Hujiang Dictionary is a tool that provides translation services between Japanese, Chinese, and English. It supports various translation modes such as Japanese to Chinese, Chinese to Japanese, English to Japanese, and more. The tool utilizes cloud services like Telegram, Lambda, and Cloudflare Workers for different deployment options. Users can interact with the tool via a command-line interface (CLI) to perform translations and access online resources like weblio and Google Translate. Additionally, the tool offers a Telegram bot for users to access translation services conveniently. The tool also supports setting up and managing databases for storing translation data.

LocalLLMClient is a Swift package designed to interact with local Large Language Models (LLMs) on Apple platforms. It supports GGUF, MLX models, and the FoundationModels framework, providing streaming API, multimodal capabilities, and tool calling functionalities. Users can easily integrate this tool to work with various models for text generation and processing. The package also includes advanced features for low-level API control and multimodal image processing. LocalLLMClient is experimental and subject to API changes, offering support for iOS, macOS, and Linux platforms.

Aiounifi is a Python library that provides a simple interface for interacting with the Unifi Controller API. It allows users to easily manage their Unifi network devices, such as access points, switches, and gateways, through automated scripts or applications. With Aiounifi, users can retrieve device information, perform configuration changes, monitor network performance, and more, all through a convenient and efficient API wrapper. This library simplifies the process of integrating Unifi network management into custom solutions, making it ideal for network administrators, developers, and enthusiasts looking to automate and streamline their network operations.

The AI21 Labs Python SDK is a comprehensive tool for interacting with the AI21 API. It provides functionalities for chat completions, conversational RAG, token counting, error handling, and support for various cloud providers like AWS, Azure, and Vertex. The SDK offers both synchronous and asynchronous usage, along with detailed examples and documentation. Users can quickly get started with the SDK to leverage AI21's powerful models for various natural language processing tasks.

SpecForge is a powerful tool for generating API specifications from code. It helps developers to easily create and maintain accurate API documentation by extracting information directly from the codebase. With SpecForge, users can streamline the process of documenting APIs, ensuring consistency and reducing manual effort. The tool supports various programming languages and frameworks, making it versatile and adaptable to different development environments. By automating the generation of API specifications, SpecForge enhances collaboration between developers and stakeholders, improving overall project efficiency and quality.

Ollama4j is a Java library that serves as a wrapper or binding for the Ollama server. It allows users to communicate with the Ollama server and manage models for various deployment scenarios. The library provides APIs for interacting with Ollama, generating fake data, testing UI interactions, translating messages, and building web UIs. Users can easily integrate Ollama4j into their Java projects to leverage the functionalities offered by the Ollama server.

The 'llm' package for Emacs provides an interface for interacting with Large Language Models (LLMs). It abstracts functionality to a higher level, concealing API variations and ensuring compatibility with various LLMs. Users can set up providers like OpenAI, Gemini, Vertex, Claude, Ollama, GPT4All, and a fake client for testing. The package allows for chat interactions, embeddings, token counting, and function calling. It also offers advanced prompt creation and logging capabilities. Users can handle conversations, create prompts with placeholders, and contribute by creating providers.

Langfuse Docs is a repository for langfuse.com, built on Nextra. It provides guidelines for contributing to the documentation using GitHub Codespaces and local development setup. The repository includes Python cookbooks in Jupyter notebooks format, which are converted to markdown for rendering on the site. It also covers media management for images, videos, and gifs. The stack includes Nextra, Next.js, shadcn/ui, and Tailwind CSS. Additionally, there is a bundle analysis feature to analyze the production build bundle size using @next/bundle-analyzer.

Arcade AI is a developer-focused tooling and API platform designed to enhance the capabilities of LLM applications and agents. It simplifies the process of connecting agentic applications with user data and services, allowing developers to concentrate on building their applications. The platform offers prebuilt toolkits for interacting with various services, supports multiple authentication providers, and provides access to different language models. Users can also create custom toolkits and evaluate their tools using Arcade AI. Contributions are welcome, and self-hosting is possible with the provided documentation.

