For developers who build AI systems the hard way — local inference, MCP servers, multi-agent orchestration, and agent memory. No tutorials recycled from the official docs. Real patterns from production builds.
After building 25+ MCP servers, here are the patterns that hold up in production and the shortcuts that blow up in your face when an agent starts hammering the endpoint.
Real numbers, not marketing copy. What it can and can't do on consumer hardware.
Episodic, semantic, procedural — not just theory. Built all 7, here's what each one actually changes.
Everything we build that isn't a client product goes public. MIT license. Fork it, improve it, ship it.
This is the minimal FastMCP server template we start every build with. Copy it, rename the tools, ship it.
# pip install fastmcp
from fastmcp import FastMCP
mcp = FastMCP("my-skill-server")
@mcp.tool()
async def do_thing(input: str) -> str:
"""Does the thing. Describe it here — agents read this."""
return f"Did: {input}"
if __name__ == "__main__":
mcp.run()