Best AI tools for< Learn Methods >

Quetab is a modern AI-driven platform designed to boost productivity, enhance skill sets, and facilitate learning through advanced technology. Users can create flashcards, generate questions, summarize text, and more with the help of AI tools. The platform offers a range of study sets, including US Citizenship Test Questions, Medical Terms Translation, and English Vocabulary Questions. Quetab aims to revolutionize learning efficiency and content creation by leveraging AI-powered solutions.

Dhime is an AI-powered dance coaching application that allows users to learn dance conveniently from expert video tutorials, practice with AI guidance and feedback, and improve their dance skills with confidence. It offers unlimited practice, personalized lessons, and continuous progress tracking for effective learning. Dhime serves as a platform for both tutors and dance academies to reach worldwide students and enhance their teaching methods. The application ensures privacy by recording and analyzing dance videos internally without sharing them externally. With Dhime, users can learn dance anytime, anywhere, and at their own pace.

Onri is a revolutionary online platform that provides a personalized learning path and high-quality learning materials to help users master any concept efficiently. With over 7 million concepts, 170 million paths, and 30,000 learning materials, Onri caters to students, industry analysts, and self-learners. The platform aims to address the challenges of traditional learning methods by offering expert guidance and resources for free, making learning accessible and effective in a rapidly changing world.

IllumiDesk is a generative AI platform designed for instructors and content developers. It enables users to create and monetize tailored content up to 10 times faster than traditional methods. The platform offers a range of features including automated grading, collaboration tools, real-time collaboration, AI-powered content creation, and integrations with various services. IllumiDesk is suitable for a wide range of users, from freelancers and solopreneurs to large organizations and educational institutions.

LearnQ.ai is a personalized AI-powered learning platform that transforms the learning journey by providing real-time data and insights to students, teachers, and administrators. It utilizes AI to detect and address learning gaps, empower teachers with student analytics, and boost students' confidence through data-driven learning. The platform offers various features such as diagnostic tests, engaging game-based learning modules, full-length practice tests, and a personalized AI tutor named Mia.

Elements of AI is a free online educational platform that offers courses on artificial intelligence for non-experts. Created by MinnaLearn and the University of Helsinki, the platform aims to demystify AI by providing theory, practical exercises, and a comprehensive understanding of AI concepts. With over 1 million students from 170 countries, the courses cover topics like Introduction to AI and Building AI, encouraging a broad audience to learn about AI's impact on society and how to create AI methods.

Coursable is an AI-powered student workspace designed to enhance learning experiences. It leverages artificial intelligence to provide personalized study recommendations, track progress, and offer interactive learning tools. With Coursable, students can access a virtual study companion that adapts to their learning style and pace, making studying more efficient and engaging. The platform aims to revolutionize traditional learning methods by incorporating AI technology to support students in achieving their academic goals.

Voz is an AI-powered language learning platform that offers AI-guided video lessons to help users master foreign languages from intermediate to advanced levels. The platform provides immersive learning experiences through real-world videos, AI tutors for speaking practice, and personalized feedback on vocabulary and grammar. Voz is designed to be cheaper and faster than traditional language learning methods, making it an effective tool for language learners of all levels.

AIPodNav is an AI-powered tool designed to enhance your podcast listening experience by providing features such as mind maps, summaries, takeaways, keywords, chapters, and transcriptions. It accelerates knowledge acquisition by 10 times faster than traditional podcast listening methods. AIPodNav aims to revolutionize how users engage with podcasts by offering innovative AI-driven functionalities.

This is an AI-powered language learning app that helps you grow your vocabulary in Spanish. It uses a variety of methods, including learning by example and repetition, to help you learn new words and phrases quickly and easily.

Furwee is an AI-powered educational platform designed to elevate early learning experiences for kids worldwide. It offers interactive dialogues, vocabulary expansion, reading enhancement, and emotional intelligence boost. Furwee harnesses AI technology to provide tailored solutions for children's educational needs, making learning fun and engaging. The platform is available in 9 global languages and caters to special needs learners with empathy and understanding at its core. Furwee aims to transform traditional learning methods by providing a safe, engaging, and scientifically superior environment for children to thrive.

Photomath is the ultimate math help app designed to assist learners of all levels, from elementary through college, in understanding and solving math problems. The app provides step-by-step explanations, allows users to scan problems, offers multiple solution methods, and encourages learning through detailed explanations. Photomath aims to help users build their math skills and confidence by providing personalized math assistance anytime, anywhere. With millions of learners benefiting from its features monthly, Photomath is a valuable tool for anyone seeking math support.

EcoSnap is an AI application that helps users recycle plastic more effectively by utilizing Artificial Intelligence technology. Users can simply take a picture of a plastic code and receive guidance on how to recycle it properly. The application aims to promote environmental sustainability by providing easy-to-access information on recycling methods.

