Try One of macOS 27's Best Features Right Now Apple's fall macOS release will let you build Shortcuts by typing what you want to happen. But Claude Code and Codex users don't have to wait. Buried deep inside everything announced at WWDC this year was something I, an Apple Shortcuts enthusiast, can't wait to try: the ability to make Apple Shortcuts using generative artificial intelligence. In macOS 27, you'll be able to just type what you want a shortcut to do, and the app will build it. Anyone who builds shortcuts regularly knows the process of doing so can be tedious, even if the end results save you a lot of time.

Pump.Fun's Bounties Platform Is a Black Hole of Circular Grifting The crypto platform claims you can "pay anyone to do anything," from quitting a job on camera to getting a memecoin-themed tattoo. But it mostly seems like people trying to scam each other. Would you run into a crowded university lecture hall, fart into a megaphone, and bellow "fartcoin" at the top of your lungs? If so--and should you have the means to document this stunt on video, preferably capturing the audience's reaction--you may claim a reward of approximately $1,000 . The money, of course, will be dispensed in fartcoin, a meme cryptocurrency trading at a little over 10 cents at time of publication, with a total market capitalization hovering around $130 million. Such is the promise of Pump.Fun GO, a new feature on Pump.Fun, one of the fastest-growing crypto businesses of the past few years.

When you purchase through links in our articles, we may earn a small commission. I can't stop using this website that lets me drive through cities around the world Drive and Listen combines street-level video with live local radio for a surprisingly immersive experience--even if it's only from your desktop. Drive and Listen might not sound like it, but it's strangely relaxing once you try it. The site provides exactly the experience the name suggests: Pick a city anywhere in the world, press play, and sit back as you ride along through the streets listening to local radio stations. The site was created during the pandemic by a student in Turkey who missed traveling and wanted a way to reconnect with places beyond his own neighborhood.

Large language models (LLMs) have demonstrated exceptional performance in reasoning tasks such as mathematics and coding, matching or surpassing human capabilities. However, these impressive reasoning abilities face significant challenges in specialized domains. Taking Go as an example, although AlphaGo has established the high performance ceiling of AI systems in Go, mainstream LLMs still struggle to reach even beginner-level proficiency, let alone perform natural language reasoning. This performance gap between general-purpose LLMs and domain experts is significantly limiting the application of LLMs on a wider range of domain-specific tasks. In this work, we aim to bridge the divide between LLMs' general reasoning capabilities and expert knowledge in domain-specific tasks. We perform mixed fine-tuning with structured Go expertise and general long Chain-ofThought (CoT) reasoning data as a cold start, followed by reinforcement learning to integrate expert knowledge in Go with general reasoning capabilities. Through this methodology, we present LoGos, a powerful LLM that not only maintains outstanding general reasoning abilities, but also conducts Go gameplay in natural language, demonstrating effective strategic reasoning and accurate next-move prediction. LoGos achieves performance comparable to human professional players, substantially surpassing all existing LLMs. Through this work, we aim to contribute insights on applying general LLM reasoning capabilities to specialized domains.

Tactile sensing is crucial for achieving human-level robotic capabilities in ma-nipulation tasks [ 54].

Recently, large vision-language models (LVLMs) have rapidly gained popularity for their strong generation and reasoning capabilities given diverse multimodal inputs. However, these models incur significant computational and memory overhead during inference, which greatly hinders the efficient deployment in practical scenarios. The extensive key-value (KV) cache, necessitated by the lengthy input and output sequences, notably contributes to the high inference cost. Based on this, recent works have investigated ways to reduce the KV cache size for higher efficiency. Although effective, they generally overlook the distinct importance distributions of KV vectors across layers and maintain the same cache size for each layer during the next token prediction.

While recent multimodal large language models (MLLMs) have advanced automated ECG interpretation, they still face two key limitations: (1) insufficient multimodal synergy between ECG time series and ECG images, and (2) limited explainability in linking diagnoses to granular waveform evidence. We introduce GEM, the first MLLM unifying ECG time series, 12-lead ECG images and text for grounded and clinician-aligned ECG interpretation. GEM enables feature-grounded analysis, evidence-driven reasoning, and a clinician-like diagnostic process through three core innovations: a dual-encoder framework extracting complementary time series and image features, cross-modal alignment for effective multimodal understanding, and knowledge-guided instruction data generation for generating high-granularity grounding data (ECG-Grounding) linking diagnoses to measurable parameters (e.g., QRS/PR Intervals). Additionally, we propose the Grounded ECGUnderstanding task, a clinically motivated benchmark designed to comprehensively assess the MLLM's capability in grounded ECG understanding. Experimental results on both existing and our proposed benchmarks show GEM significantly improves predictive performance (CSN 7.4%), explainability (22.7%), and grounding (25.3%), making it a promising approach for real-world clinical applications.

The efficient deployment of large language models (LLMs) in online settings requires optimizing inference performance under stringent latency constraints, particularly the time-to-first-token (TTFT) and time-per-output-token (TPOT). This paper focuses on the query scheduling problem for LLM inference with prefix reuse, a technique that leverages shared prefixes across queries to reduce computational overhead. Our work reveals previously unknown limitations of the existing first-come-first-serve (FCFS) and longest-prefix-match (LPM) scheduling strategies with respect to satisfying latency constraints. We present a formal theoretical framework for LLM query scheduling under RadixAttention, a prefix reuse mechanism that stores and reuses intermediate representations in a radix tree structure. Our analysis establishes the NP-hardness of the scheduling problem with prefix reuse under TTFT constraints and proposes a novel scheduling algorithm, k-LPM, which generalizes existing methods by balancing prefix reuse and fairness in query processing. Theoretical guarantees demonstrate that k-LPM achieves improved TTFT performance under realistic traffic patterns captured by a data generative model. Empirical evaluations in a realistic serving setting validates our findings, showing significant reductions in P99 TTFT compared to baseline methods.

Find the right wearable for your lifestyle, workouts, and goals. Like every piece of gear you wear on your body day in and day out, fitness trackers are incredibly personal. The right tracker for you should be comfortable, accurate, and tailored to your lifestyle, including your preferred workouts and health goals. Do you bike, row, or strength train? Do you run on trails for hours at a time, or do you just want a reminder to stand up every hour? Do you want to wear it on your wrist or your finger, or tuck it into your sports bra? No matter what your needs are, there's never been a better time to find a powerful, sophisticated tool to help optimize your workouts or jump-start your routine. We test dozens of fitness trackers every year while running, climbing, hiking, or just doing workout videos on our iPads at night, to bring you these picks. For more wearables, check out our guides to the Best Smartwatches, Best Smart Rings, and Best Sleep Trackers . Garmin makes some of the most accurate fitness trackers on the market, and the Vivoactive 6 is the best midrange option for most people.

Follow this section to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW? Smart Alerts: Get notified about major news as it happens. Follow this tag to personalize your feed and get instant alerts. Follow Go to your personalized feed WHY FOLLOW?