Writing
Notes from the workbench.
Practical notes on AI adoption and local models, from someone who runs both in production. New posts land here first; there is also an RSS feed.
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The Mac mini that never swaps
Why the unattended jobs in my local-AI setup run on a dedicated always-on Mac mini instead of my 64GB Mac Studio, serving one small model that never gets evicted.
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I built a loop that learns from my editor
Part 2 of the local writer story: a self-improvement loop whose first act was rejecting its own idea, and why that rejection is the feature
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I trained a local model to write like Claude
I trained a 9B to write like Claude, benched it blind, and the ending flipped: the bigger model with the distilled skill took the seat
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The judge that runs on my desk
I benched local LLMs as judges, watched a bare prompt invert the winner, and swapped a cloud judge for a local one once the rubric was armed
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The 4:30am shift
A stack of cron jobs on local LLMs runs my knowledge base overnight. Two failures I watched in the logs changed how I build every local pipeline.
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A wiki my models maintain
My local LLMs maintain a knowledge base: they capture sources, summarize them, and commit the output daily. The RAG engine I built on top was the wrong idea
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Scheduling one big local model on a 64GB Mac Studio
How I schedule large local models on a 64GB Mac Studio with a router and a swap tool instead of juggling memory by hand every day
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I delete more local models than I keep
How a nightly coding benchmark and a strict parity rule turned a season of hyped local models into deleted weights and a stack I trust
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The missing 90 percent of AI adoption
73% of surveyed enterprises use AI but only 10% run on it. Missing ownership, sign-off paths, and measured workflows create the gap; the models are fine
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The open-weight frontier arrived in June
In one June window, open weights reached the coding frontier. What I'd build on, what I'd admire from a distance, and which numbers to trust
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Local LLMs on a 64GB Mac Studio
The awkward middle tier of local AI in mid-2026, the capacity and bandwidth math that governs it, and a 75 percent verdict on what a 64GB Mac Studio actually delivers
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The week the cloud blinked
Fable 5 vanished for 18 days under an export-control order. Frontier model access is now a dependency risk you have to engineer against
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Routing work between local and cloud
Model rankings churn monthly; a routing policy survives. Classify LLM workloads by risk, privacy, latency, and volume, and keep the decision log.
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Self-refinement that knows when to stop
Nous Research's Autoreason finds naive self-refinement makes output worse, then fixes it with a blind tournament where doing nothing can win