My Mac Mini is going back to the shop
Three separate experiments on the same Mac Mini all pointed at the same conclusion: free and private don't matter if the answers are wrong or don't arrive at all.
Self-Refine Without a Repetition Penalty
A controlled case study: the same recursive self-improvement loop, on the same local model, six and a half hours each. One run collapses into repetition within forty cycles. The other holds clean for all two hundred and one. The difference is a single sampler setting.
I ran a local AI model through 179 rounds of self-refine. It collapsed. One sampler setting fixed it.
An unguarded local model collapsed into repetition after 179 rounds of self-refine. Adding one sampler setting and rerunning the identical task produced 201 clean cycles instead.
Becoming a CMO: What You Actually Lose When You Stop Doing the Work
Career advice tells you what you gain on the way to CMO. Nobody tells you what you trade away - and how to stay close enough to the work to keep your judgement calibrated.
A 35-billion-parameter model now runs on a $599 Mac Mini. That doesn't make it useful for everything.
Ternary quantization gets a 35B open-weight model running on a base Mac Mini at reading speed. It still confidently got GEO wrong, which is the more useful finding.
How We Grew Marketing Sourced Pipeline by 20% in One Quarter
Why judging marketing by what happened this quarter is the wrong lens, and how Redgate grew marketing-sourced pipeline 20% anyway.
Human Beings are Holding Back Machine Learning
Machine learning tools have been accessible for decades. The real shortage has always been people who know how to use them properly.