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Video title: Shitposting Compilation V129 Content creator ( Youtuber ) : AlexNoreal Official YT video:

Nick movie: Aprender a soltar Time: 1 november 2024 Netflix / Amazon / HBO: Netflix Duration of the movie: 1h 50m Trailer:

https://www.gadgets360.com/mobiles/news/iqoo-neo-10-pro-launch-specifications-expected-features-6994374?_gl=1*du7jin*_ga*SUZwMEg5LU54cDkySm9QZHBJYVRxRzlQcGE2S3FEaWJYNDVMTnlOaFpCNTA1TWZ4ekQxeWplXzZEVktqTVJjcw..*_ga_XQCGTLW8NV*MTczMTM0MTUyNy4xLjEuMTczMTM0MTUyNy4wLjAuMA.. iQOO Neo 10 series will be unveiled in China soon. Details about the upcoming lineup have been circulating in the rumour mill over the past few weeks, and now a senior company executive has confirmed its imminent launch, though the exact date remains unannounced. The series is expected to include a base iQOO Neo 10 and an iQOO Neo 10 Pro. The lineup will succeed the iQOO Neo 9 and iQOO Neo 9 Pro, which were introduced in China in December 2023. iQOO is also set to unveil the iQOO 13 next month in India. iQOO Neo 10 Series Launch The iQOO Neo 10 series was officially confirmed in a Weibo post by iQOO Neo Product Manager. Apart from the moniker, no other details were revealed. A recent leak suggested that the iQOO Neo 10 series may arrive in China in November. Since we are almost mid-way through the month, we could see the launch take place towards the end. A formal launch date announcement will likely come soon. The leak added that the base iQOO Neo 10 could get a Snapdragon 8 Gen 3 SoC, while the Pro variant may come with a MediaTek Dimensity 9400 chipset. The phones are expected to support 100W wired fast charging. They may get 6,000mAh batteries and 1.5K flat display with narrow bezels. Other rumours have claimed that the iQOO Neo 10 series handsets could get metal middle frame, which are considerable upgrades over the plastic frame on the iQOO Neo 9 series. The base iQOO Neo 9 carries a Snapdragon 8 Gen 2 SoC, while the iQOO Neo 9 Pro has a MediaTek Dimensity 9300 chipset. The handsets are backed by 5,160mAh batteries each with support for 20W wired fast charging. The phones have 6.78-inch AMOLED displays and 50-megapixel dual rear camera units.

https://www.tomshardware.com/video-games/playstation/pizza-huts-new-pizza-warmer-uses-the-playstation-5s-heat-to-keep-your-pizza-hot-you-can-3d-print-the-new-pizzawarmr-for-free Pizza Hut has melded the disparate worlds of bready, cheesy foodstuffs and console gaming with the new PIZZAWRMR. This innovation is designed to sit atop your Sony PlayStation 5 console and keep your takeaway of choice piping hot while you enjoy your heated gaming session. This isn't a new retail product or a giveaway, though. Pizza Hut Canada has made the 3D printing source files free for anyone who signs up to download, modify, and print. Pizza Hut has melded the disparate worlds of bready, cheesy foodstuffs and console gaming with the new PIZZAWRMR. This innovation is designed to sit atop your Sony PlayStation 5 console and keep your takeaway of choice piping hot while you enjoy your heated gaming session. This isn't a new retail product or a giveaway, though. Pizza Hut Canada has made the 3D printing source files free for anyone who signs up to download, modify, and print. The PIZZAWRMR design is inspired by the pizza-centric restaurant's red roof. The lid opens laptop-style for convenient pizza slice access. According to Pizza Hut, several slices of pizza can fit into the top box. Diagrams show that the hot exhaust from the console is channeled under and into the pizza area, which is the appliance of "science and engineering for the greater good," says the Pizza Hut marketing team. The archive includes STL files and a PDF guide, which are included in the Pizza Hut Canada download. You will find 3D printer files for the body, left stand, lid, manifold, and suitable stand. According to the PDF guide, the design, as provided, is "specifically engineered to be compatible only with the console that has rear ventilation measuring 11.7 x 1.31 inches." Your 3D printer should have a bed at least 15 x 15 inches to accommodate the pizza, erm, PIZZAWRMR. Further user measures are needed to protect your expensive console from the real and present danger of crumbs and grease. Pizza Hut suggests PIZZAWRMR users insert a 34 x 23 x 2.5cm foil tray inside the warmer. We don't know why Pizza Hut switched to the metric system for foil trays—perhaps it's a Canadian thing. According to the fast food firm, the last piece of advice in the PDF is to start gaming to warm up the PIZZAWRMR and then place your pizza slices in the tray. Pizza Hut's medium pizza slices will fit best. Fast food firms developing side-projects to appeal to gamers isn't exactly a new marketing strategy. In 2020, KFC famously presented the bucket-shaped KFConsole. The idea started as a joke but snowballed into a fully-fledged Intel NUC-powered console with a fried chicken storage drawer.

