Fathohm — Comprehension-Debt System of Record

Fathohm makes an invisible problem visible: the gap between what a codebase does and what any human on the team actually understands. As AI agents write a growing share of production code, teams accumulate comprehension debt — modules nobody reviewed deeply and nobody can explain. Fathohm analyzes code authorship and human review depth via a read-only GitHub App, classifies agent-authored vs human-authored commits (heuristics first — co-author trailers, committer signatures — with batched LLM classification only for the ambiguous residue), and computes a deterministic, fully explainable comprehension score per module. The hero view is the Map: a treemap of the repository colored by score, built to stay fast on 10k+ file repos. Everything derives from an append-only event spine, so improving the scorer retroactively re-values all history. Source code is read transiently and never persisted — stored data is metadata, events, scores, and summaries only.