A project that utilizes large models to help with Code Review on Gitlab, aimed at improving development efficiency. The project is customized for Gitlab and is developing a Multi-Agent plugin for collaborative review. It integrates various large models for code security issues and stays updated with the latest Code Review trends. The project architecture is designed to be powerful, flexible, and efficient, with easy integration of different models and high customization for developers.

SwanLab is an open-source, lightweight AI experiment tracking tool that provides a platform for tracking, comparing, and collaborating on experiments, aiming to accelerate the research and development efficiency of AI teams by 100 times. It offers a friendly API and a beautiful interface, combining hyperparameter tracking, metric recording, online collaboration, experiment link sharing, real-time message notifications, and more. With SwanLab, researchers can document their training experiences, seamlessly communicate and collaborate with collaborators, and machine learning engineers can develop models for production faster.

Higress is an open-source cloud-native API gateway built on the core of Istio and Envoy, based on Alibaba's internal practice of Envoy Gateway. It is designed for AI-native API gateway, serving AI businesses such as Tongyi Qianwen APP, Bailian Big Model API, and Machine Learning PAI platform. Higress provides capabilities to interface with LLM model vendors, AI observability, multi-model load balancing/fallback, AI token flow control, and AI caching. It offers features for AI gateway, Kubernetes Ingress gateway, microservices gateway, and security protection gateway, with advantages in production-level scalability, stream processing, extensibility, and ease of use.

HaE is a framework project in the field of network security (data security) that combines artificial intelligence (AI) large models to achieve highlighting and information extraction of HTTP messages (including WebSocket). It aims to reduce testing time, focus on valuable and meaningful messages, and improve vulnerability discovery efficiency. The project provides a clear and visual interface design, simple interface interaction, and centralized data panel for querying and extracting information. It also features built-in color upgrade algorithm, one-click export/import of data, and integration of AI large models API for optimized data processing.

KouriChat is a project that seamlessly integrates virtual and real interactions, providing eternal gentle bonds. It offers features like WeChat integration, immersive role-playing, intelligent conversation segmentation, emotion-based emojis, image generation, image recognition, voice messages, and more. The project is focused on technical research and learning exchanges, with a strong emphasis on ethical and legal guidelines. Users are required to take full responsibility for their actions, especially minors who should use the tool under supervision. The project architecture includes avatar configurations, data storage, handlers, AI service interfaces, a web UI, and utility libraries.

Bailing is an open-source voice assistant designed for natural conversations with users. It combines Automatic Speech Recognition (ASR), Voice Activity Detection (VAD), Large Language Model (LLM), and Text-to-Speech (TTS) technologies to provide a high-quality voice interaction experience similar to GPT-4o. Bailing aims to achieve GPT-4o-like conversation effects without the need for GPU, making it suitable for various edge devices and low-resource environments. The project features efficient open-source models, modular design allowing for module replacement and upgrades, support for memory function, tool integration for information retrieval and task execution via voice commands, and efficient task management with progress tracking and reminders.

llm-resource is a comprehensive collection of high-quality resources for Large Language Models (LLM). It covers various aspects of LLM including algorithms, training, fine-tuning, alignment, inference, data engineering, compression, evaluation, prompt engineering, AI frameworks, AI basics, AI infrastructure, AI compilers, LLM application development, LLM operations, AI systems, and practical implementations. The repository aims to gather and share valuable resources related to LLM for the community to benefit from.

Awesome-LLM-RAG-Application is a repository that provides resources and information about applications based on Large Language Models (LLM) with Retrieval-Augmented Generation (RAG) pattern. It includes a survey paper, GitHub repo, and guides on advanced RAG techniques. The repository covers various aspects of RAG, including academic papers, evaluation benchmarks, downstream tasks, tools, and technologies. It also explores different frameworks, preprocessing tools, routing mechanisms, evaluation frameworks, embeddings, security guardrails, prompting tools, SQL enhancements, LLM deployment, observability tools, and more. The repository aims to offer comprehensive knowledge on RAG for readers interested in exploring and implementing LLM-based systems and products.

Chuanhu Chat is a user-friendly web graphical interface that provides various additional features for ChatGPT and other language models. It supports GPT-4, file-based question answering, local deployment of language models, online search, agent assistant, and fine-tuning. The tool offers a range of functionalities including auto-solving questions, online searching with network support, knowledge base for quick reading, local deployment of language models, GPT 3.5 fine-tuning, and custom model integration. It also features system prompts for effective role-playing, basic conversation capabilities with options to regenerate or delete dialogues, conversation history management with auto-saving and search functionalities, and a visually appealing user experience with themes, dark mode, LaTeX rendering, and PWA application support.

KubeDoor is a microservice resource management platform developed using Python and Vue, based on K8S admission control mechanism. It supports unified remote storage, monitoring, alerting, notification, and display for multiple K8S clusters. The platform focuses on resource analysis and control during daily peak hours of microservices, ensuring consistency between resource request rate and actual usage rate.

uDesktopMascot is an open-source project for a desktop mascot application with a theme of 'freedom of creation'. It allows users to load and display VRM or GLB/FBX model files on the desktop, customize GUI colors and background images, and access various features through a menu screen. The application supports Windows 10/11 and macOS platforms.

Gez is a high-performance micro frontend framework based on ESM. It uses Rspack compilation and maps modules to URLs with strong caching and content-based hashing. Gez embraces modern micro frontend architecture by leveraging ESM and importmap for dependency management, providing reliable isolation with module scope, seamless integration with any modern frontend framework, intuitive development experience, and optimal performance with zero runtime overhead and reliable caching strategies.

