Software engineer / Miami, FL
The dispatch
Long-form thoughts on programming, leadership, product design, and more, collected in chronological order.
I turned prefix caching back on for a 125B hybrid model on a DGX Spark, with the fixes carried from five open pull requests. Hits landed, a number planted deep in the prompt came back exactly every time, and an 11,000-token prompt dropped from 5.99 s to 0.96 s. Then one request answered the question two other requests were asking. Proving that was not a leak took a better instrument than the one I started with.
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The model card advertised a 4B multi-token-prediction head. No published GGUF contained it, and the architecture had no code path to run it. Both halves arrived within 36 hours from two different strangers, in incompatible forms — so I merged the head into the target file myself. It went from 27.75 to 43.30 tok/s, and three of my four mistakes along the way were about verification, not tensors.
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An 87 GiB model got OOM-killed with nothing in its own log. The process that triggered it was a 27 MB dashboard service. The reason my inference server was the kernel first choice was not its size — it was a systemd user-manager default that scores every terminal-launched process to die before any system service. Then restarting it killed my editor.
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A 125B model decoding at 26.6 tok/s looks unremarkable beside a 31B at 29.3 — until you notice the 31B is running a drafter, so its number is a forward-pass rate multiplied by an acceptance length. Per forward pass the big model was 3x faster. Here is how to decompose the number, and what the bandwidth arithmetic says is still on the table.
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Four slots served my agentic pipeline at 1.2% prefix-cache reuse. One slot served the same pipeline at 86.7%, and saved 11.6 minutes of prefill in a 39-minute window. Slots schedule requests; they do not add compute — and on a single GPU they scatter the one thing a prefill-bound workload actually depends on.
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I have swapped the local model under my newspaper 85 times in four months. That looked like a natural experiment, so I built an instrument to read it — including a gate that refuses comparisons contaminated by time. The gate passed. It was reading my switch log instead of my writing, and the two models it cleared had never once alternated.
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I used a language model to pick the better of two drafts, and trusted it for months. Then I gave it two drafts from an identical configuration and asked it to choose. Here is what measuring a judge’s noise floor costs, and why every A/B result before it was unreadable.
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Three times this year a reasoning model spent its whole token budget thinking and handed back empty content under HTTP 200. Three times, the code reading the result invented a different explanation — a parser bug, then an unconstrained grammar. Neither was true, and one of them fired inside a safety check.
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How this site went from a heavy ink-wipe page transition to a 300ms breath through paper — WebGL bokashi gradation, mathematical opacity guarantees, and the fail-safes that keep navigation working when the shader does not.
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