CheatGPT is an AI-powered virtual study assistant designed to help students with their academic tasks. It offers a wide range of features such as providing fast and concise answers, image recognition capabilities, personal tutoring in various subjects, translations, and grammar corrections. The application utilizes advanced AI models like GPT-4o and Claude 3.5 Sonnet to deliver accurate and efficient responses to user queries. CheatGPT aims to enhance the learning experience for students by offering a cost-effective and efficient alternative to traditional study methods.

BoltAI is a powerful and user-friendly ChatGPT app for Mac that seamlessly integrates AI into your workflow. With BoltAI, you can access the capabilities of ChatGPT directly within your favorite macOS apps, enhancing your productivity and creativity. Whether you're a developer, content creator, student, or entrepreneur, BoltAI empowers you to leverage AI to streamline your tasks and achieve more. Its intuitive chat UI, powerful AI commands, and inline AI capabilities make it easy to incorporate AI assistance into your daily routine. BoltAI is designed to be versatile and customizable, allowing you to tailor it to your specific needs and preferences. With BoltAI, you can create custom AI assistants, utilize a library of prompts, and enjoy highly customizable features to optimize your workflow. BoltAI prioritizes your privacy and security, ensuring that your data remains protected and confidential. It operates locally on your device, with no data or prompts being stored or transmitted to external servers. Your OpenAI API key is securely stored in the Apple Keychain, adhering to industry-standard encryption methods. Additionally, BoltAI includes an automatic data detection feature that redacts sensitive information, providing peace of mind. BoltAI is committed to continuous improvement, with regular updates and new features being added to enhance your experience. By integrating BoltAI into your workflow, you gain access to a powerful AI assistant that can help you write high-quality content, generate creative ideas, debug code, learn new concepts, and much more. Unleash the potential of AI with BoltAI and experience a new level of productivity and efficiency.

The Visual Computing & Artificial Intelligence Lab at TUM is a group of research enthusiasts advancing cutting-edge research at the intersection of computer vision, computer graphics, and artificial intelligence. Our research mission is to obtain highly-realistic digital replica of the real world, which include representations of detailed 3D geometries, surface textures, and material definitions of both static and dynamic scene environments. In our research, we heavily build on advances in modern machine learning, and develop novel methods that enable us to learn strong priors to fuel 3D reconstruction techniques. Ultimately, we aim to obtain holographic representations that are visually indistinguishable from the real world, ideally captured from a simple webcam or mobile phone. We believe this is a critical component in facilitating immersive augmented and virtual reality applications, and will have a substantial positive impact in modern digital societies.

Prepvil is an AI-powered platform designed to elevate your meal experience. It offers a recipe sharing platform where users can discover diverse cuisines, unlock culinary creativity, and optimize their meals. The platform features a trained AI assistant that can help with various food-related needs such as recipes, nutrition, allergies, cooking methods, and more. Users can register, chat with the AI assistant, and share their own recipes to grow their audience. Prepvil aims to provide a space for home cooks and culinary enthusiasts to connect, learn, and explore the world of food.

Blackbox is an AI-powered code generation, code chat, and code search tool that helps developers write better code faster. With Blackbox, you can generate code snippets, chat with an AI assistant about code, and search for code examples from a massive database.

LingoSub is a language learning platform that allows users to learn new languages by watching videos with subtitles and AI-powered translations. With a vast library of videos in multiple languages, learners can immerse themselves in content that interests them while expanding their vocabulary and language skills. The platform offers a community of language learners, various pricing plans, and a responsive interface for seamless learning across devices.

Clarity AI is an AI-powered technology platform that offers a Sustainability Tech Kit for sustainable investing, shopping, reporting, and benchmarking. The platform provides built-in sustainability technology with customizable solutions for various needs related to data, methodologies, and tools. It seamlessly integrates into workflows, offering scalable and flexible end-to-end SaaS tools to address sustainability use cases. Clarity AI leverages powerful AI and machine learning to analyze vast amounts of data points, ensuring reliable and transparent data coverage. The platform is designed to empower users to assess, analyze, and report on sustainability aspects efficiently and confidently.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

The Aleph Alpha Intelligence Layer️ offers a comprehensive suite of development tools for crafting solutions that harness the capabilities of large language models (LLMs). With a unified framework for LLM-based workflows, it facilitates seamless AI product development, from prototyping and prompt experimentation to result evaluation and deployment. The Intelligence Layer SDK provides features such as Composability, Evaluability, and Traceability, along with examples to get started. It supports local installation using poetry, integration with Docker, and access to LLM endpoints for tutorials and tasks like Summarization, Question Answering, Classification, Evaluation, and Parameter Optimization. The tool also offers pre-configured tasks for tasks like Classify, QA, Search, and Summarize, serving as a foundation for custom development.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

Nixtla is a production-ready generative pretrained transformer for time series forecasting and anomaly detection. It can accurately predict various domains such as retail, electricity, finance, and IoT with just a few lines of code. TimeGPT introduces a paradigm shift with its standout performance, efficiency, and simplicity, making it accessible even to users with minimal coding experience. The model is based on self-attention and is independently trained on a vast time series dataset to minimize forecasting error. It offers features like zero-shot inference, fine-tuning, API access, adding exogenous variables, multiple series forecasting, custom loss function, cross-validation, prediction intervals, and handling irregular timestamps.

