Same 9B Qwen weights: 19.1% in Aider vs 45.6% with a scaffold adapted to small local models

Using the same Qwen3.5-9B Q4 weights on the 225-task Aider Polyglot benchmark, the author changed only the scaffold and raised mean pass@2 from 19.11% to 45.56%. The little-coder setup is not a new model; it uses bounded reasoning, a write guard, explicit workspace discovery, and small per-turn skill injections. The key claim is scaffold-model fit, but the post reports only two full runs and does not disclose ablations, cross-model replications, or a second benchmark.

Why it matters: HKR-H/K/R all pass: the hook is a 2.4x jump on Aider Polyglot 225 with the same 9B Qwen weights, and the post names the scaffold mechanisms. Importance stays low-featured because evidence is thin: two full runs, no ablation, no cross-model rerun, and no second benchmark.