When Philosophy meets AI

An MCP Multimodal AI Agent with eyes and ears!

Python command-line tool for interacting with AI models through the OpenRouter API/Cloudflare AI Gateway, or local self-hosted Ollama. Optionally support Microsoft LLMLingua prompt token compression

🐙 A curated set of Codex and OpenClaw skills for workflow automation, technical debugging, and agent-assisted development patterns.

AI Agent built with Google ADK that leverages Google Maps MCP Server to answer real-world location questions with tool usage and traceable execution via Opik.

🚀 Production-ready AI agents framework. Fork → Deploy → Ship in minutes. Multi-agent patterns, FastAPI backend, observability with Opik. Built with Google ADK or Langgraph. MIT License.

A one-stop repository of resources for AI Product Managers and Engineers. Contains code for Evals, prompt templates, Claude skills, and more!

🔐 Curated OSINT toolkit for cybersecurity investigations, threat analysis, and public data mapping

Building Production-Ready AI Agent Evaluation with Opik MCP Server on AWS AgentCore

Community-maintained PHP SDK for Opik - an LLM observability and evaluation platform.

In this we implement opik llm evaluation metrics on medical data analyzer

FretCoach : AI-Powered Adaptive Guitar Training

Where AI disagrees before users suffer

tangkurak4you

Dex is a production-grade personal AI assistant built on the Model Context Protocol (MCP) architecture. Unlike generic chatbots, Dex is designed to be a persistent, memory-aware assistant.

Project Vyasa is a local-first research execution framework for DGX Spark that helps researchers, journal authors, and domain experts turn unstructured documents into defensible, evidence-bound manuscripts for high-stakes, long-running inquiry. It keeps humans in control of judgment while AI handles extracting, validating, and governing evidence.

MLOps-driven LLM RAG assistant that learns your writing style from your online content, with an FTI pipeline (Features → Training → Inference) , RAG for context grounding, and ZenML orchestration.

An autonomous AI Agent that uses Computer Vision and LLM reasoning to monitor focus, "shame" distractions, and ensure your 2026 productivity resolutions actually stick.

This project provides a collection of tools and examples for working with Large Language Models (LLMs), focusing on Groq's fast inference capabilities and integration with the Opik LLM evaluation and tracing platform.

Reproducibility code for “Evaluating the Performance of Large Language Models in Taxonomic Classification of Questions in Verbal Protocols of Design” (AI EDAM submission; under review). [WIP]