These plant-based meal kits and delivery services bring healthy preprepared meals and meal kits to your door. Plant-Based meal kit services are a modern miracle for vegetarians and vegans, who usually aren't afforded the same conveniences as meat eaters or those without dietary restrictions. We at WIRED love meal kits, because they're all about modern convenience--you can eat what you want, even if you're on a specialty diet or have strong food preferences, without ever leaving your house. Gone are the days of grocery shopping and scouring online for recipes; these contemporary plant-based meal kit services do the heavy lifting for you using curated menus and algorithms, with choices for both premade microwavable meals and kits where you do the cooking yourself. Some plant-based meal kit services, like Hungryroot, use AI customization to curate menus based on your specific tastes. Others, like Daily Harvest, have a set selection of choices so you can always keep your freezer stocked with plant-based, gluten-free meals to have on hand. I'm vegan, so I know how difficult it can be to find new recipes that will actually taste good without breaking the bank. Plus, plant-based meal kits are a great way to try out new foods and recipes, especially if you're looking to switch to a healthier diet in the new year.

These vegan meal kits and delivery services bring preprepared meals and meal kits to your door. 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. Vegan-specific meal kit services are a modern miracle for vegans, who usually aren't afforded the same conveniences as meat eaters or those without dietary restrictions. We at WIRED love meal kits, because they're all about modern convenience--you can eat what you want, even if you're on a specialty diet or have strong food preferences, without ever leaving your house. Gone are the days of grocery shopping and scouring online for recipes; these contemporary vegan meal kit services do the heavy lifting for you using curated menus and algorithms, with choices for both premade microwavable meals and kits where you do the cooking yourself. Some vegan meal kit services, like Hungryroot, use AI customization to curate menus based on your specific tastes. Others, like Daily Harvest, have a set selection of choices so you can always keep your freezer stocked with vegan, gluten-free meals to have on hand.

Maintaining a balanced diet is essential for overall health, yet many individuals struggle with meal planning due to nutritional complexity, time constraints, and lack of dietary knowledge. Personalized food recommendations can help address these challenges by tailoring meal plans to individual preferences, habits, and dietary restrictions. However, existing dietary recommendation systems often lack adaptability, fail to consider real-world constraints such as food ingredient availability, and require extensive user input, making them impractical for sustainable and scalable daily use. To address these limitations, we introduce NutriGen, a framework based on large language models (LLM) designed to generate personalized meal plans that align with user-defined dietary preferences and constraints. By building a personalized nutrition database and leveraging prompt engineering, our approach enables LLMs to incorporate reliable nutritional references like the USDA nutrition database while maintaining flexibility and ease-of-use. We demonstrate that LLMs have strong potential in generating accurate and user-friendly food recommendations, addressing key limitations in existing dietary recommendation systems by providing structured, practical, and scalable meal plans. Our evaluation shows that Llama 3.1 8B and GPT-3.5 Turbo achieve the lowest percentage errors of 1.55\% and 3.68\%, respectively, producing meal plans that closely align with user-defined caloric targets while minimizing deviation and improving precision. Additionally, we compared the performance of DeepSeek V3 against several established models to evaluate its potential in personalized nutrition planning.

Given their ability for advanced reasoning, extensive contextual understanding, and robust question-answering abilities, large language models have become prominent in healthcare management research. Despite adeptly handling a broad spectrum of healthcare inquiries, these models face significant challenges in delivering accurate and practical advice for chronic conditions such as diabetes. We evaluate the responses of ChatGPT versions 3.5 and 4 to diabetes patient queries, assessing their depth of medical knowledge and their capacity to deliver personalized, context-specific advice for diabetes self-management. Our findings reveal discrepancies in accuracy and embedded biases, emphasizing the models' limitations in providing tailored advice unless activated by sophisticated prompting techniques. Additionally, we observe that both models often provide advice without seeking necessary clarification, a practice that can result in potentially dangerous advice. This underscores the limited practical effectiveness of these models without human oversight in clinical settings. To address these issues, we propose a commonsense evaluation layer for prompt evaluation and incorporating disease-specific external memory using an advanced Retrieval Augmented Generation technique. This approach aims to improve information quality and reduce misinformation risks, contributing to more reliable AI applications in healthcare settings. Our findings seek to influence the future direction of AI in healthcare, enhancing both the scope and quality of its integration.

