Amazon's eReaders are best-in-class, and offer a legitimate opportunity for distraction-free reading. We may earn revenue from the products available on this page and participate in affiliate programs. The right Kindle will reignite your love of reading. Using a Kindle may seem unnecessary in a world where reading books, articles, and any other text on a phone or tablet is easy. Carrying around a dedicated mono-tasking device will add weight to your load, and it's another gadget to keep track of and charge. Yet Kindles remain popular because they only have one job and do it very well: let you carry and consume the stories that captivate you. A Kindle's e-ink screen won't reflect the sun when reading outdoors, unlike the reflective LCD displays used on phones and tablets.

Make sure your e-reader is set up exactly the way you want it. There are plenty of ways to tweak how your Kindle works. Breakthroughs, discoveries, and DIY tips sent six days a week. All of the Amazon Kindle models are intentionally designed to be straightforward to use. Grab your Kindle, tap the power button, and you're back reading from the place you left off (it's almost as simple as opening a real book).

Just over half (51%) of published novelists in the UK say that artificial intelligence is likely to end up entirely replacing their work as fiction writers, a new report from the University of Cambridge has found. Close to two-thirds (59%) of novelists say they know their work has been used to train AI Large Language Models (LLMs) without permission or payment. Over a third (39%) of novelists say their income has already taken a hit from generative AI, for example due to loss of other work that facilitates novel writing. Most (85%) novelists expect their future income to be driven down by AI. In new research for Cambridge's Minderoo Centre for Technology and Democracy (MCTD), Dr Clementine Collett surveyed 258 published novelists earlier this year, as well as 74 industry insiders - from commissioning editors to literary agents - to gauge how AI is viewed and used in the world of British fiction.*

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.

Realistic 3D city generation is fundamental to a wide range of applications, including virtual reality and digital twins. However, most existing methods rely on training a single diffusion model, which limits their ability to generate personalized and boundless city-scale scenes. In this paper, we present Yo'City, a novel agentic framework that enables user-customized and infinitely expandable 3D city generation by leveraging the reasoning and compositional capabilities of off-the-shelf large models. Specifically, Yo'City first conceptualize the city through a top-down planning strategy that defines a hierarchical "City-District-Grid" structure. The Global Planner determines the overall layout and potential functional districts, while the Local Designer further refines each district with detailed grid-level descriptions. Subsequently, the grid-level 3D generation is achieved through a "produce-refine-evaluate" isometric image synthesis loop, followed by image-to-3D generation. To simulate continuous city evolution, Yo'City further introduces a user-interactive, relationship-guided expansion mechanism, which performs scene graph-based distance- and semantics-aware layout optimization, ensuring spatially coherent city growth. To comprehensively evaluate our method, we construct a diverse benchmark dataset and design six multi-dimensional metrics that assess generation quality from the perspectives of semantics, geometry, texture, and layout. Extensive experiments demonstrate that Yo'City consistently outperforms existing state-of-the-art methods across all evaluation aspects.

Abstract--Recent advances in large language models have created new opportunities for stylometry, the study of writing styles and authorship. Two challenges, however, remain central: training generative models when no paired data exist, and evaluating stylistic text without relying only on human judgment. In this work, we present a framework for both generating and evaluating sentences in the style of 19th-century novelists. Large language models are fine-tuned with minimal, single-token prompts to produce text in the voices of authors such as Dickens, Austen, Twain, Alcott, and Melville. T o assess these generative models, we employ a transformer-based detector trained on authentic sentences, using it both as a classifier and as a tool for stylistic explanation. We complement this with syntactic comparisons and explainable AI methods, including attention-based and gradient-based analyses, to identify the linguistic cues that drive stylistic imitation. Our findings show that the generated text reflects the authors' distinctive patterns and that AI-based evaluation offers a reliable alternative to human assessment. All artifacts of this work are published online. The ability to recognize and reproduce an author's writing style has long fascinated both literary scholars and computer scientists. Stylometry, the quantitative study of writing style, rests on the idea that every author leaves behind unconscious patterns in vocabulary, syntax, and rhythm [2, 3]. These patterns have been analyzed for centuries in questions of disputed authorship, the study of literary traditions, and more recently in applications such as security and forensics [4].

Text-embedded image generation plays a critical role in industries such as graphic design, advertising, and digital content creation. Text-to-Image generation methods leveraging diffusion models, such as TextDiffuser-2, have demonstrated promising results in producing images with embedded text. TextDiffuser-2 effectively generates bounding box layouts that guide the rendering of visual text, achieving high fidelity and coherence. However, existing approaches often rely on resource-intensive processes and are limited in their ability to run efficiently on both CPU and GPU platforms. To address these challenges, we propose a novel two-stage pipeline that integrates reinforcement learning (RL) for rapid and optimized text layout generation with a diffusion-based image synthesis model. Our RL-based approach significantly accelerates the bounding box prediction step while reducing overlaps, allowing the system to run efficiently on both CPUs and GPUs. Extensive evaluations demonstrate that our framework achieves comparable performance to TextDiffuser-2 in terms of text placement and image synthesis, while offering markedly faster runtime and increased flexibility. Our method produces high-quality images comparable to TextDiffuser-2, while being 42.29 times faster and requiring only 2 MB of CPU RAM for inference, unlike TextDiffuser-2's M1 model, which is not executable on CPU-only systems.

Is reading always better for your brain than listening to audiobooks? Reading books and listening to audiobooks tap into different elements of cognition, each with their own benefits. So which one should you choose, and when? But when a friend recently asked me whether her daughter was getting the same cognitive benefits from an audiobook as she would from reading, my instinct was to think "she's enjoying a book, the format doesn't matter". However, when I dug into the science, I found the medium does shape the mind in subtly different but meaningful ways.

The Access to Algorithmic Justice project (A2AJ) is an open-source alternative to the Canadian Legal Information Institute (CanLII). At a moment when technology promises to enable new ways of working with law, CanLII is becoming an impediment to the free access of law and access to justice movements because it restricts bulk and programmatic access to Canadian legal data. This means that Canada is staring down a digital divide: well-resourced actors have the best new technological tools and, because CanLII has disclaimed leadership, the public only gets second-rate tools. This article puts CanLII in its larger historical context and shows how long and deep efforts to democratize access to Canadian legal data are, and how often they are thwarted by private industry. We introduce the A2AJ's Canadian Legal Data project, which provides open access to over 116,000 court decisions and 5,000 statutes through multiple channels including APIs, machine learning datasets, and AI integration protocols. Through concrete examples, we demonstrate how open legal data enables courts to conduct evidence-based assessments and allows developers to create tools for practitioners serving low-income communities.

DeeperDive, a new tool that converses with readers, is an effort to beat the AI industry at its own game. The publishing company behind USA Today and 220 other publications is today rolling out a chatbot -like tool called DeeperDive that can converse with readers, summarize insights from its journalism, and suggest new content from across its sites. "Visitors now have a trusted AI answer engine on our platform for anything they want to engage with, anything they want to ask," Mike Reed, CEO of Gannett and the USA Today Network, said at the WIRED AI Power Summit in New York, an event that brought together voices from the tech industry, politics, and the world of media. "and it is performing really great." Most publishers have a fraught relationship with AI, as the chatbots that trained on their content are now summarizing it and eating the traffic that search engines used to send them.