GPT Academic is a powerful tool that leverages the capabilities of large language models (LLMs) to enhance academic research and writing. It provides a user-friendly interface that allows researchers, students, and professionals to interact with LLMs and utilize their abilities for various academic tasks. With GPT Academic, users can access a wide range of features and functionalities, including: * **Summarization and Paraphrasing:** GPT Academic can summarize complex texts, articles, and research papers into concise and informative summaries. It can also paraphrase text to improve clarity and readability. * **Question Answering:** Users can ask GPT Academic questions related to their research or studies, and the tool will provide comprehensive and well-informed answers based on its knowledge and understanding of the relevant literature. * **Code Generation and Explanation:** GPT Academic can generate code snippets and provide explanations for complex coding concepts. It can also help debug code and suggest improvements. * **Translation:** GPT Academic supports translation of text between multiple languages, making it a valuable tool for researchers working with international collaborations or accessing resources in different languages. * **Citation and Reference Management:** GPT Academic can help users manage their citations and references by automatically generating citations in various formats and providing suggestions for relevant references based on the user's research topic. * **Collaboration and Note-Taking:** GPT Academic allows users to collaborate on projects and take notes within the tool. They can share their work with others and access a shared workspace for real-time collaboration. * **Customizable Interface:** GPT Academic offers a customizable interface that allows users to tailor the tool to their specific needs and preferences. They can choose from a variety of themes, adjust the layout, and add or remove features to create a personalized workspace. Overall, GPT Academic is a versatile and powerful tool that can significantly enhance the productivity and efficiency of academic research and writing. It empowers users to leverage the capabilities of LLMs and unlock new possibilities for academic exploration and knowledge creation.

MediaAI is a repository containing lectures and materials for Aalto University's AI for Media, Art & Design course. The course is a hands-on, project-based crash course focusing on deep learning and AI techniques for artists and designers. It covers common AI algorithms & tools, their applications in art, media, and design, and provides hands-on practice in designing, implementing, and using these tools. The course includes lectures, exercises, and a final project based on students' interests. Students can complete the course without programming by creatively utilizing existing tools like ChatGPT and DALL-E. The course emphasizes collaboration, peer-to-peer tutoring, and project-based learning. It covers topics such as text generation, image generation, optimization, and game AI.

This public repository contains API, tools, and packages for Datagrok, a web-based data analytics platform. It offers support for scientific domains, applications, connectors to web services, visualizations, file importing, scientific methods in R, Python, or Julia, file metadata extractors, custom predictive models, platform enhancements, and more. The open-source packages are free to use, with restrictions on server computational capacities for the public environment. Academic institutions can use Datagrok for research and education, benefiting from reproducible and scalable computations and data augmentation capabilities. Developers can contribute by creating visualizations, scientific methods, file editors, connectors to web services, and more.

This repository provides sample code for improving RAG applications with rich data sources including SQL Warehouses and documents analysed with Azure Document Intelligence. It includes components for Text2SQL generation and querying, linking Azure Document Intelligence with AI Search for processing complex documents, and deploying AI search indexes. The plugins and skills aim to enhance response quality in RAG applications by accessing and pulling data from SQL tables, drawing insights from complex charts and images, and intelligently grouping similar sentences.

RecAI is a project that explores the integration of Large Language Models (LLMs) into recommender systems, addressing the challenges of interactivity, explainability, and controllability. It aims to bridge the gap between general-purpose LLMs and domain-specific recommender systems, providing a holistic perspective on the practical requirements of LLM4Rec. The project investigates various techniques, including Recommender AI agents, selective knowledge injection, fine-tuning language models, evaluation, and LLMs as model explainers, to create more sophisticated, interactive, and user-centric recommender systems.

The Jentic Public APIs repository aims to collate all knowledge about the world's APIs into a detailed, comprehensive, structured documentation catalog designed for use by AI. It focuses on standardized OpenAPI specifications, Arazzo workflows, associated tooling, evaluations, and RFCs for extensions to open formats. The project is in ALPHA stage and welcomes contributions to accelerate the effort of building an open knowledge foundation for AI agents.

Knowledge is a tool for saving, searching, accessing, exploring and chatting with all of your favorite websites, documents and files. Dive into a more interactive learning experience with Knowledge's new Chat feature! Engage in dynamic conversations with your Projects and Sources, leveraging the power of Large Language Models. The Chat feature is designed to transform the way you interact with your data, offering a more engaging and exploratory approach to learning. Unleash the power of context with the built-in Chromium browser. Transform your browsing into knowledge gathering effortlessly.

