D

diffing

insideLLMs is a Python library and CLI for comparing LLM behaviour across models using shared probes and datasets. The harness is deterministic by design, so you can store run artefacts and reliably diff behaviour in CI.

A replacement collection view with integrated diffing for sectioned collection data and animated low-effort reloading, using an internal view model approach to keeping Cocoa’s collection view data in sync with expected sourced data.

A testing project for intelligently handling sectioned collection differences through differentials for use with Cocoa's NSCollectionView, supporting collective and step-by-step animation.