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Anker’s Sleep Speaker uses radar and flower power to help you relax
Peace, man. | Image: Anker Anker's aptly named Sleep Speaker promises to track and enhance sleep without a wearable. It uses millimeter-wave radar (60GHz) blasting from your nightstand to track micro-movements in your chest and body to continuously monitor your heart rate and breathing to enhance whatever sleep stage you're in. It also embeds lots of hippie shit. If I'm understanding things correctly, the speaker will - over the course of a month - determine which routines help you get the most rest, using acoustic binaural beats and lighting to gradually deepen relaxation. It operates with a touch of a button, independently of your phone, and doesn't require a paid … Read the full story at The Verge.
Anker’s Soundcore is bringing its incredible call quality to more headphones
The Soundcore Space 2 Pro will be the first headphones to run on Anker’s Thus chip. | Image: Soundcore After introducing the Thus processing chip in April and releasing it with the Liberty 5 Pro series earbuds, Soundcore is bringing it to more audio products. The Thus chip is behind the best call quality I've ever heard from a pair of earbuds or headphones, virtually eliminating ambient sounds from the speaker's environment. At IFA 2026 in Berlin, Soundcore announced the Space 2 Pro headphones, AeroClip 2 and AeroClip 2 Pro open clip earbuds, and Liberty Buds 2. The Space 2 Pro will be the first headphones to use the Thus chip and use a 6-microphone array for voice isolation. There are also eight microphones for the headphones' adaptive acti … Read the full story at The Verge.
Tension wood: A 'muscle' that can both bend and straighten plants
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What Actually Separates a Working AI Assistant From an Expensive Chat Widget
Most conversational AI integrations don t fail because the model is weak. They fail because nobody scoped what it s allowed to do.Continue reading on Medium »
Why Most GPU Providers Cannot Answer a Buyer’s First Three Questions
Gartner projects neoclouds will capture 20% of a $267 billion AI cloud market by 2030. That growth rate has outpaced the number of Continue reading on Medium »
Gemini 3.8 Flash Released
How to use Gemini 3.8 Flash for free?Continue reading on Data Science in Your Pocket »
AI Avatar Video Generator: The Future of AI Video Creation and Digital Content
Video content has become one of the most powerful ways to communicate information online. Businesses, marketers, educators, influencers Continue reading on Medium »
Meta-ethics and AI: exploring the novel meta-ethical questions in the era of AI
arXiv:2609.01685v1 Announce Type: new Abstract: With the development of artificial intelligence (AI), the landscape of meta-ethics, which has largely centred on human ethics, faces pressures that may significantly reconfigure it. In particular, if future AI systems were to exhibit sufficiently integrated capacities for moral reasoning, moral intentionality, and moral reflection, novel meta-ethical questions would arise concerning what I call "AI's own ethics", as distinct from ethical principles merely imposed on AI by human designers. This paper offers a conditional and methodological framework for identifying the questions that would emerge if such AI systems were to arise. On that basis, the paper distinguishes four domains of meta-ethical inquiry in the era of AI: questions about the nature of human ethics from the human perspective; questions about the nature of AI's own ethics from the human perspective; questions about the nature of human ethics from the AI perspective; and ques
WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling
arXiv:2609.01608v1 Announce Type: new Abstract: Black-box optimization problems remain challenging because of large, weakly structured, and high-dimensional search spaces. Existing methods often suffer from poor sample efficiency because they rely on direct candidate generation or trial-and-error refinement. A natural way to improve search efficiency is to use world modeling, which can help identify promising optimization directions before costly evaluation. Large language models can predict the outcomes of these candidates with nontrivial accuracy because of their implicit knowledge. Motivated by this observation, we propose WMLLM, a self-evolving optimization-agent framework based on predict-then-act world modeling. The agent first predicts promising directions and then acts to generate candidates. Combined with agentic multi-turn refinement, population-based search, and reinforcement learning, WMLLM refines both its implicit world model and its optimization strategy during search. E
EvalDetectBench: A Benchmark for Measuring Evaluation Awareness in Frontier Language Models
arXiv:2609.01611v1 Announce Type: new Abstract: Frontier large language models can often recognize when they are being evaluated, a capability known as evaluation awareness. If models behave differently in evaluations than in deployment, this undermines the validity of evaluation results, which are a crucial component of current AI safety frameworks. We introduce EvalDetectBench, an open pipeline and benchmark for measuring evaluation awareness that works with any Inspect-compatible evaluation, allowing practitioners to test against current and future benchmarks. EvalDetectBench ships with a newly curated transcript suite covering current frontier system-card evaluations and diverse deployment sources. The benchmark serves two purposes: measuring how reliably frontier LLMs recognize that they are being evaluated, and assessing how detectable individual benchmarks are as evaluations. We identify two methodological choices in the existing literature that introduce systematic bias: the id
DiDrive: A Risk-Aware Hierarchical Diffusion Framework for Safe Offline Reinforcement Learning in Autonomous Driving
arXiv:2609.01609v1 Announce Type: new Abstract: While diffusion models effectively capture multimodal behavioral priors for autonomous driving, offline reinforcement learning (RL) policies remain susceptible to distribution shift, heavy-tailed risk signals, out-of-distribution (OOD) action generation, and high-dimensional state redundancy. To address these challenges, we propose DiDrive, a distribution-guided offline diffusion framework featuring two synergistic components: the Risk-Aware Hierarchical Diffusion (RHDif) architecture and the 3DICE policy optimization paradigm. In the state space, RHDif utilizes a low-level risk-gated encoder and a high-level contextual modulator to filter environmental redundancy and focus on safety-critical threats. In the action space, 3DICE mitigates OOD overestimation and gradient oscillation through in-sample calibrated guidance, spatiotemporal optimization, and ensemble-based candidate ranking. Evaluations on the CARLA benchmark demonstrate DiDrive
Why AI Companies Break Your Valuation Model
The revenue looks like software. It does not behave like software. Here is what that does to the number.Continue reading on Medium »