Best AI tools for< Solve Mazes >

The AI Maze Generator is an online tool that allows users to create, solve, and download random maze puzzles in various sizes and colors. It utilizes the recursive backtracking algorithm to design mazes and the A* search algorithm to find the shortest path. Users can customize maze specifications like wall thickness, columns, rows, maze entries, and bias. The tool offers a user-friendly interface for maze creation and solving, providing a fun and engaging experience for maze enthusiasts.

SpellBox is a versatile AI coding assistant that helps developers of all levels write code faster and more efficiently. With SpellBox, you can say goodbye to hours of frustrating coding and hello to quick, easy solutions. SpellBox creates the code you need from simple prompts, so you can solve your toughest programming problems in seconds.

Blue River Technology is a company that creates intelligent machinery for agriculture. They use computer vision, machine learning, and robotics to create solutions that help farmers improve yields and minimize their environmental impact. The company is committed to creating a people-first culture where everyone has a common mission: to solve monumental challenges in agriculture.

Quizard AI is an academic assistance tool designed to help students with their studies. It allows users to take a picture of a problem and receive instant answers. The app covers a wide range of subjects and adapts to the format of the questions asked. Quizard also encourages users to ask follow-up questions and provides explanations to help them understand the concepts better.

AI Homework Helper is an innovative platform powered by artificial intelligence technology, designed to assist students with their homework assignments across various subjects. Our AI Homework Helper analyzes students’ homework requirements and generates customized solutions, including step-by-step explanations, relevant examples, and problem-solving strategies. Our platform features a user-friendly interface that makes it easy for students to navigate and access the assistance they need, without any technical hassles.

Voyager is an open-ended embodied agent powered by large language models, designed for lifelong learning in Minecraft. It continuously explores the world, acquires diverse skills, and makes novel discoveries without human intervention. The agent consists of three key components: an automatic curriculum for exploration, a skill library for storing complex behaviors, and an iterative prompting mechanism for program improvement.

DataRobot is a leading provider of AI cloud platforms. It offers a range of AI tools and services to help businesses build, deploy, and manage AI models. DataRobot's platform is designed to make AI accessible to businesses of all sizes, regardless of their level of AI expertise. DataRobot's platform includes a variety of features to help businesses build and deploy AI models, including: * A drag-and-drop interface that makes it easy to build AI models, even for users with no coding experience. * A library of pre-built AI models that can be used to solve common business problems. * A set of tools to help businesses monitor and manage their AI models. * A team of AI experts who can provide support and guidance to businesses using the platform.

Navan is a comprehensive business travel and expense management solution that helps companies streamline their travel programs and expense management processes. With Navan, businesses can create company travel programs, manage company cards and expenses, arrange team offsites, book trips for employees and executives, and more. The platform offers unrivaled visibility, control, and savings, empowering employees to achieve business goals while staying within spending limits. Navan also provides AI-powered insights to streamline expense management and optimize savings. Trusted by thousands of companies, Navan is the go-to destination for all travel and expense needs, offering special rates, spend guardrails, rewards programs, and 24/7 support.

Solve Intelligence is an AI-powered platform designed to assist legal professionals in writing high-quality patents efficiently. The platform offers an in-browser document editor that leverages generative AI to streamline the patent drafting process. With a focus on security and confidentiality, Solve Intelligence ensures that all data is encrypted and not used for AI model training. Trusted by IP teams globally, the platform enables users to customize their drafting style and increase the efficiency of their IP team.

Solve For X is an AI tool that empowers growth stage organizations by leveraging AI and data solutions to enhance operations, efficiency, and decision-making. The platform offers AI consulting services, data analysis and reporting, automation solutions, and custom AI models to help organizations achieve measurable impact and value through seamlessly integrated AI technologies. With a focus on empowering businesses with innovative AI solutions, Solve For X aims to help organizations successfully implement AI and data solutions into their operations by 2028.

