Backend engineer focused on AI application development. I'm interested in everything along the path toward AGI — from models and agents to tools, voice, and local inference — and enjoy turning those ideas into useful products.
- 🤖 AI agents that can reason, use tools, and get things done.
- 📚 Knowledge systems that know when to answer — and when not to.
- 🛠️ The engineering behind reliable AI applications.
- 🧬 LightRAG: a retrieval system that helps AI applications connect documents, entities, and relationships.
- 🧱 Dify: a visual way to build AI workflows, knowledge bases, and agent applications.
- 🔎 RAGFlow: a way to turn documents and data into structured context for RAG applications.
- 🧠 Hindsight: a memory layer that helps coding agents retain and use past context.
- 🌐 browser-use: a bridge between AI agents and the websites they need to operate.
- 🦞 OpenClaw: a personal AI assistant that brings models, tools, and conversations together across your own devices.
- 🧠 LocalAI: a practical route to running AI models locally through an OpenAI-compatible interface.
- 🐍 redis-py: the Python client that lets applications talk to Redis in both sync and async workflows.
- 🤝 CrewAI: a framework for orchestrating role-playing, autonomous agents that take on complex tasks together.
- 🐹 ADK Go: a code-first Go toolkit for building, evaluating, and deploying agents.
- 🧩 Agent Framework: a framework for building and orchestrating multi-agent workflows in Python and .NET.
- 🐋 Orca: an environment for running a fleet of parallel coding agents on desktop, mobile, and remote runtimes.
- ⚡ FastGPT: a knowledge platform for turning documents into question-answering applications.
- 💬 AnythingLLM: a local-first workspace for documents, chat, and personal assistants.
- 👥 Multica: an open-source place for people and agents to work as one team.