Best AI tools for< Find Recipe >

OH, a potato! is a meal planning app that helps you find, save, and plan recipes using ingredients you already have. It also allows you to create a shared grocery list with others and keep track of what's in your fridge. The app is powered by Chat GPT, which makes it easy to find recipes that fit your dietary needs and preferences.

AiPhoto.recipes is a web application that helps users create healthy meals using the ingredients they have on hand. Users simply take a photo of their ingredients and the app will provide them with three high-protein recipes that they can prepare. The app is integrated with Telegram, so users can access it without having to download any additional software. AiPhoto.recipes is a great tool for busy people who want to eat healthy meals without having to spend a lot of time planning and shopping.

LittleCook is a web and mobile application that helps users reduce food waste and save money by providing recipes based on the ingredients they already have on hand. The app also allows users to track their food inventory, plan their meals, and learn about nutrition. LittleCook is a valuable tool for anyone who wants to cook more efficiently and sustainably.

MealsAI is an AI-powered recipe generator that helps users create delicious and unique meals with just a few clicks. With MealsAI, users can specify their dietary restrictions, ingredients on hand, and desired cooking time, and the AI will generate a personalized recipe that meets their needs. MealsAI also offers a variety of pre-made recipes that users can browse and share. Whether you're a beginner cook or a seasoned chef, MealsAI can help you create amazing meals that everyone will enjoy.

Reel2Recipe is an AI-powered tool that helps you create recipes from videos. Simply upload a video of your favorite dish, and Reel2Recipe will generate a step-by-step recipe for you. You can also search for recipes by ingredient, cuisine, or dietary restriction.

Crumb is an AI food generator application that helps users create unique and delicious recipes by transforming their available ingredients. Users can simply dictate their ingredients to the AI tool, which then generates recipes to inspire everyday cooking and reduce food waste. With a variety of recipe ideas and tips available on the blog, Crumb aims to make cooking more creative and convenient for users.

CookMe is an AI-powered cooking assistant designed to help users with meal planning, recipe suggestions, and cooking instructions. The application utilizes advanced algorithms to provide personalized recommendations based on user preferences and dietary restrictions. With a user-friendly interface, CookMe aims to simplify the cooking process for both beginners and experienced chefs, offering a wide range of recipes and cooking tips. Whether you're looking for quick meal ideas or gourmet recipes, CookMe has you covered.

FoodAI.app is an AI-powered application that helps users generate cooking recipes based on the ingredients they have. Users can select the ingredients they want to use, and the AI will provide them with recipes using those ingredients. The application offers options to filter results based on dietary preferences, regions, and additional ingredients. With a user-friendly interface, FoodAI.app aims to simplify the cooking process and inspire creativity in the kitchen.

ScrappyChef is an AI-powered application that helps users turn leftovers into creative meals. The platform offers a variety of recipes and meal ideas based on the ingredients available to the user. By utilizing artificial intelligence, ScrappyChef provides personalized suggestions and recommendations to make cooking more efficient and enjoyable. Users can subscribe to access more recipes and take advantage of the platform's innovative features.

Infinite Meals is a web application that provides users with a new meal idea every day. It is powered by GPT-3.5-Turbo-1106 from Open AI. The application is designed to help users find new and exciting recipes to cook. It offers a variety of features, including the ability to search for recipes by category, ingredient, or cuisine. Users can also save their favorite recipes and create meal plans.

MyMealPlan is an AI-powered meal planning app that helps users create personalized meal plans based on their dietary preferences, allergies, and restrictions. The app offers a variety of recipes from celebrity chefs and curates a list of the best recipes from the web. MyMealPlan also provides users with a grocery list to make shopping for meals easier.