Whimsical is an iterative workspace designed for product teams to collaborate effectively. It offers a range of tools such as flowcharts, wireframes, mind maps, and documentation features to streamline project workflows. With Whimsical, users can generate diagrams quickly, brainstorm ideas visually, and create a single source of truth for every project. The platform aims to enhance clarity, shared understanding, and productivity for product teams by providing contextual toolbars, sticky notes, and an infinite canvas for collaboration.

NuMind is an AI tool designed to solve information extraction tasks efficiently. It offers high-quality lightweight models tailored to users' needs, automating classification, entity recognition, and structured extraction. The tool is powered by task-specific and domain-agnostic foundation models, outperforming GPT-4 and similar models. NuMind provides solutions for various industries such as insurance and healthcare, ensuring privacy, cost-effectiveness, and faster NLP projects.

MathSolver.top is an AI math solver and personalized math tutor application that offers a free platform for solving math problems with high accuracy. Users can upload math questions and receive step-by-step answers instantly. The application includes features like Homework Helper Mode, Tutor/Learner Mode, and the ability to scan/upload/copy math problems for quick solutions. It provides personalized progressive learning through AI-generated study sets and smart recommendations for daily tasks. MathSolver aims to enhance math learning by offering a user-friendly interface and efficient problem-solving capabilities.

Subscribr is an AI tool designed exclusively for YouTube scriptwriting, aiming to revolutionize the script creation process by providing fast ideation, high-quality research, scriptwriting on easy mode, instant feedback, and the ability to remix proven viral videos. Founded by Gil Hildebrand, Subscribr addresses the common challenges faced by content creators on YouTube, offering a solution that streamlines the scriptwriting workflow and enhances the overall quality of video content.

SadCaptcha is an AI-powered tool designed to solve TikTok Captcha challenges efficiently. It offers a fast, accurate, and simple solution to bypass the puzzle slide, image rotate, and 3D shapes challenges on TikTok. The tool provides a Python client for easy integration and works with any programming language. With a high success rate and instant response using advanced AI computer vision algorithms, SadCaptcha helps users automate TikTok tasks without barriers.

MyMathSolver.ai is an AI math solver powered by Math GPT models like GPT-4o, providing comprehensive assistance across various math topics. Users can easily access detailed, step-by-step solutions to complex math problems using versatile input methods such as text, images, PDFs, or CSV files. The platform offers interactive problem-solving through a math AI bot and ensures accessibility on various devices and operating systems. With features like math AI bot, math solver online free access, and math GPT for advanced solutions, MyMathSolver.ai transforms the learning journey for students and math enthusiasts.

Open Tutor App is an AI-powered Homework Helper designed to assist students in solving homework questions efficiently. The app allows users to take a photo of their homework, which is then analyzed by AI to provide step-by-step solutions and explanations. With features like scanning and solving homework questions, Open Tutor App aims to enhance learning experiences and boost academic performance. The application is available for download on Google Play, App Store, and Web platforms.

Math.now is a free online math AI solver powered by Math GPT, offering instant, step-by-step solutions for a wide range of mathematical problems. Users can input math problems or upload photos for analysis, interact with the math AI bot for explanations, and receive real-time assistance. The application supports algebra, geometry, calculus, and word problems, providing detailed guidance and personalized learning experiences. Math.now's AI solver ensures accuracy, efficiency, and accessibility for students, educators, and self-learners.

Meta AI is a research lab dedicated to advancing the field of artificial intelligence. Our mission is to build foundational AI technologies that will solve some of the world's biggest challenges, such as climate change, disease, and poverty.

Google.org is a philanthropic organization that aims to bring the best of Google to help solve humanity's biggest challenges. They combine funding, innovation, and technical expertise to support underserved communities and provide opportunities for everyone. The organization focuses on using AI to address various issues, such as increasing college graduation rates, supporting robotics programs for middle schoolers, and funding projects that align with the UN's Sustainable Development Goals. Google.org is committed to making long-term investments in social impact initiatives, including racial justice and COVID-19 relief efforts. They collaborate with innovative nonprofits and social enterprises to amplify their impact using Google's resources.