Motleycrew is an ultimate framework for building multi-agent AI systems, allowing users to mix and match AI agents and tools from popular frameworks, design advanced workflows, and leverage dynamic knowledge graphs with simplicity and elegance. It acts as a conductor orchestrating a symphony of AI agents and tools, providing building blocks for creating AI systems and enabling users to focus on high-level design while taking care of the rest. The framework offers integration with various tools, flexibility in providing agents with tools or other agents, advanced flow design capabilities, and built-in observability and caching features.

The Document Knowledge Mining Solution Accelerator leverages Azure OpenAI and Azure AI Document Intelligence to ingest, extract, and classify content from various assets, enabling chat-based insight discovery, analysis, and prompt guidance. It uses OCR and multi-modal LLM to extract information from documents like text, handwritten text, charts, graphs, tables, and form fields. Users can customize the technical architecture and data processing workflow. Key features include ingesting and extracting real-world entities, chat-based insights discovery, text and document data analysis, prompt suggestion guidance, and multi-modal information processing.

This repository provides a sample solution for building generative AI agents using Amazon Bedrock, Amazon DynamoDB, Amazon Kendra, Amazon Lex, and LangChain. The solution creates a generative AI financial services agent capable of assisting users with account information, loan applications, and answering natural language questions. It serves as a launchpad for developers to create personalized conversational agents for applications like chatbots and virtual assistants.

FoundationaLLM is a platform designed for deploying, scaling, securing, and governing generative AI in enterprises. It allows users to create AI agents grounded in enterprise data, integrate REST APIs, experiment with various large language models, centrally manage AI agents and their assets, deploy scalable vectorization data pipelines, enable non-developer users to create their own AI agents, control access with role-based access controls, and harness capabilities from Azure AI and Azure OpenAI. The platform simplifies integration with enterprise data sources, provides fine-grain security controls, scalability, extensibility, and addresses the challenges of delivering enterprise copilots or AI agents.

AI Hub is a comprehensive solution that leverages artificial intelligence and cloud computing to provide functionalities such as document search and retrieval, call center analytics, image analysis, brand reputation analysis, form analysis, document comparison, and content safety moderation. It integrates various Azure services like Cognitive Search, ChatGPT, Azure Vision Services, and Azure Document Intelligence to offer scalable, extensible, and secure AI-powered capabilities for different use cases and scenarios.

FoundationaLLM is a platform designed for deploying, scaling, securing, and governing generative AI in enterprises. It allows users to create AI agents grounded in enterprise data, integrate REST APIs, experiment with large language models, centrally manage AI agents and assets, deploy scalable vectorization data pipelines, enable non-developer users to create their own AI agents, control access with role-based access controls, and harness capabilities from Azure AI and Azure OpenAI. The platform simplifies integration with enterprise data sources, provides fine-grain security controls, load balances across multiple endpoints, and is extensible to new data sources and orchestrators. FoundationaLLM addresses the need for customized copilots or AI agents that are secure, licensed, flexible, and suitable for enterprise-scale production.

PythonDataScienceFullThrottle is a comprehensive repository containing various Python scripts, libraries, and tools for data science enthusiasts. It includes a wide range of functionalities such as data preprocessing, visualization, machine learning algorithms, and statistical analysis. The repository aims to provide a one-stop solution for individuals looking to dive deep into the world of data science using Python.

The Electronic Component Classifier is a project that uses machine learning and artificial intelligence to automate the identification and classification of electrical and electronic components. It features component classification into seven classes, user-friendly design, and integration with Flask for a user-friendly interface. The project aims to reduce human error in component identification, make the process safer and more reliable, and potentially help visually impaired individuals in identifying electronic components.

AI Gallery is a showcase site built using React and Nextjs for static site generation, featuring interactive visualizations of classic algorithms, classic games implementation, and various interesting widgets. The project utilizes AI assistance from Claude 3.5 and GPT-4 to create components and enhance the development process. It aims to continually add more components with AI assistance, providing a platform for contributors to leverage AI in frontend development.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

ONNX Runtime Generative AI is a library that provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management. Users can call a high level `generate()` method, or run each iteration of the model in a loop. It supports greedy/beam search and TopP, TopK sampling to generate token sequences, has built in logits processing like repetition penalties, and allows for easy custom scoring.