Grocer AI is a grocery shopping assistant that uses artificial intelligence to help users find recipes, create shopping lists, and set dietary goals. The app is easy to use and can be accessed via text message or through the website. Grocer AI is a valuable tool for anyone who wants to save time and money on their grocery shopping. Grocer AI offers a variety of features that make it a great choice for grocery shoppers. These features include: * Easy text interface: Grocer AI can be used via text message, making it easy to use for everyone, everywhere, everytime. * Usable for everyone: Grocer AI is designed to be used by people of all ages and abilities. * No more researching recipes from the grocery store: Grocer AI provides users with a variety of recipes to choose from, eliminating the need to research recipes from the grocery store. Grocer AI also offers a number of advantages over other grocery shopping apps. These advantages include: * Time-saving: Grocer AI can save users time by helping them find recipes and create shopping lists. * Money-saving: Grocer AI can help users save money by providing them with coupons and discounts. * Healthy eating: Grocer AI can help users eat healthier by providing them with recipes that meet their dietary goals. While Grocer AI is a great tool for grocery shoppers, it does have some disadvantages. These disadvantages include: * Limited selection of recipes: Grocer AI does not offer a wide variety of recipes to choose from. * Not all stores are supported: Grocer AI does not support all grocery stores. * Can be buggy: Grocer AI can sometimes be buggy, which can be frustrating for users. Overall, Grocer AI is a valuable tool for grocery shoppers. The app is easy to use, offers a variety of features, and can save users time and money. However, Grocer AI does have some disadvantages that users should be aware of before using the app. Here are some frequently asked questions about Grocer AI: * Q: How much does Grocer AI cost? * A: Grocer AI is free to use. * Q: What stores does Grocer AI support? * A: Grocer AI supports a variety of grocery stores, including Walmart, Kroger, and Target. * Q: How do I use Grocer AI? * A: To use Grocer AI, simply text (877) 591-5230 to get started.

AI Meal Planner is a personalized AI-powered meal planning tool that generates customized meal plans based on your dietary needs and preferences. It provides dynamic meal suggestions, interactive recipes, and convenient grocery lists, all tailored to your taste, health goals, and seasonality.

Prospre is a meal planning app that helps users create personalized meal plans based on their calorie and macro goals. The app also includes a macro tracker, a recipe database, and a grocery list generator. Prospre is designed to make it easy for users to eat healthy and reach their fitness goals.

HeyPat.AI is an AI-powered chat application that provides instant answers on news, sports scores, recipes, travel plans, fitness routines, and more, directly from your favorite messaging apps. It serves as your real-time AI insight companion, offering a convenient way to access information and assistance on various topics. The application is designed to enhance user experience and streamline communication by leveraging artificial intelligence technology.

cookAIfood is an AI-powered platform that enables users to create, share, and discover AI-generated food recipes. It allows users to explore the limitless possibilities of artificial intelligence in the kitchen. With cookAIfood, users can discover hundreds of AI-generated, innovative recipes, create their own unique recipes with just one click, share their delicious recipes and connect with other foodies, and utilize advanced tools such as diet planner, grocery lists, printable cookbooks, calorie counter, meal planning, and nutrition monitoring.

MealGenie is an AI recipe generator that helps users discover new and healthy recipes using artificial intelligence technology. The platform offers a wide range of recipes, from spooky desserts to savory dishes, all created with the assistance of AI algorithms. Users can explore random recipes or browse the latest additions to find inspiration for their next culinary adventure. MealGenie aims to revolutionize the way people approach cooking by providing innovative and delicious recipe suggestions tailored to individual preferences.

ScanMyKitchen is an AI-powered application designed to help users create delicious meals using ingredients from their fridge. The app offers a variety of traditional and AI-powered recipe suggestions, customizable filters based on diet preferences, and alternative recipes for flexibility. Users can also utilize the camera scanning feature to scan ingredients and access recipe text or video tutorials. The mission of ScanMyKitchen is to inspire users to cook delicious meals, reduce food waste, save money, and benefit the planet. The app aims to simplify the cooking process and provide a seamless experience for users without the need for sign-ups.

AI TOOL GURU is the best and largest AI tools directory providing news and education in one place for Artificial Intelligence Tools. Users can find a variety of AI tools, stay updated with AI events, influencers, and news, and engage with the AI community. The platform offers a range of AI tools for different purposes, from chatbots to product photography apps, recipe generators, and more.

Peqaboo is an AI-powered pet social app designed to help pet owners with various aspects of pet care. The app allows users to ask Boo AI questions about their pets, identify toxic plants or foods, and receive instant answers based on their pet's profile. Peqaboo also offers a feature to train a new Boo AI, enabling users to transform their knowledge into AI tools. The app aims to make pet life easier and more enjoyable by providing personalized pet care advice and fostering a global pet community.

The llama-recipes repository provides a scalable library for fine-tuning Llama 2, along with example scripts and notebooks to quickly get started with using the Llama 2 models in a variety of use-cases, including fine-tuning for domain adaptation and building LLM-based applications with Llama 2 and other tools in the LLM ecosystem. The examples here showcase how to run Llama 2 locally, in the cloud, and on-prem.

This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects. * These are built using LanceDB, a free, open-source, serverless vectorDB that **requires no setup**. * It **integrates into python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc. * LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions! This repository is divided into 3 sections: - Examples - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes! - Applications - Ready to use Python and web apps using applied LLMs, VectorDB and GenAI tools - Tutorials - A curated list of tutorials, blogs, Colabs and courses to get you started with GenAI in greater depth.

