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    <title>Jason Belnick — Writing</title>
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    <description>Practical notes on AI adoption and local models.</description>
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    <lastBuildDate>Tue, 07 Jul 2026 00:00:00 +0000</lastBuildDate>
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      <title>The Mac mini that never swaps</title>
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      <pubDate>Tue, 07 Jul 2026 00:00:00 +0000</pubDate>
      <description>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.</description>
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      <title>I built a loop that learns from my editor</title>
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      <pubDate>Sat, 04 Jul 2026 00:00:00 +0000</pubDate>
      <description>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</description>
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      <title>I trained a local model to write like Claude</title>
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      <pubDate>Fri, 03 Jul 2026 00:00:00 +0000</pubDate>
      <description>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</description>
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      <title>The judge that runs on my desk</title>
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      <pubDate>Thu, 02 Jul 2026 00:00:00 +0000</pubDate>
      <description>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</description>
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      <title>The 4:30am shift</title>
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      <pubDate>Thu, 02 Jul 2026 00:00:00 +0000</pubDate>
      <description>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.</description>
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      <title>A wiki my models maintain</title>
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      <pubDate>Thu, 02 Jul 2026 00:00:00 +0000</pubDate>
      <description>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</description>
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      <title>Scheduling one big local model on a 64GB Mac Studio</title>
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      <pubDate>Thu, 02 Jul 2026 00:00:00 +0000</pubDate>
      <description>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</description>
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      <title>I delete more local models than I keep</title>
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      <pubDate>Thu, 02 Jul 2026 00:00:00 +0000</pubDate>
      <description>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</description>
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      <title>The missing 90 percent of AI adoption</title>
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      <pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate>
      <description>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</description>
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      <title>The open-weight frontier arrived in June</title>
      <link>https://www.jasonbelnick.com/writing/the-open-weight-frontier-mid-2026/</link>
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      <pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate>
      <description>In one June window, open weights reached the coding frontier. What I&#x27;d build on, what I&#x27;d admire from a distance, and which numbers to trust</description>
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      <title>Local LLMs on a 64GB Mac Studio</title>
      <link>https://www.jasonbelnick.com/writing/local-llms-on-a-64gb-mac-studio/</link>
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      <pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate>
      <description>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</description>
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      <title>The week the cloud blinked</title>
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      <pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate>
      <description>Fable 5 vanished for 18 days under an export-control order. Frontier model access is now a dependency risk you have to engineer against</description>
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      <title>Routing work between local and cloud</title>
      <link>https://www.jasonbelnick.com/writing/routing-work-between-local-and-cloud/</link>
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      <pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate>
      <description>Model rankings churn monthly; a routing policy survives. Classify LLM workloads by risk, privacy, latency, and volume, and keep the decision log.</description>
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      <title>Self-refinement that knows when to stop</title>
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      <pubDate>Wed, 01 Jul 2026 00:00:00 +0000</pubDate>
      <description>Nous Research&#x27;s Autoreason finds naive self-refinement makes output worse, then fixes it with a blind tournament where doing nothing can win</description>
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