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.

I had a sidewalk conversation with two neighbors that turned into a real-time debate about AI replacing jobs. Then I watched a Nate video that gave me the exact framework to explain why all 3 were right — and wrong. One neighbor is a project manager already using AI daily. One is a business analyst who coaches companies. One of my neighbours’ husband — a skeptic — is convinced AI cannot do creative work. All 3 are right in their position, and also all of us wrong, depending on which…