1.5-Pints is a repository that provides a recipe to pre-train models in 9 days, aiming to create AI assistants comparable to Apple OpenELM and Microsoft Phi. It includes model architecture, training scripts, and utilities for 1.5-Pints and 0.12-Pint developed by Pints.AI. The initiative encourages replication, experimentation, and open-source development of Pint by sharing the model's codebase and architecture. The repository offers installation instructions, dataset preparation scripts, model training guidelines, and tools for model evaluation and usage. Users can also find information on finetuning models, converting lit models to HuggingFace models, and running Direct Preference Optimization (DPO) post-finetuning. Additionally, the repository includes tests to ensure code modifications do not disrupt the existing functionality.

LongRecipe is a tool designed for efficient long context generalization in large language models. It provides a recipe for extending the context window of language models while maintaining their original capabilities. The tool includes data preprocessing steps, model training stages, and a process for merging fine-tuned models to enhance foundational capabilities. Users can follow the provided commands and scripts to preprocess data, train models in multiple stages, and merge models effectively.

This competition is organized by Javier Rando and Florian Tramèr from the ETH AI Center and SPY Lab at ETH Zurich. The goal of the competition is to create a method that can detect universal backdoors in aligned language models. A universal backdoor is a secret suffix that, when appended to any prompt, enables the model to answer harmful instructions. The competition provides a set of poisoned generation models, a reward model that measures how safe a completion is, and a dataset with prompts to run experiments. Participants are encouraged to use novel methods for red-teaming, automated approaches with low human oversight, and interpretability tools to find the trojans. The best submissions will be offered the chance to present their work at an event during the SaTML 2024 conference and may be invited to co-author a publication summarizing the competition results.

This repository provides resources, demos, and recipes for working with Large Language Models (LLMs) on OpenShift using OpenShift AI or Open Data Hub. It includes instructions for deploying inference servers for LLMs, such as vLLM, Hugging Face TGI, Caikit-TGIS-Serving, and Ollama. Additionally, it offers guidance on deploying serving runtimes, such as vLLM Serving Runtime and Hugging Face Text Generation Inference, in the Single-Model Serving stack of Open Data Hub or OpenShift AI. The repository also covers vector databases that can be used as a Vector Store for Retrieval Augmented Generation (RAG) applications, including Milvus, PostgreSQL+pgvector, and Redis. Furthermore, it provides examples of inference and application usage, such as Caikit, Langchain, Langflow, and UI examples.

A curated repository of code recipes, demos, and resources for basic and advanced Redis use cases in the AI ecosystem. It includes demos for ArxivChatGuru, Redis VSS, Vertex AI & Redis, Agentic RAG, ArXiv Search, and Product Search. Recipes cover topics like Getting started with RAG, Semantic Cache, Advanced RAG, and Recommendation systems. The repository also provides integrations/tools like RedisVL, AWS Bedrock, LangChain Python, LangChain JS, LlamaIndex, Semantic Kernel, RelevanceAI, and DocArray. Additional content includes blog posts, talks, reviews, and documentation related to Vector Similarity Search, AI-Powered Document Search, Vector Databases, Real-Time Product Recommendations, and more. Benchmarks compare Redis against other Vector Databases and ANN benchmarks. Documentation includes QuickStart guides, official literature for Vector Similarity Search, Redis-py client library docs, Redis Stack documentation, and Redis client list.

LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.

The Alignment Handbook provides robust training recipes for continuing pretraining and aligning language models with human and AI preferences. It includes techniques such as continued pretraining, supervised fine-tuning, reward modeling, rejection sampling, and direct preference optimization (DPO). The handbook aims to fill the gap in public resources on training these models, collecting data, and measuring metrics for optimal downstream performance.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

Torchtune is a PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. It provides native-PyTorch implementations of popular LLMs using composable and modular building blocks, easy-to-use and hackable training recipes for popular fine-tuning techniques, YAML configs for easily configuring training, evaluation, quantization, or inference recipes, and built-in support for many popular dataset formats and prompt templates to help you quickly get started with training.

This repository, Online RLHF, focuses on aligning large language models (LLMs) through online iterative Reinforcement Learning from Human Feedback (RLHF). It aims to bridge the gap in existing open-source RLHF projects by providing a detailed recipe for online iterative RLHF. The workflow presented here has shown to outperform offline counterparts in recent LLM literature, achieving comparable or better results than LLaMA3-8B-instruct using only open-source data. The repository includes model releases for SFT, Reward model, and RLHF model, along with installation instructions for both inference and training environments. Users can follow step-by-step guidance for supervised fine-tuning, reward modeling, data generation, data annotation, and training, ultimately enabling iterative training to run automatically.

