I spent two days benchmarking three Qwen3.6 variants against gpt-oss:120b on my MacBook Pro M3 Max. The shocking result: a 21 GB coding-tuned model ran an OpenClaw-shaped research-brief workload that I use for the non profit MACONA.org in 6 seconds — 10x faster than gpt-oss:120b on the same prompt. Fast enough that I now have reasonable confidence I could move this kind of work off the SaaS-hosted frontier models I have been paying for and onto local hardware on my dev machine. The deeper…

Five OpenClaw agents run the content and executive-assistant pipeline for MACONA — a nonprofit I volunteer with — at $0.72 a day. The most common support question on r/OpenClaw is “$25 in 9 hours, help.” The gap between those two numbers is three config decisions stacked on top of each other: heartbeat interval, model on the loop, and hours of operation. None of them are clever. All of them are usually set wrong by default.

Over one weekend I fixed a 2001 Ford Ranger that would not crank and a lawnmower that had been sitting in a shed for four years. The truck turned out to be a stuck clutch safety switch — cost, zero. The lawnmower needed a replacement carburetor from Amazon for under twenty dollars. I am not a mechanic. ChatGPT was my shop manual, and the whole thing was driven by photos of my actual parts, not generic internet tutorials.