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.

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 project is a tutorial on developing large model applications for novice developers. It aims to provide a comprehensive introduction to large model development, focusing on Alibaba Cloud servers and integrating personal knowledge assistant projects. The tutorial covers the following topics: 1. **Introduction to Large Models**: A simplified introduction for novice developers on what large models are, their characteristics, what LangChain is, and how to develop an LLM application. 2. **How to Call Large Model APIs**: This section introduces various methods for calling APIs of well-known domestic and foreign large model products, including calling native APIs, encapsulating them as LangChain LLMs, and encapsulating them as Fastapi calls. It also provides a unified encapsulation for various large model APIs, such as Baidu Wenxin, Xunfei Xinghuo, and Zh譜AI. 3. **Knowledge Base Construction**: Loading, processing, and vector database construction of different types of knowledge base documents. 4. **Building RAG Applications**: Integrating LLM into LangChain to build a retrieval question and answer chain, and deploying applications using Streamlit. 5. **Verification and Iteration**: How to implement verification and iteration in large model development, and common evaluation methods. The project consists of three main parts: 1. **Introduction to LLM Development**: A simplified version of V1 aims to help beginners get started with LLM development quickly and conveniently, understand the general process of LLM development, and build a simple demo. 2. **LLM Development Techniques**: More advanced LLM development techniques, including but not limited to: Prompt Engineering, processing of multiple types of source data, optimizing retrieval, recall ranking, Agent framework, etc. 3. **LLM Application Examples**: Introduce some successful open source cases, analyze the ideas, core concepts, and implementation frameworks of these application examples from the perspective of this course, and help beginners understand what kind of applications they can develop through LLM. Currently, the first part has been completed, and everyone is welcome to read and learn; the second and third parts are under creation. **Directory Structure Description**: requirements.txt: Installation dependencies in the official environment notebook: Notebook source code file docs: Markdown documentation file figures: Pictures data_base: Knowledge base source file used

A repository sharing the panorama of the methodology literature on Transformer architecture upgrades in Large Language Models for handling extensive context windows, with real-time updating the newest published works. It includes a survey on advancing Transformer architecture in long-context large language models, flash-ReRoPE implementation, latest news on data engineering, lightning attention, Kimi AI assistant, chatglm-6b-128k, gpt-4-turbo-preview, benchmarks like InfiniteBench and LongBench, long-LLMs-evals for evaluating methods for enhancing long-context capabilities, and LLMs-learning for learning technologies and applicated tasks about Large Language Models.

Azure OpenAI Service provides REST API access to OpenAI's powerful language models including GPT-3, Codex and Embeddings. Users can easily adapt models for content generation, summarization, semantic search, and natural language to code translation. The workshop covers basics, prompt engineering, common NLP tasks, generative tasks, conversational dialog, and learning methods. It guides users to build applications with PowerApp, query SQL data, create data pipelines, and work with proprietary datasets. Target audience includes Power Users, Software Engineers, Data Scientists, and AI architects and Managers.

Combine the power of LLMs with Django's productivity to build intelligent applications. Let AI Assistants call methods from Django's side and do anything your users need! Use AI Tool Calling and RAG with Django to easily build state of the art AI Assistants.

DL3DV-10K is a large-scale dataset of real-world scene-level videos with annotations, covering diverse scenes with different levels of reflection, transparency, and lighting. It includes 10,510 multi-view scenes with 51.2 million frames at 4k resolution, and offers benchmark videos for novel view synthesis (NVS) methods. The dataset is designed to facilitate research in deep learning-based 3D vision and provides valuable insights for future research in NVS and 3D representation learning.

PEFT (Parameter-Efficient Fine-Tuning) is a collection of state-of-the-art methods that enable efficient adaptation of large pretrained models to various downstream applications. By only fine-tuning a small number of extra model parameters instead of all the model's parameters, PEFT significantly decreases the computational and storage costs while achieving performance comparable to fully fine-tuned models.

nb_utils is a Flutter package that provides a collection of useful methods, extensions, widgets, and utilities to simplify Flutter app development. It includes features like shared preferences, text styles, decorations, widgets, extensions for strings, colors, build context, date time, device, numbers, lists, scroll controllers, system methods, network utils, JWT decoding, and custom dialogs. The package aims to enhance productivity and streamline common tasks in Flutter development.

