Machine learning engineer. I build AI tools, CLI applications, and civic tech, and write about the parts that were harder than they should have been.
My LLM benchmarks kept collapsing 4x mid-session. macmon caught a firmware power limiter clamping the GPU to 338 MHz and holding it while the die cooled, fans never past 70%. Pinning them at max with fanpro: 2.57x sustained decode, and a 100k-context job in 259 s instead of 568+, byte-identical.
Last post I called local 1M context a mirage. Then Kimi-Linear-48B passed: 8/8 needle recall at a real 1,048,692-token prompt on a 512GB M3 Ultra, full ingestion proven. Native 1M is finally real. It just takes a five-hour prefill.
I ran a 754B model on a 512GB Mac Studio. It fits, it crawls, and its 1M context flag is a warning message and a silent cap. Where local models fail as coding tools, and the one job they are great at.
Easy-Switch moves the keyboard and abandons the mouse. One HID++ packet fixes that, once you get past the two permission gates macOS hides between you and your own devices.
Two switches I leave on: the advisor tool and /effort ultracode. An honest, log-backed account of where they help, where they are overkill, and where the model wisely skips them.
An opinionated Cloudflare-first template. Next.js 16, D1, Better Auth, Stripe. No monorepo. No edge-runtime religion. No status page.
Head-to-head on a Mac Studio M3. 26 prompts, 6 categories, and one surprising finding about thinking mode overhead.
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