Didactopus

Didactopus is a local-first AI-assisted autodidactic mastery platform designed to help motivated learners achieve genuine mastery through Socratic mentoring, structured practice, project work, verification, and competency-based evaluation.

Tagline: Many arms, one goal — mastery.

Vision

Didactopus treats AI as a mentor, curriculum planner, critic, evaluator, and project guide rather than an answer vending machine. The design goal is to produce capable practitioners who can explain, apply, test, and extend knowledge in real settings.

The platform is meant to support AI-assisted autodidacts: learners who pursue real expertise outside, alongside, or beyond traditional institutions.

Core principles

• Active learning over passive consumption
• Socratic questioning over direct answer dumping
• Verification culture over uncritical acceptance
• Competency gates over time-based progression
• Project-based evidence of mastery
• Local-first model use when available
• Portable, shareable domain plans and learning artifacts

Initial architecture

The initial prototype is organized around six core services:

1. Domain Mapping Engine
   Builds a concept graph for a target field, including prerequisites, competencies, canonical problem types, and artifact templates.

2. Curriculum Generator
   Produces a staged learning roadmap adapted to learner goals and prior knowledge.

3. Mentor Agent
   Conducts Socratic dialogue, reviews reasoning, and offers targeted critique.

4. Practice Generator
   Produces exercises aimed at specific concepts and skill gaps.

5. Project Advisor
   Proposes and scaffolds real projects that demonstrate competence.

6. Evaluation System
   Scores explanations, problem solutions, project outputs, and transfer tasks against explicit rubrics.

Distribution model for contributed learning content

Didactopus is designed to support distribution of contributed artifacts, including:

• domain plans
• concept maps
• curriculum templates
• exercise sets
• project blueprints
• evaluation rubrics
• benchmark packs
• exemplar portfolios

These should be shareable as versioned packages or repositories so that contributors can publish reusable mastery paths for particular domains.

See:

• docs/artifact-distribution.md
• docs/domain-pack-format.md

Local model strategy

The codebase is designed to support a provider abstraction:

• Local-first: Ollama, llama.cpp server, vLLM, LM Studio, or other on-prem inference endpoints
• Remote optional: API-backed models only when configured
• Hybrid mode: local models for routine mentoring, remote models only for heavier synthesis or evaluation if explicitly allowed

Repository layout

didactopus/
├── README.md
├── LICENSE
├── pyproject.toml
├── Makefile
├── docker-compose.yml
├── Dockerfile
├── .gitignore
├── .github/workflows/ci.yml
├── configs/
│   └── config.example.yaml
├── docs/
│   ├── architecture.md
│   ├── repository-plan.md
│   ├── component-specs.md
│   ├── prototype-roadmap.md
│   ├── artifact-distribution.md
│   └── domain-pack-format.md
├── domain-packs/
│   └── example-statistics/
│       ├── pack.yaml
│       ├── concepts.yaml
│       ├── roadmap.yaml
│       ├── projects.yaml
│       └── rubrics.yaml
├── src/didactopus/
│   ├── __init__.py
│   ├── main.py
│   ├── config.py
│   ├── model_provider.py
│   ├── domain_map.py
│   ├── curriculum.py
│   ├── mentor.py
│   ├── practice.py
│   ├── project_advisor.py
│   ├── evaluation.py
│   └── artifact_registry.py
└── tests/
    ├── test_config.py
    ├── test_domain_map.py
    └── test_artifact_registry.py

Quick start

python -m venv .venv
source .venv/bin/activate
pip install -e .[dev]
cp configs/config.example.yaml configs/config.yaml
python -m didactopus.main --domain "statistics" --goal "reach practical mastery"
pytest

Prototype capabilities in this scaffold

The current scaffold provides:

• a configuration model for local/remote provider selection
• a concept graph data structure for domain maps
• stubs for curriculum, mentor, practice, project, and evaluation services
• a simple artifact registry for local domain-pack discovery
• an example domain pack layout
• a CLI entry point to demonstrate end-to-end flow
• tests to validate configuration and artifact behavior

Suggested first implementation milestones

Milestone 1: Learner and domain modeling

• learner profile schema
• concept graph generation
• prerequisite traversal
• domain-pack schema validation
• local artifact discovery

Milestone 2: Guided study loop

• Socratic mentor prompts
• explanation checking
• exercise generation by competency target
• evidence capture for learner work

Milestone 3: Project-centered learning

• capstone generator
• milestone planning
• artifact review rubrics
• distributed project pack ingestion

Milestone 4: Mastery evidence

• explanation scoring
• transfer tasks
• benchmark alignment
• progress dashboard
• artifact publication workflow

Notes on evaluation design

A key design choice is that the assessment layer should look for:

• correct explanations in the learner's own words
• ability to solve novel problems
• detection of flawed reasoning
• evidence of successful project execution
• transfer across adjacent contexts

Naming rationale

Didactopus combines didactic / didact with octopus: a central intelligence coordinating many arms of learning support.

License

MIT