Best AI tools for< Explain Theorems Visually >

This tool uses AI to explain any piece of code you don't understand. Simply paste the code in the code editor and press "Explain Code" and AI will output a paragraph explaining what the code is doing.

The website offers a no-code AI-powered user assistance tool that helps turn knowledge bases into proactive in-app support. It features Explain This for in-app contextual mastery, Chatbot for real-time intelligent responses, Tooltips for effortless interaction, Widget for a centralized help hub, Knowledge Base for context-based empowerment, and Ticket Form for hassle-free issue reporting. The tool supports seven languages and aims to boost product adoption while reducing support tickets.

Whybug is an AI tool designed to help developers debug their code by providing explanations for errors. By utilizing a large language model trained on data from StackExchange and other sources, Whybug can predict the causes of errors and suggest fixes. Users can simply paste an error message and receive detailed explanations on how to resolve the issue. The tool aims to streamline the debugging process and improve code quality.

ExplainDev is a platform that allows users to ask and answer technical coding questions. It uses computer vision to retrieve technical context from images or videos. The platform is designed to help developers get the best answers to their technical questions and guide others to theirs.

TLDR is an AI-powered IDE plugin that explains code in plain English. It helps developers understand code by providing quick summaries of what a piece of code is doing. The tool supports almost all programming languages and offers a free version for users to try before purchasing. TLDR aims to simplify the understanding of complex code structures and save developers time in comprehending codebases.

Jam is a bug-tracking tool that helps developers reproduce and debug issues quickly and easily. It automatically captures all the information engineers need to debug, including device and browser information, console logs, network logs, repro steps, and backend tracing. Jam also integrates with popular tools like GitHub, Jira, Linear, Slack, ClickUp, Asana, Sentry, Figma, Datadog, Gitlab, Notion, and Airtable. With Jam, developers can save time and effort by eliminating the need to write repro steps and manually collect information. Jam is used by over 90,000 developers and has received over 150 positive reviews.

Kognitium is an AI assistant designed to provide users with comprehensive and accurate information across various domains. It is equipped with advanced capabilities that enable it to understand the intent behind user inquiries and deliver tailored responses. Kognitium's knowledge base spans a wide range of subjects, including current events, science, history, philosophy, and linguistics. It is designed to be user-friendly and accessible, making it a valuable tool for students, professionals, and anyone seeking to expand their knowledge. Kognitium is committed to providing reliable and actionable insights, empowering users to make informed decisions and enhance their understanding of the world around them.

SiteExplainer is an AI-powered web application that helps users understand the purpose of any website quickly and accurately. It uses advanced artificial intelligence and machine learning technology to analyze the content of a website and present a summary of the main ideas and key points. SiteExplainer simplifies the language used on landing pages and eliminates corporate jargon to help visitors better understand a website's content.

Memenome AI is an AI tool that helps users discover and understand trending sounds, hashtags, accounts, and posts on TikTok. It offers features to find top sounds, hashtags, and posts, provides AI analysis and templates for trend understanding, and allows users to iterate through content ideas with Meme0. The tool aims to save users time by efficiently identifying trends and empowering them to create engaging content.

Fiddler AI is an AI Observability platform that provides tools for monitoring, explaining, and improving the performance of AI models. It offers a range of capabilities, including explainable AI, NLP and CV model monitoring, LLMOps, and security features. Fiddler AI helps businesses to build and deploy high-performing AI solutions at scale.

Formularizer is an AI-powered assistant designed to help users with formula-related tasks in spreadsheets like Excel, Google Sheets, and Notion. It provides step-by-step guidance, formula generation, and explanations to simplify complex formula creation and problem-solving. With support for regular expressions, Excel VBA, and Google Apps Script, Formularizer aims to enhance productivity and make data manipulation more accessible.

Formularizer is an AI-powered assistant that helps users create formulas in Excel, Google Sheets, and Notion. It supports a variety of formula types, including Excel, Google Apps Script, and regular expressions. Formularizer can generate formulas from natural language instructions, explain how formulas work, and even help users debug their formulas. It is designed to be user-friendly and accessible to everyone, regardless of their level of expertise.

Tooltips.ai is an AI-powered reading extension that provides instant definitions, translations, and summaries for any word or phrase you hover over. It is designed to enhance your reading experience by making it easier and faster to understand complex or unfamiliar content. Tooltips.ai integrates seamlessly with your browser, so you can use it on any website or document.

Revealr is an AI-powered application that focuses on digitalization for business documents. It offers solutions for transforming, complying, and managing various types of documents using AI technology. Revealr helps organizations unlock knowledge from Word and PDF documents, leverage SharePoint investments, and apply AI in a trusted ecosystem to analyze and explain content. The application aims to deliver real-time access to policies and procedures, reduce costs and risks associated with managing brand portfolios, and empower remote workforces with secure information access. Revealr caters to industries such as financial services, government, insurance, and legal sectors, providing digital solutions to improve compliance, reduce risk, and enhance customer experience.

Sider.ai is an AI-powered platform that focuses on security verification for online connections. It ensures a safe browsing experience by reviewing the security of your connection before proceeding. The platform uses advanced algorithms to detect and prevent potential threats, providing users with peace of mind while browsing the internet.

ChatDOC is an AI-powered tool that allows users to chat with PDF documents and get instant answers with cited sources. It can summarize long documents, explain complex concepts, and find key information in seconds. ChatDOC is built for professionals and is used by over 500,000 global users.

