Topic
Reproducing results: seeds, harnesses, and verdicts.
Across 155 small-model experiments centered on Qwen 3.5 4B, the same thing kept working: give the model something executable it can check against the evidence it has, and it punches far above its benchmark weight. Here is the field guide — the levers that worked, how I know they're real, and the frontier they opened up.
The headline benchmark is the least durable thing in a model release. Here is how to read access, licenses, cards, eval protocols, and serving facts before you commit engineering to a number you cannot reproduce.
The nanoGPT speedrun is a rare, fully open optimization target: hit 3.28 FineWeb validation loss on a GPT-2 (124M)-class model in minimum wall-clock on 8×H100. We reproduce the pipeline, isolate what the Muon optimizer and the architecture changes actually buy, and flag what will not transfer off the bench.
A rare fully-public optimization target with a reproducible harness — exactly the kind of artifact we like. The Muon optimizer it popularized is the most interesting practical idea to come out of it.
The standard behind everything we publish: the filters that decide what earns your attention, the reproduced-to-unverified ladder we grade claims on, how we handle benchmarks and weightless releases, and why every correction is dated and logged rather than silently edited.