Blogs

TL;DR I published a post last week about replacing gpt-oss:120b with Qwen 3.6 on my MacBook Pro M3 Max. The numbers in that post were real, but one set of tests was structurally gameable — 38 of 40 baseline images were the same class, so an “always-say-A” stub also scored 95 percent. I went back, designed three un-gameable reasoning tasks, and ran them against both local models on identical hardware. gpt-oss:120b finished the three tasks in 137 seconds. qwen3.6:27b-q8_0 took 1593 seconds — 11.6 times slower, on the same M3 Max, for the same prompts and essentially the same answers. This post is the methodology, the receipts, and the honest correction to the migration narrative. ...

TL;DR 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 finding: the same local model confidently hallucinated a description of an image it could not see, with no error and no warning. Local models are ready for production agent work — at least on hardware like this. Operational asymmetries are real. Verification is not optional. ...

TL;DR 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. ...

TL;DR 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. ...

TL;DR 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 management function you are talking about. ...

TL;DR Neuroscientist Lisa Genova explains why forgetting is normal — your brain filters out most of your day, loses context when you change rooms, and is terrible at remembering future intentions. I watched her TED talk and realized I had already encountered every one of these failures in AI agents I build for real organizations. Here is how the parallels work, what breaks in production, and what I built to fix it. ...

TL;DR Andrej Karpathy tweeted about using LLMs to build personal knowledge bases — raw sources in, compiled wiki out, all in Obsidian. I implemented his entire workflow in one session using Claude Code skills. Four YouTube transcripts became 21 cross-linked wiki articles. The system now compiles new sources, health-checks its own consistency, and searches itself. It took an afternoon. It will compound forever. Your AI should not just answer questions. It should build a knowledge base that makes every future question easier to answer. ...

TL;DR I run an autonomous AI agent on a Mac Mini in my house. She handles 16 daily cron jobs — finances, email triage, outreach campaigns, device monitoring, morning briefings. The agent says “done.” But did it actually do anything? I built a 9-dimension eval rubric to find out. Along the way I discovered that my evals were broken, my agent was better than I thought, and the most important metric isn’t pass/fail — it’s whether a failure is your fault or the agent’s fault. ...

TL;DR We deployed an autonomous AI executive assistant for a nonprofit in under two weeks. She runs eight scheduled programs daily — morning briefings, social media, donor research, newsletter drafts, content scouting, and end-of-day digests — all without being asked. The CEO went from drowning in operational work to just making decisions. The same pattern works for any small organization: medical practices, restaurants, law firms, conferences, mom-and-pop shops. “The CEO’s time should be spent on decisions, not data entry. On strategy, not scheduling. On relationships, not routine.” ...

TL;DR I built two iOS apps in about ten days total. Neither was planned as a product — both started as solutions to my own problems. In the first month on the App Store, people in 114 countries found them through organic search. No marketing budget. No ads. No influencer deals. Here is what the numbers look like, what they taught me, and why I think everyone with an idea should just build the thing. ...