I moved my coding agents off hosted APIs and onto two machines I own, a MacBook Pro M5 Pro as the client and a gaming PC with an RX 7800 XT as the model server. Model capability was rarely the blocker. The blockers were unified memory limits, inference engines that mangle tool call formats, a model stuck in a loop, and a laptop that kept freezing for reasons I still have not pinned down.
How I built an agentic writing pipeline on Mastra: researcher, writer, editor, human approval, and the token-limiter bug that made revisions invent details.
Colibrì, a ~1,300-line pure C inference engine with zero dependencies, runs the 744B-parameter GLM 5.2 MoE model on a laptop with 25 GB of RAM and no GPU by streaming int4-quantized experts from NVMe. Cold-cache throughput is 0.05 to 0.1 tokens per second, so speed is the honest limiter, but it is an existence proof that frontier-scale local inference works on commodity hardware.
Coding agents get better every month, and the way to stay in control is to give them clear instructions and context rather than fight the shift. This covers agent instruction file conventions worth adding to a repo (AGENTS.md, SKILL.md, CLAUDE.md, Cursor skills) and two open-source tools, Ponytail and Caveman, that cut token spend by trimming what agents write and compressing what you send.