Crabml is a llama.cpp compatible AI inference engine written in Rust, designed for efficient inference on various platforms with WebGPU support. It focuses on running inference tasks with SIMD acceleration and minimal memory requirements, supporting multiple models and quantization methods. The project is hackable, embeddable, and aims to provide high-performance AI inference capabilities.

Jailbreak is a comprehensive guide exploring iOS 17 and its various versions, discussing the benefits, status, possibilities, and future impact of jailbreaking iOS devices. It covers topics such as preparation, safety measures, differences between tethered and untethered jailbreaks, best practices, and FAQs. The guide also provides information on specific jailbreak tools like Palera1n, Serotonin, NekoJB, Redensa, and Dopamine, along with their features and download links. Users can learn about supported devices, the latest updates, and the status of jailbreaking for different iOS versions. The tool aims to empower users to unlock new possibilities and customize their devices beyond Apple's restrictions.

This project explores the capability of Large Language Models (LLMs) to perform regression tasks using in-context examples. It compares the performance of LLMs like GPT-4 and Claude 3 Opus with traditional supervised methods such as Linear Regression and Gradient Boosting. The project provides preprints and results demonstrating the strong performance of LLMs in regression tasks. It includes datasets, models used, and experiments on adaptation and contamination. The code and data for the experiments are available for interaction and analysis.

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 |

Mergoo is a library for easily merging multiple LLM experts and efficiently training the merged LLM. With Mergoo, you can efficiently integrate the knowledge of different generic or domain-based LLM experts. Mergoo supports several merging methods, including Mixture-of-Experts, Mixture-of-Adapters, and Layer-wise merging. It also supports various base models, including LLaMa, Mistral, and BERT, and trainers, including Hugging Face Trainer, SFTrainer, and PEFT. Mergoo provides flexible merging for each layer and supports training choices such as only routing MoE layers or fully fine-tuning the merged LLM.

The 'bots' repository is a collection of guides, tools, and example bots for programming bots to play video games. It provides resources on running bots live, installing the BotLab client, debugging bots, testing bots in simulated environments, and more. The repository also includes example bots for games like EVE Online, Tribal Wars 2, and Elvenar. Users can learn about developing bots for specific games, syntax of the Elm programming language, and tools for memory reading development. Additionally, there are guides on bot programming, contributing to BotLab, and exploring Elm syntax and core library.

The Second Brain AI Agent Project is a tool designed to empower personal knowledge management by automatically indexing markdown files and links, providing a smart search engine powered by OpenAI, integrating seamlessly with different note-taking methods, and enhancing productivity by accessing information efficiently. The system is built on LangChain framework and ChromaDB vector store, utilizing a pipeline to process markdown files and extract text and links for indexing. It employs a Retrieval-augmented generation (RAG) process to provide context for asking questions to the large language model. The tool is beneficial for professionals, students, researchers, and creatives looking to streamline workflows, improve study sessions, delve deep into research, and organize thoughts and ideas effortlessly.

A pro level cuber who guides you end to end on how to solve a Rubik’s cube. Learn different methods as you level up. Just follow the algorithms!. Upload 6 faces of the cube onto SolveRubiks.

A GPT that explains research methods in a language that everyone can easily understand.

Enthusiastic, friendly cannabis expert here to personalize and improve your experience.

Leveraging some of the most esteemed and recognized instructional methods, my aim is to elevate your grasp of any concepts you're curious about!

I share hacking histories, not methods.

A therapist blending clinical methods with Eastern and Western philosophies.

A culinary expert on seafood cuisine, sharing cooking methods and recipes.

An informative guide on invasive species, their impact, and control methods.

I'm here to help you create screenplay ideas, outlines, and refinements using the tried and true methods developed by writers throughout history.

Chill and witty cannabis guide

Language Tutor using the Language Transfer Podcast Method.

If you crave a systematic approach to learning, I'm your Knowledge Architect. I'll navigate you through comprehensive knowledge hierarchies, step by step, in any subject you choose. Share this systematic learning method with your friends to elevate their learning experiences.

Babywise methodology advisor for parenting queries.

Experte für Agile Methoden inkl. aller Konversationen im Agile Focal Point Podcast.

Tell me the origin, processing method, and variety of a premium coffee that interests you, and I will provide you with some possible cupping notes about it

Assists in finding the perfect coffee grind size based on grinder and coffee.

I'm a humorous ML trainer who teaches with jokes and quizzes!

This GPT will help you learn Cajun French before it dies forever

Bilingual teacher fluent in English and Tagalog, focused on teaching Tagalog.

A personal guide to socially responsible and beneficial innovation