Jupyter AI connects generative AI with Jupyter notebooks. It provides a user-friendly and powerful way to explore generative AI models in notebooks and improve your productivity in JupyterLab and the Jupyter Notebook. Specifically, Jupyter AI offers: * An `%%ai` magic that turns the Jupyter notebook into a reproducible generative AI playground. This works anywhere the IPython kernel runs (JupyterLab, Jupyter Notebook, Google Colab, Kaggle, VSCode, etc.). * A native chat UI in JupyterLab that enables you to work with generative AI as a conversational assistant. * Support for a wide range of generative model providers, including AI21, Anthropic, AWS, Cohere, Gemini, Hugging Face, NVIDIA, and OpenAI. * Local model support through GPT4All, enabling use of generative AI models on consumer grade machines with ease and privacy.

Khoj is an open-source, personal AI assistant that extends your capabilities by creating always-available AI agents. You can share your notes and documents to extend your digital brain, and your AI agents have access to the internet, allowing you to incorporate real-time information. Khoj is accessible on Desktop, Emacs, Obsidian, Web, and Whatsapp, and you can share PDF, markdown, org-mode, notion files, and GitHub repositories. You'll get fast, accurate semantic search on top of your docs, and your agents can create deeply personal images and understand your speech. Khoj is self-hostable and always will be.

LangChain.dart is a Dart port of the popular LangChain Python framework created by Harrison Chase. LangChain provides a set of ready-to-use components for working with language models and a standard interface for chaining them together to formulate more advanced use cases (e.g. chatbots, Q&A with RAG, agents, summarization, extraction, etc.). The components can be grouped into a few core modules: * **Model I/O:** LangChain offers a unified API for interacting with various LLM providers (e.g. OpenAI, Google, Mistral, Ollama, etc.), allowing developers to switch between them with ease. Additionally, it provides tools for managing model inputs (prompt templates and example selectors) and parsing the resulting model outputs (output parsers). * **Retrieval:** assists in loading user data (via document loaders), transforming it (with text splitters), extracting its meaning (using embedding models), storing (in vector stores) and retrieving it (through retrievers) so that it can be used to ground the model's responses (i.e. Retrieval-Augmented Generation or RAG). * **Agents:** "bots" that leverage LLMs to make informed decisions about which available tools (such as web search, calculators, database lookup, etc.) to use to accomplish the designated task. The different components can be composed together using the LangChain Expression Language (LCEL).

Danswer is an open-source Gen-AI Chat and Unified Search tool that connects to your company's docs, apps, and people. It provides a Chat interface and plugs into any LLM of your choice. Danswer can be deployed anywhere and for any scale - on a laptop, on-premise, or to cloud. Since you own the deployment, your user data and chats are fully in your own control. Danswer is MIT licensed and designed to be modular and easily extensible. The system also comes fully ready for production usage with user authentication, role management (admin/basic users), chat persistence, and a UI for configuring Personas (AI Assistants) and their Prompts. Danswer also serves as a Unified Search across all common workplace tools such as Slack, Google Drive, Confluence, etc. By combining LLMs and team specific knowledge, Danswer becomes a subject matter expert for the team. Imagine ChatGPT if it had access to your team's unique knowledge! It enables questions such as "A customer wants feature X, is this already supported?" or "Where's the pull request for feature Y?"

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

mikupad is a lightweight and efficient language model front-end powered by ReactJS, all packed into a single HTML file. Inspired by the likes of NovelAI, it provides a simple yet powerful interface for generating text with the help of various backends.

