caddy-defender

The Caddy Defender plugin is a middleware for Caddy that allows you to block or manipulate requests based on the client's IP address. It provides features such as IP range filtering, predefined IP ranges for popular AI services, custom IP ranges configuration, and multiple responder backends for different actions like blocking, custom responses, dropping connections, returning garbage data, redirecting, and tarpitting to stall bots. The plugin can be easily installed using Docker or built with `xcaddy`. Configuration is done through the Caddyfile syntax with various options for responders, IP ranges, custom messages, and URLs.

PAI is an open-source personal AI infrastructure designed to orchestrate personal and professional lives. It provides a scaffolding framework with real-world examples for life management, professional tasks, and personal goals. The core mission is to augment humans with AI capabilities to thrive in a world full of AI. PAI features UFC Context Architecture for persistent memory, specialized digital assistants for various tasks, an integrated tool ecosystem with MCP Servers, voice system, browser automation, and API integrations. The philosophy of PAI focuses on augmenting human capability rather than replacing it. The tool is MIT licensed and encourages contributions from the open-source community.

LLM4S provides a simple, robust, and scalable framework for building Large Language Models (LLM) applications in Scala. It aims to leverage Scala's type safety, functional programming, JVM ecosystem, concurrency, and performance advantages to create reliable and maintainable AI-powered applications. The framework supports multi-provider integration, execution environments, error handling, Model Context Protocol (MCP) support, agent frameworks, multimodal generation, and Retrieval-Augmented Generation (RAG) workflows. It also offers observability features like detailed trace logging, monitoring, and analytics for debugging and performance insights.

Klavis AI is a production-ready solution for managing Multiple Communication Protocol (MCP) servers. It offers self-hosted solutions and a hosted service with enterprise OAuth support. With Klavis AI, users can easily deploy and manage over 50 MCP servers for various services like GitHub, Gmail, Google Sheets, YouTube, Slack, and more. The tool provides instant access to MCP servers, seamless authentication, and integration with AI frameworks, making it ideal for individuals and businesses looking to streamline their communication and data management workflows.

Archon is an AI meta-agent designed to autonomously build, refine, and optimize other AI agents. It serves as a practical tool for developers and an educational framework showcasing the evolution of agentic systems. Through iterative development, Archon demonstrates the power of planning, feedback loops, and domain-specific knowledge in creating robust AI agents.

sktime is a Python library for time series analysis that provides a unified interface for various time series learning tasks such as classification, regression, clustering, annotation, and forecasting. It offers time series algorithms and tools compatible with scikit-learn for building, tuning, and validating time series models. sktime aims to enhance the interoperability and usability of the time series analysis ecosystem by empowering users to apply algorithms across different tasks and providing interfaces to related libraries like scikit-learn, statsmodels, tsfresh, PyOD, and fbprophet.

paperless-gpt is a tool designed to generate accurate and meaningful document titles and tags for paperless-ngx using Large Language Models (LLMs). It supports multiple LLM providers, including OpenAI and Ollama. With paperless-gpt, you can streamline your document management by automatically suggesting appropriate titles and tags based on the content of your scanned documents. The tool offers features like multiple LLM support, customizable prompts, easy integration with paperless-ngx, user-friendly interface for reviewing and applying suggestions, dockerized deployment, automatic document processing, and an experimental OCR feature.

Netdata is an open-source, real-time infrastructure monitoring platform that provides instant insights, zero configuration deployment, ML-powered anomaly detection, efficient monitoring with minimal resource usage, and secure & distributed data storage. It offers real-time, per-second updates and clear insights at a glance. Netdata's origin story involves addressing the limitations of existing monitoring tools and led to a fundamental shift in infrastructure monitoring. It is recognized as the most energy-efficient tool for monitoring Docker-based systems according to a study by the University of Amsterdam.

AgentNeo is a Python package that provides functionalities for project, trace, dataset, experiment management. It allows users to authenticate, create projects, trace agents and LangGraph graphs, manage datasets, and run experiments with metrics. The tool aims to streamline AI project management and analysis by offering a comprehensive set of features.

