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Quick meal prep tips for weekdays include starting small by planning just a few meals to build confidence and make the habit sustainable^[2]. Consider the balance of food groups to ensure a nutritious meal plan, incorporating whole foods and keeping a well-stocked pantry to streamline the process^[2][3].

Invest in quality storage containers for easy meal organization and portion control, and pre-portion meals into individual containers to save time^[2][4]. Batch cooking specific ingredients allows for versatile meals throughout the week, while using the freezer for prepared dishes can extend their freshness^[2][6]. Remember to keep your pantry organized to reduce stress during meal prep^[4].

Whales breathe air through blowholes located at the top of their heads, which allows them to take quick breaths while swimming or resting near the surface. Unlike fish, they cannot breathe underwater and must rise to the surface to exhale and inhale air efficiently. Their blowholes seal tightly due to strong muscles, preventing water from entering their lungs when submerged^[1][5].

Whales can consciously control their breathing and heart rate, which is crucial during deep dives. They efficiently exchange gases in their lungs, allowing them to hold their breath longer than many land mammals^[4][6]. This combination of adaptations enables whales to thrive in their aquatic environment^[2][3][4].

The exclusive contracts referred to in the trial are known as 'Mobile Application Distribution Agreements' (MADAs) and involve revenue share agreements for pre-load exclusivity on devices. Google pays for these exclusive defaults on a device-by-device basis, ensuring it is the only preinstalled search engine, which restricts competition from rivals^[2][6][3].

These contracts create significant barriers for competitors, as once signed, they lock up traffic and limit rivals' ability to attract queries. Without exclusivity, carriers could ship alternatives to Google Search, affecting rivals' investments and competitive viability^[5][4][7].

Quantization helps deployment by reducing the memory footprint of models, enabling them to be run on hardware with lower resource requirements. In the gpt-oss models, quantization of the Mixture-of-Experts (MoE) weights to MXFP4 format allows the larger model to fit on a single 80GB GPU and the smaller model to operate on systems with as little as 16GB memory^[1].

This reduction in memory requirements is critical for efficient deployment, particularly in environments with limited computational resources, making powerful models more accessible while maintaining performance^[1].

Compositionality in AI refers to the ability to generate and produce novel combinations from known components, which is essential for systematic generalization. It is a fundamental principle in the design of traditional, logic-based systems. Many statistical methods have struggled with compositional generalization, while recent advancements aim to improve this ability in deep learning architectures by incorporating analytical components that reflect the compositional structure of a domain, such as structure-processing neural networks or metalearning for compositional generalization. Despite these efforts, achieving predictable and systematic generalization in AI remains a challenge, as most results are empirical and not reliably predictable^[1].