这是一个程序员关于 ChatGPT 学习过程的记录，其中包括了 ChatGPT 的使用技巧、相关工具和资源的整理，以及一些个人见解和思考。 **使用技巧** * **充值 OpenAI API**：可以通过 https://beta.openai.com/account/api-keys 进行充值，支持信用卡和 PayPal。 * **使用专梯**：推荐使用稳定的专梯，可以有效提高 ChatGPT 的访问速度和稳定性。 * **使用魔法**：可以通过 https://my.x-air.app:666/#/register?aff=32853 访问 ChatGPT，无需魔法即可访问。 * **下载各种 apk**：可以通过 https://apkcombo.com 下载各种安卓应用的 apk 文件。 * **ChatGPT 官网**：ChatGPT 的官方网站是 https://ai.com。 * **Midjourney**：Midjourney 是一个生成式 AI 图像平台，可以通过 https://midjourney.com 访问。 * **文本转视频**：可以通过 https://www.d-id.com 将文本转换为视频。 * **国内大模型**：国内也有很多大模型，如阿里巴巴的通义千问、百度文心一言、讯飞星火、阿里巴巴通义听悟等。 * **查看 OpenAI 状态**：可以通过 https://status.openai.com/ 查看 OpenAI 的服务状态。 * **Canva 画图**：Canva 是一个在线平面设计平台，可以通过 https://www.canva.cn 进行画图。 **相关工具和资源** * **文字转语音**：可以通过 https://modelscope.cn/models?page=1&tasks=text-to-speech&type=audio 找到文字转语音的模型。 * **可好好玩玩的项目**： * https://github.com/sunner/ChatALL * https://github.com/labring/FastGPT * https://github.com/songquanpeng/one-api * **个人博客**： * https://baoyu.io/ * https://gorden-sun.notion.site/527689cd2b294e60912f040095e803c5?v=4f6cc12006c94f47aee4dc909511aeb5 * **srt 2 lrc 歌词**：可以通过 https://gotranscript.com/subtitle-converter 将 srt 格式的字幕转换为 lrc 格式的歌词。 * **5 种速率限制**：OpenAI API 有 5 种速率限制：RPM（每分钟请求数）、RPD（每天请求数）、TPM（每分钟 tokens 数量）、TPD（每天 tokens 数量）、IPM（每分钟图像数量）。 * **扣子平台**：coze.cn 是一个扣子平台，可以提供各种扣子。 * **通过云函数免费使用 GPT-3.5**：可以通过 https://juejin.cn/post/7353849549540589587 免费使用 GPT-3.5。 * **不蒜子 统计网页基数**：可以通过 https://busuanzi.ibruce.info/ 统计网页的基数。 * **视频总结和翻译网页**：可以通过 https://glarity.app/zh-CN 总结和翻译视频。 * **视频翻译和配音工具**：可以通过 https://github.com/jianchang512/pyvideotrans 翻译和配音视频。 * **文字生成音频**：可以通过 https://www.cnblogs.com/jijunjian/p/18118366 将文字生成音频。 * **memo ai**：memo.ac 是一个多模态 AI 平台，可以将视频链接、播客链接、本地音视频转换为文字，支持多语言转录后翻译，还可以将文字转换为新的音频。 * **视频总结工具**：可以通过 https://summarize.ing/ 总结视频。 * **可每天免费玩玩**：可以通过 https://www.perplexity.ai/ 每天免费玩玩。 * **Suno.ai**：Suno.ai 是一个 AI 语言模型，可以通过 https://bibigpt.co/ 访问。 * **CapCut**：CapCut 是一个视频编辑软件，可以通过 https://www.capcut.cn/ 下载。 * **Valla.ai**：Valla.ai 是一个多模态 AI 模型，可以通过 https://www.valla.ai/ 访问。 * **Viggle.ai**：Viggle.ai 是一个 AI 视频生成平台，可以通过 https://viggle.ai 访问。 * **使用免费的 GPU 部署文生图大模型**：可以通过 https://www.cnblogs.com/xuxiaona/p/18088404 部署文生图大模型。 * **语音转文字**：可以通过 https://speech.microsoft.com/portal 将语音转换为文字。 * **投资界的 ai**：可以通过 https://reportify.cc/ 了解投资界的 ai。 * **抓取小视频 app 的各种信息**：可以通过 https://github.com/NanmiCoder/MediaCrawler 抓取小视频 app 的各种信息。 * **马斯克 Grok1 开源**：马斯克的 Grok1 模型已经开源，可以通过 https://github.com/xai-org/grok-1 访问。 * **ChatALL**：ChatALL 是一个跨端支持的聊天机器人，可以通过 https://github.com/sunner/ChatALL 访问。 * **零一万物**：零一万物是一个 AI 平台，可以通过 https://www.01.ai/cn 访问。 * **智普**：智普是一个 AI 语言模型，可以通过 https://chatglm.cn/ 访问。 * **memo ai 下载**：可以通过 https://memo.ac/ 下载 memo ai。 * **ffmpeg 学习**：可以通过 https://www.ruanyifeng.com/blog/2020/01/ffmpeg.html 学习 ffmpeg。 * **自动生成文章小工具**：可以通过 https://www.cognition-labs.com/blog 生成文章。 * **简易商城**：可以通过 https://www.cnblogs.com/whuanle/p/18086537 搭建简易商城。 * **物联网**：可以通过 https://www.cnblogs.com/xuxiaona/p/18088404 学习物联网。 * **自定义表单、自定义列表、自定义上传和下载、自定义流程、自定义报表**：可以通过 https://www.cnblogs.com/whuanle/p/18086537 实现自定义表单、自定义列表、自定义上传和下载、自定义流程、自定义报表。 **个人见解和思考** * ChatGPT 是一个强大的工具，可以用来提高工作效率和创造力。 * ChatGPT 的使用门槛较低，即使是非技术人员也可以轻松上手。 * ChatGPT 的发展速度非常快，未来可能会对各个行业产生深远的影响。 * 我们应该理性看待 ChatGPT，既要看到它的优点，也要意识到它的局限性。 * 我们应该积极探索 ChatGPT 的应用场景，为社会创造价值。

