EZ-Work AI Document Translation is an AI document translation assistant accessible to everyone. It enables quick and cost-effective utilization of major language model APIs like OpenAI to translate documents in formats such as txt, word, csv, excel, pdf, and ppt. The tool supports AI translation for various document types, including pdf scanning, compatibility with OpenAI format endpoints via intermediary API, batch operations, multi-threading, and Docker deployment.

Xlings is a developer tool for programming learning, development, and course building. It provides features such as software installation, one-click environment setup, project dependency management, and cross-platform language package management. Additionally, it offers real-time compilation and running, AI code suggestions, tutorial project creation, automatic code checking for practice, and demo examples collection.

AI-Codereview-Gitlab is an automated code review tool based on large models, designed to help development teams conduct intelligent code reviews quickly during code merging or submission. It supports multiple large models including DeepSeek, ZhipuAI, OpenAI, and Ollama. The tool can automatically push review results to DingTalk, WeChat Work, and Feishu, generate daily reports based on GitLab commit records, and provide a visual dashboard to display code review records. The tool works by triggering webhook events on GitLab when users submit code, calling third-party large models to review the code, and recording the review results in corresponding Merge Requests or Commit Notes.

ZcChat is an AI desktop pet suitable for Galgame characters, featuring long-term memory, expressive actions, control over the computer, and voice functions. It utilizes Letta for AI long-term memory, Galgame-style character illustrations for more actions and expressions, and voice interaction with support for various voice synthesis tools like Vits. Users can configure characters, install Letta, set up voice synthesis and input, and control the pet to interact with the computer. The tool enhances visual and auditory experiences for users interested in AI desktop pets.

Aipan-Netdisk-Search is a free and open-source web project for searching netdisk resources. It utilizes third-party APIs with IP access restrictions, suggesting self-deployment. The project can be easily deployed on Vercel and provides instructions for manual deployment. Users can clone the project, install dependencies, run it in the browser, and access it at localhost:3001. The project also includes documentation for deploying on personal servers using NUXT.JS. Additionally, there are options for donations and communication via WeChat.

LLM-And-More is a one-stop solution for training and applying large models, covering the entire process from data processing to model evaluation, from training to deployment, and from idea to service. In this project, users can easily train models through this project and generate the required product services with one click.

Long-Novel-GPT is a long novel generator based on large language models like GPT. It utilizes a hierarchical outline/chapter/text structure to maintain the coherence of long novels. It optimizes API calls cost through context management and continuously improves based on self or user feedback until reaching the set goal. The tool aims to continuously refine and build novel content based on user-provided initial ideas, ultimately generating long novels at the level of human writers.

The langchain4j-aideepin-web repository is the frontend project of langchain4j-aideepin, an open-source, offline deployable retrieval enhancement generation (RAG) project based on large language models such as ChatGPT and application frameworks such as Langchain4j. It includes features like registration & login, multi-sessions (multi-roles), image generation (text-to-image, image editing, image-to-image), suggestions, quota control, knowledge base (RAG) based on large models, model switching, and search engine switching.

AivisSpeech is a Japanese text-to-speech software based on the VOICEVOX editor UI. It incorporates the AivisSpeech Engine for generating emotionally rich voices easily. It supports AIVMX format voice synthesis model files and specific model architectures like Style-Bert-VITS2. Users can download AivisSpeech and AivisSpeech Engine for Windows and macOS PCs, with minimum memory requirements specified. The development follows the latest version of VOICEVOX, focusing on minimal modifications, rebranding only where necessary, and avoiding refactoring. The project does not update documentation, maintain test code, or refactor unused features to prevent conflicts with VOICEVOX.

WeiXin-Dyh-AI is a backend management system that supports integrating WeChat subscription accounts with AI services. It currently supports integration with Ali AI, Moonshot, and Tencent Hyunyuan. Users can configure different AI models to simulate and interact with AI in multiple modes: text-based knowledge Q&A, text-to-image drawing, image description, text-to-voice conversion, enabling human-AI conversations on WeChat. The system allows hierarchical AI prompt settings at system, subscription account, and WeChat user levels. Users can configure AI model types, providers, and specific instances. The system also supports rules for allocating models and keys at different levels. It addresses limitations of WeChat's messaging system and offers features like text-based commands and voice support for interactions with AI.

