DeepSeek Harness: Build AI Coding Agents with Free Models

About this course

This course contains the use of artificial intelligence. Most AI coding courses stop at "install it and ask it something.

" This one ends with an agent that fixes a real defect and hands you the evidence. DeepSeek Harness is an open-source, MIT-licensed agent harness built on a single idea: everything is a plugin. Models, tools, skills, sessions, the agent loop itself — all swappable. That makes it an unusually good place to learn how coding agents actually work, because nothing is hidden behind a product. The catch with most agent courses is the bill. This one has none. The harness runs locally on an ordinary Windows, macOS or Linux machine, and the thinking happens on a free cloud model — Gemini's free API tier as the primary route, with explicit free OpenRouter models as a second provider for when a quota runs out. No paid API key. No GPU. No subscription. There is an optional section for running a small model entirely locally through Ollama, for work you cannot send anywhere. What makes this different Anyone can film an agent succeeding. The hard part — the part that decides whether you can trust one of these things at work — is telling the difference between an agent that did the job and an agent that says it did. That distinction runs through the whole course. You will read trajectories: the actual model requests, tool arguments, tool responses and commands recorded for every session. You will set permission modes so an exploration task cannot write a file, rather than politely asking it not to. You will review diffs before approving them, separate pre-existing test failures from ones you just caused, and require the agent to show real test output instead of a summary.

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