Tiny-Predictive-Text is a demonstration of predictive text without an LLM, using permy.link. It provides a detailed description of the tool, including its features, benefits, and how to use it. The tool is suitable for a variety of jobs, including content writers, editors, and researchers. It can be used to perform a variety of tasks, such as generating text, completing sentences, and correcting errors.

Clipboard Conqueror is a multi-platform omnipresent copilot alternative. Currently requiring a kobold united or openAI compatible back end, this software brings powerful LLM based tools to any text field, the universal copilot you deserve. It simply works anywhere. No need to sign in, no required key. Provided you are using local AI, CC is a data secure alternative integration provided you trust whatever backend you use. *Special thank you to the creators of KoboldAi, KoboldCPP, llamma, openAi, and the communities that made all this possible to figure out.

MouseTooltipTranslator is a Chrome extension that allows users to translate any text on a webpage by simply hovering over it. It supports both Google Translate and Bing Translate, and can also be used to listen to the pronunciation of words and phrases. Additionally, the extension can be used to translate text in input boxes and highlighted text, and to display translated tooltips for PDFs and YouTube videos. It also supports OCR, allowing users to translate text in images by holding down the left shift key and hovering over the image.

Dodari is a local web service that makes NHNDQ AI Korean-English/English-Korean translation easy for everyday people to use (based on Gradio).

GPT Academic is a powerful tool that leverages the capabilities of large language models (LLMs) to enhance academic research and writing. It provides a user-friendly interface that allows researchers, students, and professionals to interact with LLMs and utilize their abilities for various academic tasks. With GPT Academic, users can access a wide range of features and functionalities, including: * **Summarization and Paraphrasing:** GPT Academic can summarize complex texts, articles, and research papers into concise and informative summaries. It can also paraphrase text to improve clarity and readability. * **Question Answering:** Users can ask GPT Academic questions related to their research or studies, and the tool will provide comprehensive and well-informed answers based on its knowledge and understanding of the relevant literature. * **Code Generation and Explanation:** GPT Academic can generate code snippets and provide explanations for complex coding concepts. It can also help debug code and suggest improvements. * **Translation:** GPT Academic supports translation of text between multiple languages, making it a valuable tool for researchers working with international collaborations or accessing resources in different languages. * **Citation and Reference Management:** GPT Academic can help users manage their citations and references by automatically generating citations in various formats and providing suggestions for relevant references based on the user's research topic. * **Collaboration and Note-Taking:** GPT Academic allows users to collaborate on projects and take notes within the tool. They can share their work with others and access a shared workspace for real-time collaboration. * **Customizable Interface:** GPT Academic offers a customizable interface that allows users to tailor the tool to their specific needs and preferences. They can choose from a variety of themes, adjust the layout, and add or remove features to create a personalized workspace. Overall, GPT Academic is a versatile and powerful tool that can significantly enhance the productivity and efficiency of academic research and writing. It empowers users to leverage the capabilities of LLMs and unlock new possibilities for academic exploration and knowledge creation.

Chinese-LLaMA-Alpaca-2 is a large Chinese language model developed by Meta AI. It is based on the Llama-2 model and has been further trained on a large dataset of Chinese text. Chinese-LLaMA-Alpaca-2 can be used for a variety of natural language processing tasks, including text generation, question answering, and machine translation. Here are some of the key features of Chinese-LLaMA-Alpaca-2: * It is the largest Chinese language model ever trained, with 13 billion parameters. * It is trained on a massive dataset of Chinese text, including books, news articles, and social media posts. * It can be used for a variety of natural language processing tasks, including text generation, question answering, and machine translation. * It is open-source and available for anyone to use. Chinese-LLaMA-Alpaca-2 is a powerful tool that can be used to improve the performance of a wide range of natural language processing tasks. It is a valuable resource for researchers and developers working in the field of artificial intelligence.