XYBot V2 is a feature-rich WeChat robot framework that supports various interactive functions and gameplays. It provides AI chat, daily news updates, song requests, weather queries, and gaming functionalities like Gomoku and Warthunder player lookup. The tool is open-source and intended for learning and research purposes only, not for commercial or illegal activities. Users must comply with relevant laws and respect WeChat's copyrights and privacy. The tool's functionalities can be extended through a plugin system, allowing for dynamic loading/unloading of plugins.

prose-polish is a tool for AI interaction through drag-and-drop cards, focusing on editing copy and manuscripts. It can recognize Markdown-formatted documents, automatically breaking them into paragraph cards. Users can create prefabricated prompt cards and quickly connect them to the manuscript for editing. The modified manuscript is still presented in card form, allowing users to drag it out as a new paragraph. To use it smoothly, users just need to remember one rule: 'Plug the plug into the socket!'

Wiseflow is an agile information mining tool that utilizes the thinking and analysis capabilities of large models to accurately extract specific information from various given sources, without the need for manual intervention. The tool focuses on filtering noise from a vast amount of information to reveal valuable insights. It is recommended to use normal language models for information extraction tasks to optimize speed and cost, rather than complex reasoning models. The tool is designed for continuous information gathering based on specified focus points from various sources.

ERNIE SDK repository contains two projects: ERNIE Bot Agent and ERNIE Bot. ERNIE Bot Agent is a large model intelligent agent development framework based on the Wenxin large model orchestration capability introduced by Baidu PaddlePaddle, combined with the rich preset platform functions of the PaddlePaddle Star River community. ERNIE Bot provides developers with convenient interfaces to easily call the Wenxin large model for text creation, general conversation, semantic vectors, and AI drawing basic functions.

QFurina is a powerful and easily extensible Python QQ robot backend service that provides a range of automation and interactive features. It supports multiple messaging platforms and has a robust plugin system, allowing users to easily expand and customize functionality.

EZ-Work AI Document Translation is an AI document translation assistant accessible to everyone. It enables quick and cost-effective utilization of major language model APIs like OpenAI to translate documents in formats such as txt, word, csv, excel, pdf, and ppt. The tool supports AI translation for various document types, including pdf scanning, compatibility with OpenAI format endpoints via intermediary API, batch operations, multi-threading, and Docker deployment.

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

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

VectorVein is a no-code AI workflow software inspired by LangChain and langflow, aiming to combine the powerful capabilities of large language models and enable users to achieve intelligent and automated daily workflows through simple drag-and-drop actions. Users can create powerful workflows without the need for programming, automating all tasks with ease. The software allows users to define inputs, outputs, and processing methods to create customized workflow processes for various tasks such as translation, mind mapping, summarizing web articles, and automatic categorization of customer reviews.

Project Lakechain is a cloud-native, AI-powered framework for building document processing pipelines on AWS. It provides a composable API with built-in middlewares for common tasks, scalable architecture, cost efficiency, GPU and CPU support, and the ability to create custom transform middlewares. With ready-made examples and emphasis on modularity, Lakechain simplifies the deployment of scalable document pipelines for tasks like metadata extraction, NLP analysis, text summarization, translations, audio transcriptions, computer vision, and more.

AI-Office-Translator is a free, fully localized, user-friendly translation tool that helps you translate Office files (Word, PowerPoint, and Excel) between different languages. It supports .docx, .pptx, and .xlsx files and allows translation between English, Chinese, and Japanese. Users can run the tool after installing CUDA, downloading Ollama dependencies and models, setting up a virtual environment (optional), and installing requirements. The tool provides a UI where users can select languages, models, upload files for translation, start translation, and download translated files. It also supports an online mode with API key integration. The software is open-source under GPL-3.0 license and only provides AI translation services, with users expected to engage in legal translation activities.

Next-generation AI translation tool that provides high-quality, precise translations for various common file formats with a single click. It is based on cutting-edge large language models, offering exceptional translation quality with minimal operation, supporting multiple document formats and languages. Features include multi-format compatibility, global language translation, one-click rapid translation, flexible translation engines, and LAN sharing for efficient collaborative work.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

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.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.