Is hip tightness to blame for your back or knee pain? The cool tattoos are optional. Breakthroughs, discoveries, and DIY tips sent every weekday. Because, even among those of us who exercise regularly, the further we get from childhood the more limited our varieties of movement typically become, leading to weaker muscles, brittler bones and less mobile joints. "We don't move laterally as much anymore, as we get older and we're not playing sports. Even if you're long-distance running, you're just moving in one plane [of motion]," says Patrick Suarez, OCS, SCS, a physical therapist based in Albany, New York.

The four-time world champion shares the gear, fuel, and rituals that will power her through this year's New York City Marathon. 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. Marathon runner Hellen Obiri starts her day like many of us: by checking her phone. First, she looks at the weather, then her schedule, and finally her group chats.

The Atlantic CEO's new book,, examines his complicated relationship with the sport. On this week's episode of, he talks about the ways tech is helping him become a better runner. To most of the world, Nicholas Thompson is known as an editor, an AI enthusiast, or something of a LinkedIn influencer. But the former WIRED editor in chief, who is now CEO of The Atlantic, is often better known to colleagues as . On Tuesday, Thompson is releasing . As the title suggests, it's a book about his commitment to running--Thompson runs a ridiculously fast marathon and holds the American 50K record for the 45-49 age group. Ultimately, though, the book examines the complicated relationship between the sport, Thompson, and his father, who first took him on a run when he was just 5 years old. Tech obsessives, of course, will also get their fix: includes plenty of science-backed training guidance and documents Thompson's experience training with elite Nike coaches. On this week's episode of, I talked to Thompson (who was also my first boss; he hired me as an intern at WIRED in 2008) about his book, the interplay between running and addiction, and what he thinks AI can do for runners for writers. It is a joy to be here with you at Condé Nast at WIRED. I loved coming up those elevators. I love seeing you as the editor in chief. I'm thrilled that you're here. We're going to start this conversation the way we start all of them, which is with a little warmup, some rapid-fire questions. In honor of your new book,, I'm gonna make them entirely running themed. I mean, if your listeners don't wanna hear about running Trail run or track run? Worst running injury you've ever had. The one you wish people would stop talking to you about. You only need to run a 20-miler before a marathon. What do you need to run? Why do people die at mile 20? Because they only train for [marathons] with 20-mile-runs. I generally prefer people, but then you have to schedule it. Backup sport of choice if you could never run again.

DeepResearch agents represent a transformative AI paradigm, conducting expert-level research through sophisticated reasoning and multi-tool integration. However, evaluating these systems remains critically challenging due to open-ended research scenarios and existing benchmarks that focus on isolated capabilities rather than holistic performance. Unlike traditional LLM tasks, DeepResearch systems must synthesize diverse sources, generate insights, and present coherent findings, which are capabilities that resist simple verification. To address this gap, we introduce DeepResearch-ReportEval, a comprehensive framework designed to assess DeepResearch systems through their most representative outputs: research reports. Our approach systematically measures three dimensions: quality, redundancy, and factuality, using an innovative LLM-as-a-Judge methodology achieving strong expert concordance. We contribute a standardized benchmark of 100 curated queries spanning 12 real-world categories, enabling systematic capability comparison. Our evaluation of four leading commercial systems reveals distinct design philosophies and performance trade-offs, establishing foundational insights as DeepResearch evolves from information assistants toward intelligent research partners. Source code and data are available at: https://github.com/HKUDS/DeepResearch-Eval.

-- In this paper, we explore the space of running gaits for the bipedal robot Cassie. Our first contribution is to present an approach for optimizing gait efficiency across a spectrum of speeds with the aim of enabling extremely high-speed running on hardware. This raises the question of how the resulting gaits compare to human running mechanics, which are known to be highly efficient in comparison to quadrupeds. Our second contribution is to conduct this comparison based on established human biomechanical studies. We find that despite morphological differences between Cassie and humans, key properties of the gaits are highly similar across a wide range of speeds. Finally, our third contribution is to integrate the optimized running gaits into a full controller that satisfies the rules of the real-world task of the 100m dash, including starting and stopping from a standing position. We demonstrate this controller on hardware to establish the Guinness World Record for F astest 100m by a Bipedal Robot . I. INTRODUCTION In recent years, reinforcement learning (RL) has proven highly effective for sim-to-real training of bipedal locomotion [1-3].

Modeling voice identity is challenging due to its multifaceted nature. In generative speech systems, identity is often assessed using automatic speaker verification (ASV) embeddings, designed for discrimination rather than characterizing identity. This paper investigates which aspects of a voice are captured in such representations. We find that widely used ASV embeddings focus mainly on static features like timbre and pitch range, while neglecting dynamic elements such as rhythm. We also identify confounding factors that compromise speaker similarity measurements and suggest mitigation strategies. To address these gaps, we propose U3D, a metric that evaluates speakers' dynamic rhythm patterns. This work contributes to the ongoing challenge of assessing speaker identity consistency in the context of ever-better voice cloning systems. We publicly release our code.

If you've ever tried editing a 4K video on a regular computer, you know it can feel like trying to run a marathon through molasses. Lag, stuttering, and slow exports are enough to make you give up halfway through trimming your footage. That's why I started using VideoProc. This software is built to make working with big, high-res video files feel easy, even on machines that aren't top of the line. VideoProc is part video editor, part optimization tool.

We introduce LinearVC, a simple voice conversion method that sheds light on the structure of self-supervised representations. First, we show that simple linear transformations of self-supervised features effectively convert voices. Next, we probe the geometry of the feature space by constraining the set of allowed transformations. We find that just rotating the features is sufficient for high-quality voice conversion. This suggests that content information is embedded in a low-dimensional subspace which can be linearly transformed to produce a target voice. To validate this hypothesis, we finally propose a method that explicitly factorizes content and speaker information using singular value decomposition; the resulting linear projection with a rank of just 100 gives competitive conversion results. Our work has implications for both practical voice conversion and a broader understanding of self-supervised speech representations. Samples and code: https://www.kamperh.com/linearvc/.

Goal-oriented script planning, or the ability to devise coherent sequences of actions toward specific goals, is commonly employed by humans to plan for typical activities. In e-commerce, customers increasingly seek LLM-based assistants to generate scripts and recommend products at each step, thereby facilitating convenient and efficient shopping experiences. However, this capability remains underexplored due to several challenges, including the inability of LLMs to simultaneously conduct script planning and product retrieval, difficulties in matching products caused by semantic discrepancies between planned actions and search queries, and a lack of methods and benchmark data for evaluation. In this paper, we step forward by formally defining the task of E-commerce Script Planning (EcomScript) as three sequential subtasks. We propose a novel framework that enables the scalable generation of product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions. By applying our framework to real-world e-commerce data, we construct the very first large-scale EcomScript dataset, EcomScriptBench, which includes 605,229 scripts sourced from 2.4 million products. Human annotations are then conducted to provide gold labels for a sampled subset, forming an evaluation benchmark. Extensive experiments reveal that current (L)LMs face significant challenges with EcomScript tasks, even after fine-tuning, while injecting product purchase intentions improves their performance.

Strava has had a few new developments recently, including buying personalized running plan app Runna. Now, it has launched a new training feature for distance runners called Performance Predictions, which gives estimated finish times for difference race lengths. The app will now offer possible times for a 5K, 10K, half marathon and marathon -- so, now, it's not only your own goals you have to live up to but Strava's as well. Strava is providing these predictions using a machine learning model (shocking) which looks at over 100 data points from the individual and the performance of similar runners on the app. The times should change after every run and based on rest periods.