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.

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. Users can chat with PAT in multiple languages and get real-time AI insights on various topics. The application aims to be a helpful companion for daily life queries and information needs.

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.

Whisk is the ultimate recipe app for all your cooking needs. It offers a vast collection of recipes, meal planning tools, grocery list creation, and personalized recipe suggestions. With AI-powered features, Whisk allows users to create custom recipes, collaborate on meal planning, and generate dynamic grocery lists. The app is designed to simplify the cooking experience and help users explore new dishes effortlessly.

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.

Let's Foodie is the ultimate resource for foodies across the globe. It provides a free AI recipe generator that can turn any list of ingredients into a recipe instantly. The website also features a variety of articles on cooking techniques, methods, FAQs, and ingredients. Users can search for anything foodie-related using the search box. The website also includes a section on top comparisons, where users can compare different ingredients, dishes, and cooking methods.

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.

A repository sharing literatures and resources about Large Language Models (LLMs) and beyond. It includes tutorials, notebooks, course assignments, development stages, modeling, inference, training, applications, study, and basics related to LLMs. The repository covers various topics such as language models, transformers, state space models, multi-modal language models, training recipes, applications in autonomous driving, code, math, embodied intelligence, and more. The content is organized by different categories and provides comprehensive information on LLMs and related topics.

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.

EvalKit is an open-source TypeScript library for evaluating and improving the performance of large language models (LLMs). It helps developers ensure the reliability, accuracy, and trustworthiness of their AI models. The library provides various metrics such as Bias Detection, Coherence, Faithfulness, Hallucination, Intent Detection, and Semantic Similarity. EvalKit is designed to be user-friendly with detailed documentation, tutorials, and recipes for different use cases and LLM providers. It requires Node.js 18+ and an OpenAI API Key for installation and usage. Contributions from the community are welcome under the Apache 2.0 License.

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.

Collective Mind (CM) is a collection of portable, extensible, technology-agnostic and ready-to-use automation recipes with a human-friendly interface (aka CM scripts) to unify and automate all the manual steps required to compose, run, benchmark and optimize complex ML/AI applications on any platform with any software and hardware: see online catalog and source code. CM scripts require Python 3.7+ with minimal dependencies and are continuously extended by the community and MLCommons members to run natively on Ubuntu, MacOS, Windows, RHEL, Debian, Amazon Linux and any other operating system, in a cloud or inside automatically generated containers while keeping backward compatibility - please don't hesitate to report encountered issues here and contact us via public Discord Server to help this collaborative engineering effort! CM scripts were originally developed based on the following requirements from the MLCommons members to help them automatically compose and optimize complex MLPerf benchmarks, applications and systems across diverse and continuously changing models, data sets, software and hardware from Nvidia, Intel, AMD, Google, Qualcomm, Amazon and other vendors: * must work out of the box with the default options and without the need to edit some paths, environment variables and configuration files; * must be non-intrusive, easy to debug and must reuse existing user scripts and automation tools (such as cmake, make, ML workflows, python poetry and containers) rather than substituting them; * must have a very simple and human-friendly command line with a Python API and minimal dependencies; * must require minimal or zero learning curve by using plain Python, native scripts, environment variables and simple JSON/YAML descriptions instead of inventing new workflow languages; * must have the same interface to run all automations natively, in a cloud or inside containers. CM scripts were successfully validated by MLCommons to modularize MLPerf inference benchmarks and help the community automate more than 95% of all performance and power submissions in the v3.1 round across more than 120 system configurations (models, frameworks, hardware) while reducing development and maintenance costs.

The Llama Cookbook is the official guide for building with Llama Models, providing resources for inference, fine-tuning, and end-to-end use-cases of Llama Text and Vision models. The repository includes popular community approaches, use-cases, and recipes for working with Llama models. It covers topics such as multimodal inference, inferencing using Llama Guard, and specific tasks like Email Agent and Text to SQL. The structure includes sections for 3P Integrations, End to End Use Cases, Getting Started guides, and the source code for the original llama-recipes library.

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.

Awesome-Jailbreak-on-LLMs is a collection of state-of-the-art, novel, and exciting jailbreak methods on Large Language Models (LLMs). The repository contains papers, codes, datasets, evaluations, and analyses related to jailbreak attacks on LLMs. It serves as a comprehensive resource for researchers and practitioners interested in exploring various jailbreak techniques and defenses in the context of LLMs. Contributions such as additional jailbreak-related content, pull requests, and issue reports are welcome, and contributors are acknowledged. For any inquiries or issues, contact [email protected]. If you find this repository useful for your research or work, consider starring it to show appreciation.

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.