You can speak into the Pebble Index to have it remember things or set reminders, timers, and tasks. Pebble is on a roll--happily skipping along a calm lake, if you will. The resurrected smartwatch company recovered its trademarked name a few months ago, shipped all its new Pebble 2 Duo watches, and is about to start shipping the Pebble 2 Time, which alone received more than 25,000 preorders. But the company is already moving on to some new hardware: the Pebble Index 01 . And unlike most other smart rings, the Pebble Index doesn't measure your heart rate or track your sleep.

The ever increasing complexity of the hardware design process demands improved hardware design and verification methodologies. With the advent of generative AI various attempts have been made to automate parts of the design and verification process. Large language models (LLMs) as well as specialized models generate hdl and testbenches for small components, having a few leaf level components. However, there are only a few attempts to automate the entire processor design process. Hardware design demands hierarchical and modular design processes. We utilized this best practice systematically and effectively. We propose agentic automated processor design and verification with engineers in the loop. The agent with optional specification tries to break down the design into sub-components, generate HDL and cocotb tests, and verifies the components involving engineer guidance, especially during debugging and synthesis. We designed various digital systems using this approach. However, we selected two simple processors for demonstration purposes in this work. The first one is a LEGv8 like a simple processor verified, synthesized and programmed for the DE-10 Lite FPGA. The second one is a RISC-V like 32-bit processor designed and verified in similar manner and synthesized. However, it is not programmed into the DE-10 Lite. This process is accomplished usually using around a million inference tokens per processor, using a combination of reasoning (e.g gemini-pro) and non-reasoning models (eg. gpt-5-mini) based on the complexity of the task. This indicates that hardware design and verification experimentation can be done cost effectively without using any specialized hardware. The approach is scalable, we even attempted system-on-chip, which we want to experiment in our future work.

In Figure A1, we show the high-level pseudocode of our port of the PPO algorithm to Spark Streaming. Similar to our port of RLlib to RLlib Flow, we only changed the parts of the PPO algorithm in RLlib that affect distributed execution, keeping the core algorithm implementation (e.g., numerical definition of policy loss and neural networks in TensorFlow) as Figure A1: Example of Spark Streaming for Distributed RL. We conduct comparisons between the performance of both implementations. Experiments here are conducted on A WS m4.10xlarge instances. Looping operations are not well supported.

The Best White-Noise Machines for a Blissful Night's Sleep Help the whole family catch more Z's with soothing background noise from our favorite sound machines. 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. The Best White noise machine isn't a complex device, even as companies constantly add more bells and whistles. Nowadays, they come in all shapes and sizes, outfitted with the capacity to play other noise frequencies and nature sounds while at home or in a more portable, on-the-go form. They're not just for kids or babies anymore--if you're like us, trying to drown out your internal monologue so that you can finally drift off, this is the article for you. But if you're building up your arsenal of sleep gadgets, with a white noise machine among them, we've tried out everything from the best sleep trackers, best sunrise alarm clocks, the best mattresses, and the best extreme alarm clocks .

The Best White-Noise Machines for a Blissful Night's Sleep Help the whole family catch more Z's with soothing background noise from our favorite sound machines. The Best White noise machine isn't a complex device, even as companies constantly add more bells and whistles. Nowadays, they come in all shapes and sizes, outfitted with the capacity to play other noise frequencies and nature sounds while at home or in a more portable, on-the-go form. They're not just for kids or babies anymore--if you're like us, trying to drown out your internal monologue so that you can finally drift off, this is the article for you. But if you're building up your arsenal of sleep gadgets, with a white noise machine among them, we've tried out everything from the best sleep trackers, best sunrise alarm clocks, the best mattresses, and the best extreme alarm clocks . We've got a directory where you can find all of our Sleep content. If you're buying for a child, keep sound machines to no more than 50 decibels and farther than 200 centimeters (6.5 feet) from where your baby sleeps.

Time series forecasting is a foundational task across domains, such as finance, healthcare, and environmental monitoring. While recent advances in Time Series Foundation Models (TSFMs) have demonstrated strong generalisation through large-scale pretraining, existing models operate predominantly in a unimodal setting, ignoring the rich multimodal context, such as visual and textual signals, that often accompanies time series data in real-world scenarios. This paper introduces a novel parameter-efficient multimodal framework, UniCast, that extends TSFMs to jointly leverage time series, vision, and text modalities for enhanced forecasting performance. Our method integrates modality-specific embed-dings from pretrained Vision and Text Encoders with a frozen TSFM via soft prompt tuning, enabling efficient adaptation with minimal parameter updates. This design not only preserves the generalisation strength of the foundation model but also enables effective cross-modal interaction. Extensive experiments across diverse time-series forecasting benchmarks demonstrate that UniCast consistently and significantly outperforms all existing TSFM baselines.

I'm in the midst of putting together a buying guide of indoor vertical gardening systems, and the Gardyn--the 30-plant Home 4.0, to be exact--was the first tester to arrive at my house. I had it unboxed and set up within a couple of hours, lights on and water pump running. Sure enough, within a couple of weeks, all of Gardyn's proprietary seed-filled yCubes had sprouted, and a couple of weeks after that, I was harvesting bowlfuls of herbs and salad greens. Even though from setup to harvest the Gardyn required the use of about five brain cells, I was quite pleased with myself, despite having long ago given up gardening outdoors due to deer, rabbits, and my own incompetence with anything other than starts from the big-box store. What I failed to understand, but would come to grasp with subsequent systems, was that indoor hydroponic gardening is just as hard in some ways as outdoor gardening.

She needs to be spoken to slowly and clearly, as you'd talk to an aged relative with diminished faculties. '"Alexa, how long do wasps live for?" "Alexa, how long do wasps live if you hit them with a tea towel and then a saucepan?" In September 2016, a new presence appears in our house, squatting on the kitchen counter between the kettle and the coffee machine. It is blandly futuristic, a minimal cylinder with an LED ring that glows blue to alert us to the fact that it is ready, poised to answer our questions or carry out our instructions, as long as those instructions are clearly stated and fall within a narrow band of available "skills".

Channel prediction is crucial for high-mobility vehicular networks, as it enables the anticipation of future channel conditions and the proactive adjustment of communication strategies. However, achieving accurate vehicular channel prediction is challenging due to significant Doppler effects and rapid channel variations resulting from high-speed vehicle movement and complex propagation environments. In this paper, we propose a novel delay-Doppler (DD) domain channel prediction framework tailored for high-mobility vehicular networks. By transforming the channel representation into the DD domain, we obtain an intuitive, sparse, and stable depiction that closely aligns with the underlying physical propagation processes, effectively reducing the complex vehicular channel to a set of time-series parameters with enhanced predictability. Furthermore, we leverage the large artificial intelligence (AI) model to predict these DD-domain time-series parameters, capitalizing on their advanced ability to model temporal correlations. The zero-shot capability of the pre-trained large AI model facilitates accurate channel predictions without requiring task-specific training, while subsequent fine-tuning on specific vehicular channel data further improves prediction accuracy. Extensive simulation results demonstrate the effectiveness of our DD-domain channel prediction framework and the superior accuracy of the large AI model in predicting time-series channel parameters, thereby highlighting the potential of our approach for robust vehicular communication systems.