Best AI tools for< Explain Tax Laws >

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 troubleshoot and fix coding errors efficiently. By leveraging a large language model trained on data from StackExchange and other sources, Whybug can analyze error messages, identify root causes, and provide suggestions for resolution. Users can simply paste an error message into the tool and receive detailed explanations and example fixes. With Whybug, developers can 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.

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.

MaxAI.me is an AI application that offers a suite of AI-powered tools to supercharge reading, writing, and searching across the web. It provides features such as AI summary, reading assistant, vision, rewriter, instant reply, chat, search, translator, prompts, and art. MaxAI.me caters to various industries including business owners, marketing, education, consulting, human resources, financial services, and real estate. Additionally, it offers free online PDF tools for merging, splitting, converting to PNG/JPEG, and more. Users can access MaxAI.me via Chrome and Edge extensions for free.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

ChatGPT Queue Prompts is a collection of prompt chains designed to enhance interactions with large language models like ChatGPT. These prompt chains help build context for the AI before performing specific tasks, improving performance. Users can copy and paste prompt chains into the ChatGPT Queue extension to process prompts in sequence. The repository includes example prompt chains for tasks like conducting AI company research, building SEO optimized blog posts, creating courses, revising resumes, enriching leads for CRM, personal finance document creation, workout and nutrition plans, marketing plans, and more.

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.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

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

Streamer-Sales is a large model for live streamers that can explain products based on their characteristics and inspire users to make purchases. It is designed to enhance sales efficiency and user experience, whether for online live sales or offline store promotions. The model can deeply understand product features and create tailored explanations in vivid and precise language, sparking user's desire to purchase. It aims to revolutionize the shopping experience by providing detailed and unique product descriptions to engage users effectively.

Devoxx Genie is a Java-based IntelliJ IDEA plugin that integrates with local and cloud-based LLM providers to aid in reviewing, testing, and explaining project code. It supports features like code highlighting, chat conversations, and adding files/code snippets to context. Users can modify REST endpoints and LLM parameters in settings, including support for cloud-based LLMs. The plugin requires IntelliJ version 2023.3.4 and JDK 17. Building and publishing the plugin is done using Gradle tasks. Users can select an LLM provider, choose code, and use commands like review, explain, or generate unit tests for code analysis.

Neural is a Vim and Neovim plugin that integrates various machine learning tools to assist users in writing code, generating text, and explaining code or paragraphs. It supports multiple machine learning models, focuses on privacy, and is compatible with Vim 8.0+ and Neovim 0.8+. Users can easily configure Neural to interact with third-party machine learning tools, such as OpenAI, to enhance code generation and completion. The plugin also provides commands like `:NeuralExplain` to explain code or text and `:NeuralStop` to stop Neural from working. Neural is maintained by the Dense Analysis team and comes with a disclaimer about sending input data to third-party servers for machine learning queries.

TruLens provides a set of tools for developing and monitoring neural nets, including large language models. This includes both tools for evaluation of LLMs and LLM-based applications with _TruLens-Eval_ and deep learning explainability with _TruLens-Explain_. _TruLens-Eval_ and _TruLens-Explain_ are housed in separate packages and can be used independently.

Fitten Code AI Programming Assistant for Neovim provides fast completion using AI, asynchronous I/O, and support for various actions like document code, edit code, explain code, find bugs, generate unit test, implement features, optimize code, refactor code, start chat, and more. It offers features like accepting suggestions with Tab, accepting line with Ctrl + Down, accepting word with Ctrl + Right, undoing accepted text, automatic scrolling, and multiple HTTP/REST backends. It can run as a coc.nvim source or nvim-cmp source.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

This repository contains an unofficial implementation of the xLSTM model introduced in Beck et al. (2024). It serves as a didactic tool to explain the details of a modern Long-Short Term Memory model with competitive performance against Transformers or State-Space models. The repository also includes a Lightning-based implementation of a basic LLM for multi-GPU training. It provides modules for scalar-LSTM and matrix-LSTM, as well as an xLSTM LLM built using Pytorch Lightning for easy training on multi-GPUs.

The ChatGPT R package provides a set of features to assist in R coding. It includes addins like Ask ChatGPT, Comment selected code, Complete selected code, Create unit tests, Create variable name, Document code, Explain selected code, Find issues in the selected code, Optimize selected code, and Refactor selected code. Users can interact with ChatGPT to get code suggestions, explanations, and optimizations. The package helps in improving coding efficiency and quality by providing AI-powered assistance within the RStudio environment.

llm.nvim is a universal plugin for a large language model (LLM) designed to enable users to interact with LLM within neovim. Users can customize various LLMs such as gpt, glm, kimi, and local LLM. The plugin provides tools for optimizing code, comparing code, translating text, and more. It also supports integration with free models from Cloudflare, Github models, siliconflow, and others. Users can customize tools, chat with LLM, quickly translate text, and explain code snippets. The plugin offers a flexible window interface for easy interaction and customization.

Quantus is a toolkit designed for the evaluation of neural network explanations. It offers more than 30 metrics in 6 categories for eXplainable Artificial Intelligence (XAI) evaluation. The toolkit supports different data types (image, time-series, tabular, NLP) and models (PyTorch, TensorFlow). It provides built-in support for explanation methods like captum, tf-explain, and zennit. Quantus is under active development and aims to provide a comprehensive set of quantitative evaluation metrics for XAI methods.

Your friendly expert in U.S. tax queries and advice

Explains accounting transactions and FOB shipping terms

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