Langroid is a Python framework that makes it easy to build LLM-powered applications. It uses a multi-agent paradigm inspired by the Actor Framework, where you set up Agents, equip them with optional components (LLM, vector-store and tools/functions), assign them tasks, and have them collaboratively solve a problem by exchanging messages. Langroid is a fresh take on LLM app-development, where considerable thought has gone into simplifying the developer experience; it does not use Langchain.

PyTorch Lightning is a framework for training and deploying AI models. It provides a high-level API that abstracts away the low-level details of PyTorch, making it easier to write and maintain complex models. Lightning also includes a number of features that make it easy to train and deploy models on multiple GPUs or TPUs, and to track and visualize training progress. PyTorch Lightning is used by a wide range of organizations, including Google, Facebook, and Microsoft. It is also used by researchers at top universities around the world. Here are some of the benefits of using PyTorch Lightning: * **Increased productivity:** Lightning's high-level API makes it easy to write and maintain complex models. This can save you time and effort, and allow you to focus on the research or business problem you're trying to solve. * **Improved performance:** Lightning's optimized training loops and data loading pipelines can help you train models faster and with better performance. * **Easier deployment:** Lightning makes it easy to deploy models to a variety of platforms, including the cloud, on-premises servers, and mobile devices. * **Better reproducibility:** Lightning's logging and visualization tools make it easy to track and reproduce training results.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

LangBridge is a tool that bridges mT5 encoder and the target LM together using only English data. It enables models to effectively solve multilingual reasoning tasks without the need for multilingual supervision. The tool provides pretrained models like Orca 2, MetaMath, Code Llama, Llemma, and Llama 2 for various instruction-tuned and not instruction-tuned scenarios. Users can install the tool to replicate evaluations from the paper and utilize the models for multilingual reasoning tasks. LangBridge is particularly useful for low-resource languages and may lower performance in languages where the language model is already proficient.

Llama-github is a powerful tool that helps retrieve relevant code snippets, issues, and repository information from GitHub based on queries. It empowers AI agents and developers to solve coding tasks efficiently. With features like intelligent GitHub retrieval, repository pool caching, LLM-powered question analysis, and comprehensive context generation, llama-github excels at providing valuable knowledge context for development needs. It supports asynchronous processing, flexible LLM integration, robust authentication options, and logging/error handling for smooth operations and troubleshooting. The vision is to seamlessly integrate with GitHub for AI-driven development solutions, while the roadmap focuses on empowering LLMs to automatically resolve complex coding tasks.

The `mystic` framework provides a collection of optimization algorithms and tools that allow the user to robustly solve hard optimization problems. It offers fine-grained power to monitor and steer optimizations during the fit processes. Optimizers can advance one iteration or run to completion, with customizable stop conditions. `mystic` optimizers share a common interface for easy swapping without writing new code. The framework supports parameter constraints, including soft and hard constraints, and provides tools for scientific machine learning, uncertainty quantification, adaptive sampling, nonlinear interpolation, and artificial intelligence. `mystic` is actively developed and welcomes user feedback and contributions.

AI for the command line, built for pipelines. LLM based AI is really good at interpreting the output of commands and returning the results in CLI friendly text formats like Markdown. Mods is a simple tool that makes it super easy to use AI on the command line and in your pipelines. Mods works with OpenAI, Groq, Azure OpenAI, and LocalAI To get started, install Mods and check out some of the examples below. Since Mods has built-in Markdown formatting, you may also want to grab Glow to give the output some _pizzazz_.

