-resource settings. We introduce PolyLingua, a lightweight Transformer-based model for in-domain language detection and fine-grained language classification. It employs a two-level contrastive learning framework combining instance-level separation and class-level alignment with adaptive margins, yielding compact and well-separated embeddings even for closely related languages. Evaluated on two challenging datasets -- Amazon Massive (multilingual digital assistant utterances) and a Song dataset (music requests with frequent code-switching) -- PolyLingua achieves 99.25% F1 and 98.15% F1, respectively, surpassing Sonnet 3.5 while using 10x fewer parameters, making it ideal for compute- and latency-constrained environments.

Personalization in LLMs often relies on costly human feedback or interaction logs, limiting scalability and neglecting deeper user attributes. To reduce the reliance on human annotations, we introduce GRAVITY (Generative Response with Aligned Values, Interests, and Traits of You), a framework for generating synthetic, profile-grounded preference data that captures users' interests, values, beliefs, and personality traits. By integrating demographic, cultural, and psychological frameworks -- including Hofstede's cultural dimensions, Schwartz's basic values, the World Values Survey, and Big Five OCEAN traits -- GRAVITY synthesizes preference pairs to guide personalized content generation. We evaluate GRAVITY on book descriptions for 400 Amazon users, comparing it to prompt-based conditioning, standard fine-tuning, and naive synthetic pair generation. Profile-grounded synthetic data consistently improves generation, especially across multiple cultures (USA, Brazil, Japan, India), achieving over 4% higher preference gains across baselines, with user studies showing that GRAVITY outputs are preferred over 86% of the time. Our results show that scenario-grounded synthetic data can capture richer user variation, reduce reliance on costly annotation, and produce more engaging, user-centered content, offering a scalable path for LLM personalization.

GPU prices could follow RAM's big rise Spotify's new playlist feature gives users more control over their recommendation algorithm Users in New Zealand will be able to write prompts for custom playlists. Spotify is attempting to give users more control over the music the streaming service recommends with a new playlist feature called Prompted Playlist. The beta feature is rolling out in New Zealand starting on December 11, and will let users write a custom prompt that Spotify can use -- alongside their listening history -- to create a playlist of new music. By tapping on Prompted Playlist, Spotify subscribers participating in the beta will be presented with a prompt field where they can type exactly what they want to hear and how they want Spotify's algorithm to respond. And while past AI features took users' individual taste into consideration, Spotify claims Prompted Playlist taps into your entire Spotify listening history, all the way back to day one.