bumpgen

bumpgen is a tool designed to automatically upgrade TypeScript / TSX dependencies and make necessary code changes to handle any breaking issues that may arise. It uses an abstract syntax tree to analyze code relationships, type definitions for external methods, and a plan graph DAG to execute changes in the correct order. The tool is currently limited to TypeScript and TSX but plans to support other strongly typed languages in the future. It aims to simplify the process of upgrading dependencies and handling code changes caused by updates.

anythingllm-docs is a documentation repository for the AnythingLLM project. It contains detailed guides, setup instructions, and information on features and legal aspects of the project. The repository structure is organized into public, pages, components, and configuration files. Users can contribute by creating issues and pull requests following specific guidelines. The project is licensed under the MIT License and has been migrated to NextJS with the help of @ShadowArcanist.

ShellGPT is a command-line productivity tool powered by AI large language models (LLMs). This command-line tool offers streamlined generation of shell commands, code snippets, documentation, eliminating the need for external resources (like Google search). Supports Linux, macOS, Windows and compatible with all major Shells like PowerShell, CMD, Bash, Zsh, etc.

GPT-All-Star is an AI-powered code generation tool designed for scratch development of web applications with team collaboration of autonomous AI agents. The primary focus of this research project is to explore the potential of autonomous AI agents in software development. Users can organize their team, choose leaders for each step, create action plans, and work together to complete tasks. The tool supports various endpoints like OpenAI, Azure, and Anthropic, and provides functionalities for project management, code generation, and team collaboration.

The dbgpts repository contains data apps, AWEL operators, AWEL workflow templates, and agents that are built upon DB-GPT. Users can install and manage these components within their DB-GPT environment. The repository offers functionalities such as listing available flows, installing dbgpts from the official repository, viewing installed dbgpts, running flows, and managing repositories. Users can create new workflow templates and operators using the provided commands. The repository aims to enhance the capabilities of DB-GPT by providing a collection of useful tools and resources for data processing and workflow management.

LangGraph Expense tracker is a small project that explores the possibilities of LangGraph. It allows users to send pictures of invoices, which are then structured and categorized into expenses and stored in a database. The project includes functionalities for invoice extraction, database setup, and API configuration. It consists of various modules for categorizing expenses, creating database tables, and running the API. The database schema includes tables for categories, payment methods, and expenses, each with specific columns to track transaction details. The API documentation is available for reference, and the project utilizes LangChain for processing expense data.

DeepDanbooru is an anime-style girl image tag estimation system written in Python. It allows users to estimate images using a live demo site. The tool requires specific packages to be installed and provides a structured dataset for training projects. Users can create training projects, download tags, filter datasets, and start training to estimate tags for images. The tool uses a specific dataset structure and project structure to facilitate the training process.

Tangent is a canvas for exploring AI conversations, allowing users to resurrect and continue conversations, branch and explore different ideas, organize conversations by topics, and support archive data exports. It aims to provide a visual/textual/audio exploration experience with AI assistants, offering a 'thoughts workbench' for experimenting freely, reviving old threads, and diving into tangents. The project structure includes a modular backend with components for API routes, background task management, data processing, and more. Prerequisites for setup include Whisper.cpp, Ollama, and exported archive data from Claude or ChatGPT. Users can initialize the environment, install Python packages, set up Ollama, configure local models, and start the backend and frontend to interact with the tool.

Vitron is a unified pixel-level vision LLM designed for comprehensive understanding, generating, segmenting, and editing static images and dynamic videos. It addresses challenges in existing vision LLMs such as superficial instance-level understanding, lack of unified support for images and videos, and insufficient coverage across various vision tasks. The tool requires Python >= 3.8, Pytorch == 2.1.0, and CUDA Version >= 11.8 for installation. Users can deploy Gradio demo locally and fine-tune their models for specific tasks.

doc-comments-ai is a tool designed to automatically generate code documentation using language models. It allows users to easily create documentation comment blocks for methods in various programming languages such as Python, Typescript, Javascript, Java, Rust, and more. The tool supports both OpenAI and local LLMs, ensuring data privacy and security. Users can generate documentation comments for methods in files, inline comments in method bodies, and choose from different models like GPT-3.5-Turbo, GPT-4, and Azure OpenAI. Additionally, the tool provides support for Treesitter integration and offers guidance on selecting the appropriate model for comprehensive documentation needs.