https://www.infoq.com/articles/efficient-resource-management-small-language-models/ Challenges in Resource-Constrained Edge Environments Edge computing devices like IoT sensors and smart gadgets often have limited hardware capabilities: Limited Processing Power: Many are powered by low-end CPUs or microcontrollers, which struggle to perform computationally heavy tasks. Restricted Memory: With minimal RAM - storing "large" AI models? Not happening. Energy Efficiency: Battery-powered IoT devices require efficient energy management to ensure long-lasting operation without frequent recharging or battery replacements. Network Bandwidth Constraints: Many rely on intermittent or low-bandwidth network connections, making continuous chat with cloud servers inefficient or impractical. Most AI models are just too big and power-hungry for these devices. That’s where SLMs come in. How Small Language Models (SLMs) Optimize Resource Efficiency Lightweight Architecture SLMs are like the slimmed-down, lean version of massive models like GPT-3 or GPT-4. With fewer parameters (DistilBERT, for example, has 40% less baggage than BERT), they’re small enough to squeeze into memory-constrained devices without breaking a sweat, all while retaining most of their performance magic. Compression Magic Techniques like quantization (think reducing weights to lower-precision integers - reduces computational load) and pruning (cutting off the dead weight) make them faster and lighter. The result? Speedy inference times and reduced power drain, even on devices with the computational muscle of a flip phone. Quantization In cases where quantization is applied, the memory footprint is dramatically reduced. For instance, a quantized version of Mistral 7B may consume as little as 1.5GB of memory while generating tokens at a rate of 240 tokens per second on powerful hardware like the NVIDIA RTX 6000 (Enterprise Technology News and Analysis). This makes it feasible for edge devices and real-time applications that require low-latency processing. Note: Studies on LLaMA3 and Mistral show that quantized models can still perform well in NLP and vision tasks, but the precision used for quantization must be carefully selected to avoid performance degradation. For instance, LLaMA3, when quantized to 2-4 bits, shows notable performance gaps in tasks requiring long-context understanding or detailed language modeling [Papers with Code], but it excels in more straightforward tasks like question answering and basic dialogue systems [Hugging Face]. Basically, there is no well-defined decision tree on how to do perfect quantization, it requires experimenting with specific use case data. Pruning works by identifying and removing unnecessary or redundant parameters in a model - essentially trimming neurons or connections that don't significantly contribute to the final output. This reduces the model size without major performance loss. In fact, research has shown that pruning (Neural Magic - Software-Delivered AI) can reduce model sizes by up to 90% while retaining over 95% of the original accuracy in models like BERT (Deepgram). Pruning methods range from unstructured pruning, which removes individual weights, to structured pruning, which eliminates entire neurons or layers. Structured pruning, in particular, is useful for improving both model efficiency and computational speed, as seen with Google's BERT-Large, where 90% of the network can be pruned with minimal accuracy loss (Neural Magic - Software-Delivered AI). Pruned models, like their quantized counterparts, offer improved speed and energy efficiency. For example, PruneBERT achieved a 97% reduction in weights while still retaining around 93% of its original accuracy, significantly speeding up inference times (Neural Magic - Software-Delivered AI). Similar to quantization, pruning requires careful tuning to avoid removing essential components of the model, particularly in complex tasks like natural language processing. Pattern Adapters Small Language Models (SLMs) are efficient because they can recognize patterns and avoid unnecessary recalculations, much like a smart thermostat learning your routine and adjusting the temperature without constantly checking with the cloud. This approach, known as adaptive inference, reduces computation, saving energy for more critical tasks and extending battery life. Google Edge TPU: Google's Edge TPU enables AI models to perform essential inferences locally, eliminating the need for frequent cloud communication. By applying pruning and sparsity techniques, Google has demonstrated that models running on the Edge TPU can achieve significant reductions in energy consumption and processing time while maintaining high levels of accuracy (Deepgram). For example, in image recognition tasks, the TPU focuses on key features and skips redundant processing, leading to faster, more energy-efficient performance. Apple’s Neural Engine: Apple uses adaptive learning models on devices like iPhones to minimize computation and optimize tasks like facial recognition. This approach reduces both power consumption and cloud communication. Dynamic Neural Networks: Research on dynamic networks shows up to 50% reduction in energy usage through selective activation of model layers based on input complexity. (Source: "Dynamic Neural Networks: A Survey" (2021)) TinyML Benchmarks: The MLPerf Tiny Benchmark highlights how power-aware models can use techniques like pattern reuse and adaptive processing to significantly reduce the energy footprint of AI models on microcontrollers (ar5iv). Models can leverage previously computed results, avoiding recalculation of redundant data and extending battery life on devices such as smart security cameras or wearable health monitors. IoT Applications: A prime example of pattern adaptation is found in the Nest Thermostat, which learns user behaviors and adjusts temperature settings locally. By minimizing cloud interaction, it optimizes energy use without sacrificing responsiveness. SLMs can also adaptively adjust their learning rate based on the frequency of user interactions, further optimizing their power consumption. This local learning ability makes them ideal for smart home and industrial IoT devices that require constant adaptation to changing environments without the energy cost of continuous cloud access.

Todas is the birthday of My friend @El Máster EdwinEdwin feliz cumpleaños menor que la pases bien y que tus sueños se cumplan, suéltalo mmgv yayayu

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