In fact, according background in automated recommendations of personalized to a recent meta-survey (Leme et al. 2021), almost meals and then discuss our problem formulation, key solution 40% of the population across high and low-and mediumincome components including data (recipe representation and countries do not adhere to their national food-based format conversion) and meal recommendation, and their dietary guidelines, often prioritizing convenience over nutrition evaluation. We then describe a prototype implementation of needs. Previous studies have shown that adhering the solution in the BEACON system along with the supported to a provided meal plan instead of a self-selected one reduces use cases and conclude with a discussion of practical the risk for adverse health conditions (Metz et al. considerations and avenues for future extensions.

This study explores the effectiveness of Large Language Models in meal planning, focusing on their ability to identify and decompose compound ingredients. We evaluated three models-GPT-4o, Llama-3 (70b), and Mixtral (8x7b)-to assess their proficiency in recognizing and breaking down complex ingredient combinations. Preliminary results indicate that while Llama-3 (70b) and GPT-4o excels in accurate decomposition, all models encounter difficulties with identifying essential elements like seasonings and oils. Despite strong overall performance, variations in accuracy and completeness were observed across models. These findings underscore LLMs' potential to enhance personalized nutrition but highlight the need for further refinement in ingredient decomposition. Future research should address these limitations to improve nutritional recommendations and health outcomes.

Background: The integration of artificial intelligence (AI) into daily life, particularly through chatbots utilizing natural language processing (NLP), presents both revolutionary potential and unique challenges. This intended to investigate how different input forms impact ChatGPT, a leading language model by OpenAI, performance in understanding and executing complex, multi-intention tasks. Design: Employing a case study methodology supplemented by discourse analysis, the research analyzes ChatGPT's responses to inputs varying from natural language to pseudo-code engineering. The study specifically examines the model's proficiency across four categories: understanding of intentions, interpretability, completeness, and creativity. Setting and Participants: As a theoretical exploration of AI interaction, this study focuses on the analysis of structured and unstructured inputs processed by ChatGPT, without direct human participants. Data collection and analysis: The research utilizes synthetic case scenarios, including the organization of a "weekly meal plan" and a "shopping list," to assess ChatGPT's response to prompts in both natural language and pseudo-code engineering. The analysis is grounded in the identification of patterns, contradictions, and unique response elements across different input formats. Results: Findings reveal that pseudo-code engineering inputs significantly enhance the clarity and determinism of ChatGPT's responses, reducing ambiguity inherent in natural language. Enhanced natural language, structured through prompt engineering techniques, similarly improves the model's interpretability and creativity. Conclusions: The study underscores the potential of pseudo-code engineering in refining human-AI interaction and achieving more deterministic, concise, and direct outcomes, advocating for its broader application across disciplines requiring precise AI responses.

Artificial intelligence is becoming an ordinary part of human lives in the 21st century. People already got used to the AI features on their smartphones and the artificial voice assistants in their homes. This leads us to ask the question – what's next? AI is changing the way we live by finding its way into the most personal aspects of our lives. One of the best examples of how close AI actually got to humans is its implementation in the food industry. After all, what better way is there to get more involved in personal lives than through the food we consume every day?

In the first days after a fleet of 25 delivery robots descended on George Mason University's campus in January, school officials could only speculate about the machines' long-term impact. The Igloo cooler-sized robots from the Bay Area start-up Starship Technologies -- which were designed to deliver food on demand across campus -- appeared to elicit curious glances and numerous photos, but not much else. It was clear, officials said at the time, that more time and more data would be necessary to understand whether the robots would actually change the campus culture or become a forgettable novelty. Today, some of that data emerged for the first time. In the two months since the robots arrived at the Fairfax, Va.-based school, an extra 1,500 breakfast orders have been delivered autonomously, according to Starship Technologies and Sodexo, a company that manages food services for GMU on contract and works closely with the robots.

There are over 13M men in the US who struggle with their weight, and this demographic is currently being underserved by the majority of health and weight loss programs. Lyle is the app that fills this gap through its AI-powered service that helps men reach their health and weight loss goals. Lyle's proprietary technology simulates conversations with a real dietitian to introduce accountability as well as offer personalized meal plans based on the user's goals and health needs. With a few clicks and integrations, the app enables users to conveniently order groceries for the week through third-party vendors like Shipt and Instacart to ensure they are hitting their dietary goals. AlleyWatch sat down with Philip Kasumu about how his personal experience in the fitness and bodybuilding realm inspired him to create an app to promote health and wellness tailored to each man.