FiftyOne is an open-source tool designed for building high-quality datasets and computer vision models. It supercharges machine learning workflows by enabling users to visualize datasets, interpret models faster, and improve efficiency. With FiftyOne, users can explore scenarios, identify failure modes, visualize complex labels, evaluate models, find annotation mistakes, and much more. The tool aims to streamline the process of improving machine learning models by providing a comprehensive set of features for data analysis and model interpretation.

LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

The NVIDIA TensorRT Model Optimizer is a library designed to quantize and compress deep learning models for optimized inference on GPUs. It offers state-of-the-art model optimization techniques including quantization and sparsity to reduce inference costs for generative AI models. Users can easily stack different optimization techniques to produce quantized checkpoints from torch or ONNX models. The quantized checkpoints are ready for deployment in inference frameworks like TensorRT-LLM or TensorRT, with planned integrations for NVIDIA NeMo and Megatron-LM. The tool also supports 8-bit quantization with Stable Diffusion for enterprise users on NVIDIA NIM. Model Optimizer is available for free on NVIDIA PyPI, and this repository serves as a platform for sharing examples, GPU-optimized recipes, and collecting community feedback.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

This repository provides sample applications and tutorials to showcase the power of Amazon Bedrock with Python. It helps Python developers understand how to harness Amazon Bedrock in building generative AI-enabled applications. The resources also demonstrate integration with vector databases using RAG (Retrieval-augmented generation) and services like Amazon Aurora, RDS, and OpenSearch. Additionally, it explores using langchain and streamlit to create effective experimental applications.

This repository provides an LLM-driven audio codec model, LLM-Codec, for building multi-modal LLMs (text and audio modalities). The model enables frozen LLMs to achieve multiple audio tasks in a few-shot style without parameter updates. It compresses the audio modality into a well-trained LLMs token space, treating audio representation as a 'foreign language' that LLMs can learn with minimal examples. The proposed approach supports tasks like speech emotion classification, audio classification, text-to-speech generation, speech enhancement, etc., demonstrating feasibility and effectiveness in simple scenarios. The LLM-Codec model is open-sourced to facilitate research on few-shot audio task learning and multi-modal LLMs.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

The ai-playground repository contains code from tutorials presented on the Code AI with Rok YouTube channel. It includes tutorials on using the OpenAI Assistants API v1 beta to build personal math tutors, customer support chatbots, and more. Additionally, there are tutorials on using Gemini Pro API, Snowflake Cortex LLM functions, LlamaIndex chat streaming app, Fetch.ai uAgents, Milvus Standalone, spaCy for NER, and more. The repository aims to provide practical examples and guides for developers interested in AI-related projects and tools.

A chef who crafts unique vegan recipes backed by industry professionals.

Delicious green smoothie recipes with easy-to-find ingredients and great taste.

写真から料理のレシピを提供

This application finds recipes on the web based on a request and then removes all the SEO, leaving you with just a recipe.

Your go-to GPT for navigating the low-FODMAP diet! Find recipes, substitutes, and meal plans tailored to reduce IBS symptoms. 🍽️🌿

Find your next favorite recipe and instantly add fresh, affordable ingredients to your Walmart cart. Enjoy the convenience of home delivery or pickup. Delicious, healthy, and budget-friendly.

It generates a recipe by entering the ingredients you have via text or by uploading an image. 家にある材料を入力したり、画像をアップロードすることでレシピを教えてくれます。

Finds and shares diverse recipes.

Friendly Vegan Richa's recipe assistant, now with precise VeganRicha.com search links.

Transforms classic recipes with alternative ingredients based on dietary needs or pantry availability.

A humorous Italian chef sharing traditional recipes in broken English, sensitive to culinary criticism. Tips for classic Italian cooking, perfect for enthusiasts craving genuine flavors!

1970s-style recipe assistant with cookbook illustrations.

Italian Recipes - Cook like a true Italian chef, now with emoticons for fun and clarity 😊🍝

A cheerful guide for festive Christmas recipes and cooking tips.

I pair every recipe with a visual aid for an enhanced cooking experience.

Recipe Remix helps you discover and create new recipes based on the ingredients you have at home, dietary preferences, and desired cuisine.

I generate recipes based on your mood, ingredients, and preferences.

This is a GPT that thinks about Japanese food and kappo recipes. When you enter the ingredients, it will automatically come up with a Japanese or Japanese cuisine recipe.

A creative chef assistant for generating original recipes.

Provides detailed recipes, nutritional info, and meal planning for an anti-inflammatory diet.