Spark NLP is a state-of-the-art Natural Language Processing library built on top of Apache Spark. It provides simple, performant, and accurate NLP annotations for machine learning pipelines that scale easily in a distributed environment. Spark NLP comes with 36000+ pretrained pipelines and models in more than 200+ languages. It offers tasks such as Tokenization, Word Segmentation, Part-of-Speech Tagging, Named Entity Recognition, Dependency Parsing, Spell Checking, Text Classification, Sentiment Analysis, Token Classification, Machine Translation, Summarization, Question Answering, Table Question Answering, Text Generation, Image Classification, Image to Text (captioning), Automatic Speech Recognition, Zero-Shot Learning, and many more NLP tasks. Spark NLP is the only open-source NLP library in production that offers state-of-the-art transformers such as BERT, CamemBERT, ALBERT, ELECTRA, XLNet, DistilBERT, RoBERTa, DeBERTa, XLM-RoBERTa, Longformer, ELMO, Universal Sentence Encoder, Llama-2, M2M100, BART, Instructor, E5, Google T5, MarianMT, OpenAI GPT2, Vision Transformers (ViT), OpenAI Whisper, and many more not only to Python and R, but also to JVM ecosystem (Java, Scala, and Kotlin) at scale by extending Apache Spark natively.

AgentNeo is an advanced, open-source Agentic AI Application Observability, Monitoring, and Evaluation Framework designed to provide deep insights into AI agents, Large Language Model (LLM) calls, and tool interactions. It offers robust logging, visualization, and evaluation capabilities to help debug and optimize AI applications with ease. With features like tracing LLM calls, monitoring agents and tools, tracking interactions, detailed metrics collection, flexible data storage, simple instrumentation, interactive dashboard, project management, execution graph visualization, and evaluation tools, AgentNeo empowers users to build efficient, cost-effective, and high-quality AI-driven solutions.

skpro is a library for supervised probabilistic prediction in python. It provides `scikit-learn`-like, `scikit-base` compatible interfaces to: * tabular **supervised regressors for probabilistic prediction** \- interval, quantile and distribution predictions * tabular **probabilistic time-to-event and survival prediction** \- instance-individual survival distributions * **metrics to evaluate probabilistic predictions** , e.g., pinball loss, empirical coverage, CRPS, survival losses * **reductions** to turn `scikit-learn` regressors into probabilistic `skpro` regressors, such as bootstrap or conformal * building **pipelines and composite models** , including tuning via probabilistic performance metrics * symbolic **probability distributions** with value domain of `pandas.DataFrame`-s and `pandas`-like interface

WhatsApp Group Summary Bot is an AI-powered tool that joins WhatsApp groups, tracks conversations, and generates intelligent summaries. It features automated group chat responses, LLM-based conversation summaries, knowledge base integration, persistent message history with PostgreSQL, support for multiple message types, group management, and a REST API with Swagger docs. Prerequisites include Docker, Python 3.12+, PostgreSQL with pgvector extension, Voyage AI API key, and a WhatsApp account for the bot. The tool can be quickly set up by cloning the repository, configuring environment variables, starting services, and connecting devices. It offers API usage for loading new knowledge base topics and generating & dispatching summaries to managed groups. The project architecture includes FastAPI backend, WhatsApp Web API client, PostgreSQL database with vector storage, and AI-powered message processing.

Automodel is a Python library for automating the process of building and evaluating machine learning models. It provides a set of tools and utilities to streamline the model development workflow, from data preprocessing to model selection and evaluation. With Automodel, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to find the best model for their dataset. The library is designed to be user-friendly and customizable, allowing users to define their own pipelines and workflows. Automodel is suitable for data scientists, machine learning engineers, and anyone looking to quickly build and test machine learning models without the need for manual intervention.

The Google Workspace MCP Server is a production-ready server that integrates major Google Workspace services with AI assistants. It supports single-user and multi-user authentication via OAuth 2.1, making it a powerful backend for custom applications. Built with FastMCP for optimal performance, it features advanced authentication handling, service caching, and streamlined development patterns. The server provides full natural language control over Google Calendar, Drive, Gmail, Docs, Sheets, Slides, Forms, Tasks, and Chat through all MCP clients, AI assistants, and developer tools. It supports free Google accounts and Google Workspace plans with expanded app options like Chat & Spaces. The server also offers private cloud instance options.

Monoscope is an open-source monitoring and observability platform that uses artificial intelligence to understand and monitor systems automatically. It allows users to ingest and explore logs, traces, and metrics in S3 buckets, query in natural language via LLMs, and create AI agents to detect anomalies. Key capabilities include universal data ingestion, AI-powered understanding, natural language interface, cost-effective storage, and zero configuration. Monoscope is designed to reduce alert fatigue, catch issues before they impact users, and provide visibility across complex systems.

The Caddy Defender plugin is a middleware for Caddy that allows you to block or manipulate requests based on the client's IP address. It provides features such as IP range filtering, predefined IP ranges for popular AI services, custom IP ranges configuration, and multiple responder backends for different actions like blocking, custom responses, dropping connections, returning garbage data, redirecting, and tarpitting to stall bots. The plugin can be easily installed using Docker or built with `xcaddy`. Configuration is done through the Caddyfile syntax with various options for responders, IP ranges, custom messages, and URLs.

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.