The mcp-fundamentals repository is a collection of fundamental concepts and examples related to microservices, cloud computing, and DevOps. It covers topics such as containerization, orchestration, CI/CD pipelines, and infrastructure as code. The repository provides hands-on exercises and code samples to help users understand and apply these concepts in real-world scenarios. Whether you are a beginner looking to learn the basics or an experienced professional seeking to refresh your knowledge, mcp-fundamentals has something for everyone.

智元 Fast API is a one-stop API management system that unifies various LLM APIs in terms of format, standards, and management, achieving the ultimate in functionality, performance, and user experience. It supports various models from companies like OpenAI, Azure, Baidu, Keda Xunfei, Alibaba Cloud, Zhifu AI, Google, DeepSeek, 360 Brain, and Midjourney. The project provides user and admin portals for preview, supports cluster deployment, multi-site deployment, and cross-zone deployment. It also offers Docker deployment, a public API site for registration, and screenshots of the admin and user portals. The API interface is similar to OpenAI's interface, and the project is open source with repositories for API, web, admin, and SDK on GitHub and Gitee.

Baibot is a versatile chatbot framework designed to simplify the process of creating and deploying chatbots. It provides a user-friendly interface for building custom chatbots with various functionalities such as natural language processing, conversation flow management, and integration with external APIs. Baibot is highly customizable and can be easily extended to suit different use cases and industries. With Baibot, developers can quickly create intelligent chatbots that can interact with users in a seamless and engaging manner, enhancing user experience and automating customer support processes.

Strands Agents Tools is a community-driven project that provides a powerful set of tools for your agents to use. It bridges the gap between large language models and practical applications by offering ready-to-use tools for file operations, system execution, API interactions, mathematical operations, and more. The tools cover a wide range of functionalities including file operations, shell integration, memory storage, web infrastructure, HTTP client, Slack client, Python execution, mathematical tools, AWS integration, image and video processing, audio output, environment management, task scheduling, advanced reasoning, swarm intelligence, dynamic MCP client, parallel tool execution, browser automation, diagram creation, RSS feed management, and computer automation.

Chatluna is a machine learning model plugin that provides chat services with large language models. It is highly extensible, supports multiple output formats, and offers features like custom conversation presets, rate limiting, and context awareness. Users can deploy Chatluna under Koishi without additional configuration. The plugin supports various models/platforms like OpenAI, Azure OpenAI, Google Gemini, and more. It also provides preset customization using YAML files and allows for easy forking and development within Koishi projects. However, the project lacks web UI, HTTP server, and project documentation, inviting contributions from the community.

Cellm is an Excel extension that allows users to leverage Large Language Models (LLMs) like ChatGPT within cell formulas. It enables users to extract AI responses to text ranges, making it useful for automating repetitive tasks that involve data processing and analysis. Cellm supports various models from Anthropic, Mistral, OpenAI, and Google, as well as locally hosted models via Llamafiles, Ollama, or vLLM. The tool is designed to simplify the integration of AI capabilities into Excel for tasks such as text classification, data cleaning, content summarization, entity extraction, and more.

Gorilla is a tool that enables LLMs to use tools by invoking APIs. Given a natural language query, Gorilla comes up with the semantically- and syntactically- correct API to invoke. With Gorilla, you can use LLMs to invoke 1,600+ (and growing) API calls accurately while reducing hallucination. Gorilla also releases APIBench, the largest collection of APIs, curated and easy to be trained on!

Langflow is an open-source Python-powered visual framework designed for building multi-agent and RAG applications. It is fully customizable, language model agnostic, and vector store agnostic. Users can easily create flows by dragging components onto the canvas, connect them, and export the flow as a JSON file. Langflow also provides a command-line interface (CLI) for easy management and configuration, allowing users to customize the behavior of Langflow for development or specialized deployment scenarios. The tool can be deployed on various platforms such as Google Cloud Platform, Railway, and Render. Contributors are welcome to enhance the project on GitHub by following the contributing guidelines.