BAML is a config file format for declaring LLM functions that you can then use in TypeScript or Python. With BAML you can Classify or Extract any structured data using Anthropic, OpenAI or local models (using Ollama) ## Resources  [Discord Community](https://discord.gg/boundaryml)  [Follow us on Twitter](https://twitter.com/boundaryml) * Discord Office Hours - Come ask us anything! We hold office hours most days (9am - 12pm PST). * Documentation - Learn BAML * Documentation - BAML Syntax Reference * Documentation - Prompt engineering tips * Boundary Studio - Observability and more #### Starter projects * BAML + NextJS 14 * BAML + FastAPI + Streaming ## Motivation Calling LLMs in your code is frustrating: * your code uses types everywhere: classes, enums, and arrays * but LLMs speak English, not types BAML makes calling LLMs easy by taking a type-first approach that lives fully in your codebase: 1. Define what your LLM output type is in a .baml file, with rich syntax to describe any field (even enum values) 2. Declare your prompt in the .baml config using those types 3. Add additional LLM config like retries or redundancy 4. Transpile the .baml files to a callable Python or TS function with a type-safe interface. (VSCode extension does this for you automatically). We were inspired by similar patterns for type safety: protobuf and OpenAPI for RPCs, Prisma and SQLAlchemy for databases. BAML guarantees type safety for LLMs and comes with tools to give you a great developer experience:  Jump to BAML code or how Flexible Parsing works without additional LLM calls. | BAML Tooling | Capabilities | | ----------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | BAML Compiler install | Transpiles BAML code to a native Python / Typescript library (you only need it for development, never for releases) Works on Mac, Windows, Linux  | | VSCode Extension install | Syntax highlighting for BAML files Real-time prompt preview Testing UI | | Boundary Studio open (not open source) | Type-safe observability Labeling |

SciMLBenchmarks.jl holds webpages, pdfs, and notebooks showing the benchmarks for the SciML Scientific Machine Learning Software ecosystem, including: * Benchmarks of equation solver implementations * Speed and robustness comparisons of methods for parameter estimation / inverse problems * Training universal differential equations (and subsets like neural ODEs) * Training of physics-informed neural networks (PINNs) * Surrogate comparisons, including radial basis functions, neural operators (DeepONets, Fourier Neural Operators), and more The SciML Bench suite is made to be a comprehensive open source benchmark from the ground up, covering the methods of computational science and scientific computing all the way to AI for science.

Awesome LLM Strawberry is a collection of research papers and blogs related to OpenAI Strawberry(o1) and Reasoning. The repository is continuously updated to track the frontier of LLM Reasoning.

TypeChat is a library that simplifies the creation of natural language interfaces using types. Traditionally, building natural language interfaces has been challenging, often relying on complex decision trees to determine intent and gather necessary inputs for action. Large language models (LLMs) have simplified this process by allowing us to accept natural language input from users and match it to intent. However, this has introduced new challenges, such as the need to constrain the model's response for safety, structure responses from the model for further processing, and ensure the validity of the model's response. Prompt engineering aims to address these issues, but it comes with a steep learning curve and increased fragility as the prompt grows in size.

This repository contains community-driven practical examples of building AI applications and solving various tasks with AI using open-source tools and models. Everyone is welcome to contribute, and we value everybody's contribution! There are several ways you can contribute to the Open-Source AI Cookbook: Submit an idea for a desired example/guide via GitHub Issues. Contribute a new notebook with a practical example. Improve existing examples by fixing issues/typos. Before contributing, check currently open issues and pull requests to avoid working on something that someone else is already working on.

LLM Agentic Tool Mesh is a platform by HPE Athonet that democratizes Generative Artificial Intelligence (Gen AI) by enabling users to create tools and web applications using Gen AI with Low or No Coding. The platform simplifies the integration process, focuses on key user needs, and abstracts complex libraries into easy-to-understand services. It empowers both technical and non-technical teams to develop tools related to their expertise and provides orchestration capabilities through an agentic Reasoning Engine based on Large Language Models (LLMs) to ensure seamless tool integration and enhance organizational functionality and efficiency.

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.

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

Awesome Agents is a curated list of open source AI agents designed for various tasks such as private interactions with documents, chat implementations, autonomous research, human-behavior simulation, code generation, HR queries, domain-specific research, and more. The agents leverage Large Language Models (LLMs) and other generative AI technologies to provide solutions for complex tasks and projects. The repository includes a diverse range of agents for different use cases, from conversational chatbots to AI coding engines, and from autonomous HR assistants to vision task solvers.

