The Bitcoin Red Team campaign after Coldcard exposed a dependency beyond model capability: usable access. Separate self-reported findings from confirmed vulnerabilities, then test safeguards, data controls and fallback workflows.
Knowledge distillation trains a student using a teacher’s examples, output scores or internal features. Compare the access each method needs, then evaluate quality, unwanted behavior, serving performance and full lifecycle cost.
Local inference gives control over weights and configuration, not a guarantee of political neutrality. A practical guide to evaluating framing, sizing KV memory and enforcing hybrid routing boundaries.
A small coding model mocked on r/LocalLLM is actually a clean case of execution-verified distillation. Here is how the method works, when distilling a frontier model into a cheap local one pays off, and the three things the hype leaves out.