AI Systems: Self-Hosted Assistants, RAG, and Local Infrastructure
Most local AI setups start with a model and a runtime.
AI Systems
OpenClaw Quickstart: Install with Docker (Ollama GPU or Claude + CPU)
Install OpenClaw locally with Ollama
OpenClaw is a self-hosted AI assistant designed to run with local LLM runtimes like Ollama or with cloud-based models such as Claude Sonnet.
OpenClaw: Examining a Self-Hosted AI Assistant as a Real System
OpenClaw AI Assistant Guide
Most local AI setups start the same way: a model, a runtime, and a chat interface.
Self-Hosting Cognee: Choosing LLM on Ollama
Testing Cognee with local LLMs - real results
Cognee is a Python framework for building knowledge graphs from documents using LLMs. But does it work with self-hosted models?
Related guides for persistent knowledge layers — agent memory plugins, graph tooling, and stack context — live under the AI Systems Memory hub.
Choosing the Right LLM for Cognee: Local Ollama Setup
Thoughts on LLMs for self-hosted Cognee
Choosing the Best LLM for Cognee demands balancing graph-building quality, hallucination rates, and hardware constraints. Cognee excels with larger, low-hallucination models (32B+) via Ollama but mid-size options work for lighter setups.
Building MCP Servers in Python: WebSearch & Scrape Guide
Build MCP servers for AI assistants with Python examples
The Model Context Protocol (MCP) is revolutionizing how AI assistants interact with external data sources and tools. In this guide, we’ll explore how to build MCP servers in Python, with examples focused on web search and scraping capabilities.
Model Context Protocol (MCP), and notes on implementing MCP server in Go
Longread about MCP scpecs and implementation in GO
Here we have a description of The Model Context Protocol (MCP), short notes on how to implement an MCP server in Go, including message structure, protocol specifications.