inference — Running a trained model to get answers — the phase where AI is actually used, as opposed to trained.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
MCP — The Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.
Why it matters
Lays out the concrete components (inferenceRunning a trained model to get answers — the phase where AI is actually used, as opposed to trained.Full definition → provider, gateway/router, agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition →, skills/MCPThe Model Context Protocol — an open standard that lets any AI assistant plug into any tool or data source without custom integration code.Full definition →) needed to run open models for agentic coding, showing the switch from closed APIs is closer to a config change than a new discipline.
Key quotes
“If you already know how to use Claude Code, you’re much closer to using open models than you probably think.”
“Rather than thinking of bigger models as being better than smaller models, it’s better to think of these models as two different types of tools in a toolbox.”
“The difference between small and large is less about quality and more about how much ambiguity they can comfortably handle.”
“Instead of choosing an AI coding product, you can build your own AI coding stack.”