AI-video-boilerplate-simple is a free Live AI Video boilerplate for testing out live video AI experiments. It includes a simple Flask server that serves files, supports live video from various sources, and integrates with Roboflow for AI vision. Users can use this template for projects, research, business ideas, and homework. It is lightweight and can be deployed on popular cloud platforms like Replit, Vercel, Digital Ocean, or Heroku.

Aspire AI Chat is a full-stack chat sample that combines modern technologies to deliver a ChatGPT-like experience. The backend API is built with ASP.NET Core and interacts with an LLM using Microsoft.Extensions.AI. It uses Entity Framework Core with CosmosDB for flexible, cloud-based NoSQL storage. The AI capabilities include using Ollama for local inference and switching to Azure OpenAI in production. The frontend UI is built with React, offering a modern and interactive chat experience.

Hujiang Dictionary is a tool that provides translation services between Japanese, Chinese, and English. It supports various translation modes such as Japanese to Chinese, Chinese to Japanese, English to Japanese, and more. The tool utilizes cloud services like Telegram, Lambda, and Cloudflare Workers for different deployment options. Users can interact with the tool via a command-line interface (CLI) to perform translations and access online resources like weblio and Google Translate. Additionally, the tool offers a Telegram bot for users to access translation services conveniently. The tool also supports setting up and managing databases for storing translation data.

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Chat2DB is an AI-driven data development and analysis platform that enables users to communicate with databases using natural language. It supports a wide range of databases, including MySQL, PostgreSQL, Oracle, SQLServer, SQLite, MariaDB, ClickHouse, DM, Presto, DB2, OceanBase, Hive, KingBase, MongoDB, Redis, and Snowflake. Chat2DB provides a user-friendly interface that allows users to query databases, generate reports, and explore data using natural language commands. It also offers a variety of features to help users improve their productivity, such as auto-completion, syntax highlighting, and error checking.

Greenplum Database (GPDB) is an advanced, fully featured, open source data warehouse, based on PostgreSQL. It provides powerful and rapid analytics on petabyte scale data volumes. Uniquely geared toward big data analytics, Greenplum Database is powered by the world’s most advanced cost-based query optimizer delivering high analytical query performance on large data volumes.

Tau is a framework for building low maintenance & highly scalable cloud computing platforms that software developers will love. It aims to solve the high cost and time required to build, deploy, and scale software by providing a developer-friendly platform that offers autonomy and flexibility. Tau simplifies the process of building and maintaining a cloud computing platform, enabling developers to achieve 'Local Coding Equals Global Production' effortlessly. With features like auto-discovery, content-addressing, and support for WebAssembly, Tau empowers users to create serverless computing environments, host frontends, manage databases, and more. The platform also supports E2E testing and can be extended using a plugin system called orbit.

AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

This repository contains shared codebase for two projects: gcloud-aio and gcloud-rest. gcloud-aio is built for Python 3's asyncio, while gcloud-rest is a threadsafe requests-based implementation. It provides clients for Google Cloud services like Auth, BigQuery, Datastore, KMS, PubSub, Storage, and Task Queue. Users can install the library using pip and refer to the documentation for usage details. Developers can contribute to the project by following the contribution guide.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

AIGES is a core component of the Athena Serving Framework, designed as a universal encapsulation tool for AI developers to deploy AI algorithm models and engines quickly. By integrating AIGES, you can deploy AI algorithm models and engines rapidly and host them on the Athena Serving Framework, utilizing supporting auxiliary systems for networking, distribution strategies, data processing, etc. The Athena Serving Framework aims to accelerate the cloud service of AI algorithm models and engines, providing multiple guarantees for cloud service stability through cloud-native architecture. You can efficiently and securely deploy, upgrade, scale, operate, and monitor models and engines without focusing on underlying infrastructure and service-related development, governance, and operations.