AnyLabeling is a tool for effortless data labeling with AI support from YOLO and Segment Anything. It combines features from LabelImg and Labelme with an improved UI and auto-labeling capabilities. Users can annotate images with polygons, rectangles, circles, lines, and points, as well as perform auto-labeling using YOLOv5 and Segment Anything. The tool also supports text detection, recognition, and Key Information Extraction (KIE) labeling, with multiple language options available such as English, Vietnamese, and Chinese.

LotteryMaster is a tool designed to fetch lottery data, save it to Excel files, and provide analysis reports including number prediction, number recommendation, and number trends. It supports multiple platforms for access such as Web and mobile App. The tool integrates AI models like Qwen API and DeepSeek for generating analysis reports and trend analysis charts. Users can configure API parameters for controlling randomness, diversity, presence penalty, and maximum tokens. The tool also includes a frontend project based on uniapp + Vue3 + TypeScript for multi-platform applications. It provides a backend service running on Fastify with Node.js, Cheerio.js for web scraping, Pino for logging, xlsx for Excel file handling, and Jest for testing. The project is still in development and some features may not be fully implemented. The analysis reports are for reference only and do not constitute investment advice. Users are advised to use the tool responsibly and avoid addiction to gambling.

99AI is a commercializable AI web application based on NineAI 2.4.2 (no authorization, no backdoors, no piracy, integrated front-end and back-end integration packages, supports Docker rapid deployment). The uncompiled source code is temporarily closed. Compared with the stable version, the development version is faster.

Awesome-ChatTTS is an official recommended guide for ChatTTS beginners, compiling common questions and related resources. It provides a comprehensive overview of the project, including official introduction, quick experience options, popular branches, parameter explanations, voice seed details, installation guides, FAQs, and error troubleshooting. The repository also includes video tutorials, discussion community links, and project trends analysis. Users can explore various branches for different functionalities and enhancements related to ChatTTS.

github-pr-summary is a bot designed to summarize GitHub Pull Requests, helping open source contributors make faster decisions. It automatically summarizes commits and changed files in PRs, triggered by new commits or a magic trigger phrase. Users can deploy their own code review bot in 3 steps: create a bot from their GitHub repo, configure it to review PRs, and connect to GitHub for access to the target repo. The bot runs on flows.network using Rust and WasmEdge Runtimes. It utilizes ChatGPT/4 to review and summarize PR content, posting the result back as a comment on the PR. The bot can be used on multiple repos by creating new flows and importing the source code repo, specifying the target repo using flow config. Users can also change the magic phrase to trigger a review from a PR comment.

A project that utilizes large models to help with Code Review on Gitlab, aimed at improving development efficiency. The project is customized for Gitlab and is developing a Multi-Agent plugin for collaborative review. It integrates various large models for code security issues and stays updated with the latest Code Review trends. The project architecture is designed to be powerful, flexible, and efficient, with easy integration of different models and high customization for developers.

Code Review GPT uses Large Language Models to review code in your CI/CD pipeline. It helps streamline the code review process by providing feedback on code that may have issues or areas for improvement. It should pick up on common issues such as exposed secrets, slow or inefficient code, and unreadable code. It can also be run locally in your command line to review staged files. Code Review GPT is in alpha and should be used for fun only. It may provide useful feedback but please check any suggestions thoroughly.

Digma is a Continuous Feedback platform that provides code-level insights related to performance, errors, and usage during development. It empowers developers to own their code all the way to production, improving code quality and preventing critical issues. Digma integrates with OpenTelemetry traces and metrics to generate insights in the IDE, helping developers analyze code scalability, bottlenecks, errors, and usage patterns.

AI Code Reviewer is a GitHub Action that utilizes OpenAI's GPT-4 API to provide intelligent feedback and suggestions on pull requests. It helps enhance code quality and streamline the code review process by offering insightful comments and filtering out specified files. The tool is easy to set up and integrate into GitHub workflows.

Sourcery is an automated code reviewer tool that provides instant feedback on pull requests, helping to speed up the code review process, improve code quality, and accelerate development velocity. It offers high-level feedback, line-by-line suggestions, and aims to mimic the type of code review one would expect from a colleague. Sourcery can also be used as an IDE coding assistant to understand existing code, add unit tests, optimize code, and improve code quality with instant suggestions. It is free for public repos/open source projects and offers a 14-day trial for private repos.

RTL-Coder is a tool designed to outperform GPT-3.5 in RTL code generation by providing a fully open-source dataset and a lightweight solution. It targets Verilog code generation and offers an automated flow to generate a large labeled dataset with over 27,000 diverse Verilog design problems and answers. The tool addresses the data availability challenge in IC design-related tasks and can be used for various applications beyond LLMs. The tool includes four RTL code generation models available on the HuggingFace platform, each with specific features and performance characteristics. Additionally, RTL-Coder introduces a new LLM training scheme based on code quality feedback to further enhance model performance and reduce GPU memory consumption.

Awesome LLM for APR is a repository dedicated to exploring the capabilities of Large Language Models (LLMs) in Automated Program Repair (APR). It provides a comprehensive collection of research papers, tools, and resources related to using LLMs for various scenarios such as repairing semantic bugs, security vulnerabilities, syntax errors, programming problems, static warnings, self-debugging, type errors, web UI tests, smart contracts, hardware bugs, performance bugs, API misuses, crash bugs, test case repairs, formal proofs, GitHub issues, code reviews, motion planners, human studies, and patch correctness assessments. The repository serves as a valuable reference for researchers and practitioners interested in leveraging LLMs for automated program repair.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

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.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.