GhidrOllama is a script that interacts with Ollama's API to perform various reverse engineering tasks within Ghidra. It supports both local and remote instances of Ollama, providing functionalities like explaining functions, suggesting names, rewriting functions, finding bugs, and automating analysis of specific functions in binaries. Users can ask questions about functions, find vulnerabilities, and receive explanations of assembly instructions. The script bridges the gap between Ghidra and Ollama models, enhancing reverse engineering capabilities.

Letta is an open source framework for building stateful LLM applications. It allows users to build stateful agents with advanced reasoning capabilities and transparent long-term memory. The framework is white box and model-agnostic, enabling users to connect to various LLM API backends. Letta provides a graphical interface, the Letta ADE, for creating, deploying, interacting, and observing with agents. Users can access Letta via REST API, Python, Typescript SDKs, and the ADE. Letta supports persistence by storing agent data in a database, with PostgreSQL recommended for data migrations. Users can install Letta using Docker or pip, with Docker defaulting to PostgreSQL and pip defaulting to SQLite. Letta also offers a CLI tool for interacting with agents. The project is open source and welcomes contributions from the community.

Gemini-API is a reverse-engineered asynchronous Python wrapper for Google Gemini web app (formerly Bard). It provides features like persistent cookies, ImageFx support, extension support, classified outputs, official flavor, and asynchronous operation. The tool allows users to generate contents from text or images, have conversations across multiple turns, retrieve images in response, generate images with ImageFx, save images to local files, use Gemini extensions, check and switch reply candidates, and control log level.

ChatDBG is an AI-based debugging assistant for C/C++/Python/Rust code that integrates large language models into a standard debugger (`pdb`, `lldb`, `gdb`, and `windbg`) to help debug your code. With ChatDBG, you can engage in a dialog with your debugger, asking open-ended questions about your program, like `why is x null?`. ChatDBG will _take the wheel_ and steer the debugger to answer your queries. ChatDBG can provide error diagnoses and suggest fixes. As far as we are aware, ChatDBG is the _first_ debugger to automatically perform root cause analysis and to provide suggested fixes.

OpenAI-compatible API for TensorRT-LLM and NVIDIA Triton Inference Server, which allows you to integrate with langchain

Markdown Translation BOT is a GitHub action that translates markdown files into multiple languages using various AI models. It supports markdown, markdown-jsx, and json files only. The action can be executed by individuals with write permissions to the repository, preventing API abuse by non-trusted parties. Users can set up the action by providing their API key and configuring the workflow settings. The tool allows users to create comments with specific commands to trigger translations and automatically generate pull requests or add translated files to existing pull requests. It supports multiple file translations and can interpret any language supported by GPT-4 or GPT-3.5.

bumpgen is a tool designed to automatically upgrade TypeScript / TSX dependencies and make necessary code changes to handle any breaking issues that may arise. It uses an abstract syntax tree to analyze code relationships, type definitions for external methods, and a plan graph DAG to execute changes in the correct order. The tool is currently limited to TypeScript and TSX but plans to support other strongly typed languages in the future. It aims to simplify the process of upgrading dependencies and handling code changes caused by updates.

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.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

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.

Nvidia GPU exporter for prometheus, using `nvidia-smi` binary to gather metrics.

Tracecat is an open-source automation platform for security teams. It's designed to be simple but powerful, with a focus on AI features and a practitioner-obsessed UI/UX. Tracecat can be used to automate a variety of tasks, including phishing email investigation, evidence collection, and remediation plan generation.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

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

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.