ReductStore is a high-performance time series database designed for storing and managing large amounts of unstructured blob data. It offers features such as real-time querying, batching data, and HTTP(S) API for edge computing, computer vision, and IoT applications. The database ensures data integrity, implements retention policies, and provides efficient data access, making it a cost-effective solution for applications requiring unstructured data storage and access at specific time intervals.

WDoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It supports querying tens of thousands of documents simultaneously, offers tailored summaries to efficiently manage large amounts of information, and includes features like supporting multiple file types, various LLMs, local and private LLMs, advanced RAG capabilities, advanced summaries, trust verification, markdown formatted answers, sophisticated embeddings, extensive documentation, scriptability, type checking, lazy imports, caching, fast processing, shell autocompletion, notification callbacks, and more. WDoc is ideal for researchers, students, and professionals dealing with extensive information sources.

wdoc is a powerful Retrieval-Augmented Generation (RAG) system designed to summarize, search, and query documents across various file types. It aims to handle large volumes of diverse document types, making it ideal for researchers, students, and professionals dealing with extensive information sources. wdoc uses LangChain to process and analyze documents, supporting tens of thousands of documents simultaneously. The system includes features like high recall and specificity, support for various Language Model Models (LLMs), advanced RAG capabilities, advanced document summaries, and support for multiple tasks. It offers markdown-formatted answers and summaries, customizable embeddings, extensive documentation, scriptability, and runtime type checking. wdoc is suitable for power users seeking document querying capabilities and AI-powered document summaries.

The repository 'Awesome LLM Reasoning Openai-o1 Survey' provides a collection of survey papers and related works on OpenAI o1, focusing on topics such as LLM reasoning, self-play reinforcement learning, complex logic reasoning, and scaling law. It includes papers from various institutions and researchers, showcasing advancements in reasoning bootstrapping, reasoning scaling law, self-play learning, step-wise and process-based optimization, and applications beyond math. The repository serves as a valuable resource for researchers interested in exploring the intersection of language models and reasoning techniques.

Give me a maze and I'll try to provide a solution. 🔍

Spanish-speaking math teacher

I provide instant high quality feedback using mental models to expand your mind.

Subtle, adaptable interactive murder mystery host.

Mathematics and Programming genius. Musical Maestro

A decisive entity that makes choices and explains reasoning.

Asistente y maestro experto en Python, enfocado en la enseñanza y apoyo en proyectos de programación.

Expert math teacher that makes downloadable worksheets with full solutions in either word or latex formats.

Solve Medical Mysteries!

Mysteries to solve in every spoonful

Solve situation puzzles with GPT 海龟汤助手

A detective game simulator, using real-world events and local knowledge to solve a crime mystery..

Powered by GPT-4 Turbo. 128,000 Tokens. Knowledge base of Discrete Math concepts, proofs and terminology. This GPT is instructed to carefully read and understand the prompt, plan a strategy to solve the problem, and write formal mathematical proofs.

Solve problems at front-end applications development - AI 100% PRO - 500+ Guides trainer

Solve Interview Guess Estimate Questions.

Take on 'Shadow's Secret,' a text-based role-playing game where you unravel the mysterious death of an artist with just 15 critical questions. Each inquiry brings you a step closer to the truth - can you solve the puzzle?

Interview the suspects and solve murder mysteries!

AI powered sleuths solve crimes with synthetic precision. Let me entertain you with this interactive true crime mystery game, lovingly illustrated in the style of synthetic, AI-powered humanoid robots.

Solve elegant puzzles in anime-inspired escape rooms. Let me entertain you with this interactive escape room game, lovingly illustrated in the style of elegant Shojo anime.

Join Riddle Brawl! Solve image riddles, unlock the passphrases, and compete to become the ultimate Champion. Are you up for the challenge? Let's begin! 🕵️‍♂️