I Tested 4 Local AI Coding Setups

Pi agent vs OpenCode vs DeepSeek Harness: which free local AI coding setup actually works when the same model runs inside each one A free model you can run on your own machine now scores 77% on SWE-bench Verified, the benchmark used to rank paid flagships, so the harness you wrap around it decides what code actually ships working. This video tests four setups: Pi, OpenCode, DeepSeek Harness, and Claude Code, running the identical local model on a real coding task to see which one produces code that actually runs. OpenCode, the most widely used open-source coding harness at 207k GitHub stars, treats local models as a configuration file you maintain by hand, their own documentation admits tool-calling breaks and you'll need to manually crank context windows in Ollama. Pi, built by Armin Ronacher and Mario Zechner and sitting at 100k+ stars, documents eleven different thinking format values and three spellings of token budgets because the industry never standardized how to tell a model to reason harder. DeepSeek Harness arrived in August 2026 and hit 222k stars in one month, but under the hood it routes all local requests through Pi's library, not its own code. Claude Code refuses to run any local model at all, leaving only the three free contenders viable. The real winner emerges from a hands-on test at Atlas Cloud where three runs of the same model in different harnesses produced different outputs: two confident 'Done' checklists with broken code, one slow run that actually worked, the difference was two configuration lines: thinking format and output cap, both buried in Pi's adapter docs. Built for developers choosing between self-hosted and cloud, and anyone wondering whether free local AI coding setups are ready to replace the paid platforms. Chapters: 0:00 When the harness becomes a maintenance job 3:56 The underdog who documented everything 7:07 How 222k stars happened in 30 days 9:52 Why the paid option never showed up 12:03 Two config lines that broke the test Tools & resources mentioned: - Pi: https://github.com/earendil-works/pi - OpenCode: https://github.com/anomalyco/opencode - DeepSeek Harness: https://github.com/deepseek-ai/deepseek-harness - Ollama: https://ollama.ai - vLLM - LM Studio About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #ai coding #pi agent #deepseek #local llm #opencode