1.4 KiB
Didactopus
Didactopus is a local-first AI-assisted autodidactic mastery platform built around concept graphs, evaluator-driven evidence, adaptive planning, mastery ledgers, curriculum ingestion, and human review of generated draft packs.
This revision
This revision adds a graph-aware prerequisite analysis layer.
The goal is to inspect a pack not just as a set of files or even as a semantically plausible curriculum draft, but as an actual dependency graph whose structure may reveal deeper curation problems.
Why this matters
A pack can be syntactically valid, cross-file coherent, and even semantically plausible, yet still have a concept graph that is hard to learn from or maintain. Typical examples:
- prerequisite cycles
- isolated concepts with no curricular integration
- bottleneck concepts with too many downstream dependencies
- suspiciously flat domains with almost no dependency structure
- suspiciously deep chains suggesting over-fragmentation
Those graph problems can still raise the activation-energy cost of using a pack, because they make learning paths harder to trust and revise.
What is included
- prerequisite graph analysis module
- cycle detection
- isolated concept detection
- bottleneck concept detection
- flatness and chain-depth heuristics
- graph findings included in import preview
- UI panel for graph-analysis warnings
- sample packs and tests