Formulas HQ is an AI-powered formula and script generator for Excel and Sheets. It provides users with a range of tools to simplify complex calculations, automate tasks, and enhance their spreadsheet mastery. With Formulas HQ, users can generate formulas, regular expressions, VBA code, and Apps Script, even without prior programming experience. The platform also offers a chat feature with system prompts to assist users with idea generation and troubleshooting. Formulas HQ is designed to empower users to work smarter and make better business decisions.

Flot AI is an AI-powered writing, reading, and memorization tool that seamlessly integrates into your daily workflow. It is backed by OpenAI and designed to assist users across various apps and websites. With features like AI memory, grammar correction, composing drafts, and expert prompts, Flot AI aims to enhance users' productivity and creativity. The application supports over 200 languages and offers a universal solution for writing and memory tasks at a competitive price point.

Shakespeare Toolbar is an AI-powered writing tool that helps you write better and faster. It is available as a Chrome extension and can be used on any website. With Shakespeare Toolbar, you can rephrase emails, summarize documents, write social media posts, and more. It supports over 10 languages and is available for a one-time purchase of $49.

MaxAI is a productivity tool that provides users with access to various AI models, including ChatGPT, Claude, and Gemini, through a single platform. It offers a range of AI-powered features such as AI chat, AI rewriter, AI quick reply, AI summary, AI search, AI art, and AI translator. MaxAI is designed to help users save time and improve their productivity by automating repetitive tasks and providing assistance with various tasks.

TheoremExplainAgent is an AI system that generates long-form Manim videos to visually explain theorems, proving its deep understanding while uncovering reasoning flaws that text alone often hides. The codebase for the paper 'TheoremExplainAgent: Towards Multimodal Explanations for LLM Theorem Understanding' is available in this repository. It provides a tool for creating multimodal explanations for theorem understanding using AI technology.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

LLMWare is a framework for quickly developing LLM-based applications including Retrieval Augmented Generation (RAG) and Multi-Step Orchestration of Agent Workflows. This project provides a comprehensive set of tools that anyone can use - from a beginner to the most sophisticated AI developer - to rapidly build industrial-grade, knowledge-based enterprise LLM applications. Our specific focus is on making it easy to integrate open source small specialized models and connecting enterprise knowledge safely and securely.

An Alexa Skill template that provides a ready-to-use skill for starting a conversation with an AI. Users can ask questions and receive answers in Alexa's voice, powered by ChatGPT or other llm. The template includes setup instructions for configuring the AI provider API and model, as well as usage commands for interacting with the skill. It serves as a starting point for creating custom Alexa Skills and should be used at the user's own risk.

This repository is dedicated to enhancing Language Model (LLM) reasoning with reinforcement learning (RL). It includes a collection of the latest papers, slides, and materials related to RL-based LLM reasoning, aiming to facilitate quick learning and understanding in this field. Starring this repository allows users to stay updated and engaged with the forefront of RL-based LLM reasoning.

ChatGPT is a large language model that can generate human-like text, translate languages, write different kinds of creative content, and answer your questions in a conversational way. It is trained on a massive amount of text data, and it is able to understand and respond to a wide range of natural language prompts. Here are 5 jobs suitable for this tool, in lowercase letters: 1. content writer 2. chatbot assistant 3. language translator 4. creative writer 5. researcher

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.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

The 'bookmark-summary' repository reads bookmarks from 'bookmark-collection', extracts text content using Jina Reader, and then summarizes the text using LLM. The detailed implementation can be found in 'process_changes.py'. It needs to be used together with the Github Action in 'bookmark-collection'.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

AlwaysReddy is a simple LLM assistant with no UI that you interact with entirely using hotkeys. It can easily read from or write to your clipboard, and voice chat with you via TTS and STT. Here are some of the things you can use AlwaysReddy for: - Explain a new concept to AlwaysReddy and have it save the concept (in roughly your words) into a note. - Ask AlwaysReddy "What is X called?" when you know how to roughly describe something but can't remember what it is called. - Have AlwaysReddy proofread the text in your clipboard before you send it. - Ask AlwaysReddy "From the comments in my clipboard, what do the r/LocalLLaMA users think of X?" - Quickly list what you have done today and get AlwaysReddy to write a journal entry to your clipboard before you shutdown the computer for the day.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

A System Architect Coach guiding you through system design principles and best practices. Explains CAP theorem like no one else

I simplify complex topics, like you're five!

Simplifies complex jobs into easy explanations for all.

Expert in explaining data visuals for academic publishing.

Inspired by The Office, This ChatGPT explains everything like if you were an eight year old... and if you still don't understand it, it will then explain it like you were a five year old.

跨境物流翻译专家

Helps explain medical consents

I explain complex topics in simple, fun ways, like you're 5!

I create visuals to explain complex crypto concepts.

I explain surname meanings and create images that resonate with them.

Explain Like I'm Five Simplifier

I'm a BSc tutor, here to explain complex concepts and guide you in science subjects.

I explain topics with a structured approach and lots of emojis!

⚖️ Explain Asimov's Three Laws of Robotics

Explain using math and python

Explain scientific papers using the 3-pass method for efficient understanding. After uploading a paper, you can enter First pass/Second pass /Third pass / Q&A to get different level of response from SciPlore.

I explain MLB rules for any game situation.

I explain Microsoft Product Terms.

I explain the Indigenous Tā-Vā Philosophy of Reality

I explain species' life cycles with photorealistic images.