We drank adaptogen drinks for weeks, and taste-tested with a trained sommelier. 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 adaptogen drinks promise not just to wake you up in the morning, but offer focus and clarity and maybe even a warm wash of well-being. A different drink might tuck you gently in at night, or sub in for alcohol as a mindful party drink. I've spent months trying some of the most popular functional drinks on the market, bedding down with kava or tryptophan-laced xicha morada, and waking up with caffeine and L-theanine. Many of the new school of nootropic and functional drinks are like kissing cousins of mushroom coffee, except in refreshing soda form. Functional sodas might be chockablock with mushroom adaptogens such as reishi and cordyceps, alongside traditional home anxiety remedies such as ashwagandha or L-theanine. I both logged the effects of each soda, and held a large taste test with Portland, Oregon, sommelier Sami Gaston, owner of an excellent wine bar and shop called Bar Diane and Negociant, respectively--to determine how happy you'd be to drink them even if they didn't help you focus better on endless spreadsheets or the hunt for a job. Also check out WIRED's guide to mushroom gummies, or take your wellness in powdered form with the best greens powders and the best protein powders .

The power of flowers and fungi is no match for these insects. Breakthroughs, discoveries, and DIY tips sent every weekday. It can turn ants into "zombies," help fictional plumbers grow, and even look like creepy fingers . One newly engineered strain of fungus uses the power of smell to kill Earth's deadliest animal --mosquitoes. Mosquito-borne diseases, including malaria and dengue, kill thousands of people per year.

Mario's super-sized mushroom exists in real life While they actually power-up trees and not plumbers, the 40 year-old video game helped make toadstools mainstream. Mario's expansive world is modeled after the real-life mushroom'Amanita muscaria.' We may earn revenue from the products available on this page and participate in affiliate programs. Nintendo's is undisputedly one the most iconic and successful video games ever made, with more than 58 million copies sold worldwide. Even if you've never played the original game or any of the hundreds of titles that span the expansive Mario Universe, you've undoubtedly seen Mario or his brother Luigi with their matching hats, dungarees, and mustaches, jumping up and breaking bricks to uncover fire flower or super mushroom power-ups along the way.

From the earliest years of our lives, humans use language to express our beliefs and desires. Being able to talk to artificial agents about our preferences would thus fulfill a central goal of value alignment. Y et today, we lack computational models explaining such language use.

Large Language Models (LLMs) falter in multi-step interactions -- often hallucinating, repeating actions, or misinterpreting user corrections -- due to reliance on linear, unstructured context. This fragility stems from the lack of persistent memory to track evolving goals and task dependencies, undermining trust in autonomous agents. We introduce the Task Memory Engine (TME), a modular memory controller that transforms existing LLMs into robust, revision-aware agents without fine-tuning. TME implements a spatial memory framework that replaces flat context with graph-based structures to support consistent, multi-turn reasoning. Departing from linear concatenation and ReAct-style prompting, TME builds a dynamic task graph -- either a tree or directed acyclic graph (DAG) -- to map user inputs to subtasks, align them with prior context, and enable dependency-tracked revisions. Its Task Representation and Intent Management (TRIM) component models task semantics and user intent to ensure accurate interpretation. Across four multi-turn scenarios-trip planning, cooking, meeting scheduling, and shopping cart editing -- TME eliminates 100% of hallucinations and misinterpretations in three tasks, and reduces hallucinations by 66.7% and misinterpretations by 83.3% across 27 user turns, outperforming ReAct. TME's modular design supports plug-and-play deployment and domain-specific customization, adaptable to both personal assistants and enterprise automation. We release TME's codebase, benchmarks, and components as open-source resources, enabling researchers to develop reliable LLM agents. TME's scalable architecture addresses a critical gap in agent performance across complex, interactive settings.

While recent research increasingly emphasizes the value of human-LLM collaboration in competitive programming and proposes numerous empirical methods, a comprehensive understanding remains elusive due to the fragmented nature of existing studies and their use of diverse, application-specific human feedback. Thus, our work serves a three-fold purpose: First, we present the first taxonomy of human feedback consolidating the entire programming process, which promotes fine-grained evaluation. Second, we introduce ELABORATIONSET, a novel programming dataset specifically designed for human-LLM collaboration, meticulously annotated to enable large-scale simulated human feedback and facilitate costeffective real human interaction studies. Third, we introduce ELABORATION, a novel benchmark to facilitate a thorough assessment of human-LLM competitive programming. With ELABORATION, we pinpoint strengthes and weaknesses of existing methods, thereby setting the foundation for future improvement. Our code and dataset are available at https://github.com/SCUNLP/ELABORATION

Food systems are responsible for a third of human-caused greenhouse gas emissions. We investigate what Large Language Models (LLMs) can contribute to reducing the environmental impacts of food production. We define a typology of design and prediction tasks based on the sustainable food literature and collaboration with domain experts, and evaluate six LLMs on four tasks in our typology. For example, for a sustainable protein design task, food science experts estimated that collaboration with an LLM can reduce time spent by 45% on average, compared to 22% for collaboration with another expert human food scientist. However, for a sustainable menu design task, LLMs produce suboptimal solutions when instructed to consider both human satisfaction and climate impacts. We propose a general framework for integrating LLMs with combinatorial optimization to improve reasoning capabilities. Our approach decreases emissions of food choices by 79% in a hypothetical restaurant while maintaining participants' satisfaction with their set of choices. Our results demonstrate LLMs' potential, supported by optimization techniques, to accelerate sustainable food development and adoption.