Autodidact

Status: ✅ COMPLETE (Phase 1 Foundation)

Ultimate Goal: Build a self-directed learning system that adapts to how each individual learns — making complex knowledge accessible through personalized visualizations, pacing, and pathways.

The autodidact module is the educational arm of the ONI Framework. It's not just documentation — it's a living system for learning and teaching neurosecurity concepts, built on the principle that education should adapt to the learner, not the other way around.

AI-Powered Learning Pipeline

Autodidact leverages AI and automation to accelerate content creation and personalization:

This isn't AI replacing learning — it's AI enabling self-directed learning at scale.

The Vision

┌─────────────────────────────────────────────────────────────────┐
│                    AUTODIDACT ECOSYSTEM                          │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│   ┌─────────────────────────────────────────────────────────┐   │
│   │                   LEARNER PROFILE                        │   │
│   │   • Learning style (visual/verbal/kinesthetic)          │   │
│   │   • Pace & comprehension tracking                       │   │
│   │   • Knowledge graph (what you already know)             │   │
│   │   • Preferences (animation speed, detail level)         │   │
│   └─────────────────────────────────────────────────────────┘   │
│                            │                                    │
│                            ▼                                    │
│   ┌─────────────┐   ┌─────────────┐   ┌─────────────┐          │
│   │  LEARNVIZ   │   │ ONI ACADEMY │   │ NEUROSCIENCE│          │
│   │  Adaptive   │◀──│  Structured │◀──│   RESEARCH  │          │
│   │  Visuals    │   │  Curriculum │   │  Foundation │          │
│   └─────────────┘   └─────────────┘   └─────────────┘          │
│         │                 │                 │                   │
│         └─────────────────┼─────────────────┘                   │
│                           ▼                                     │
│   ┌─────────────────────────────────────────────────────────┐   │
│   │              PERSONALIZED LEARNING PATH                 │   │
│   │   • Auto-adjusting difficulty                           │   │
│   │   • Visualizations tailored to your style               │   │
│   │   • Pacing based on your comprehension                  │   │
│   │   • Prerequisites auto-detected from knowledge graph    │   │
│   └─────────────────────────────────────────────────────────┘   │
│                                                                 │
│   [100% Local — Your learning data never leaves your machine]   │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Components

1. LearnViz — Adaptive Visualization Engine

Purpose: Generate educational visualizations that adapt to individual learning behaviors.

Current (v0.2):

• Concept → Code → Video pipeline
• Pattern-based concept classification
• 8 Manim templates (binary search, sorting, Pythagorean, trees, action potential, synapse, motor cortex BCI, neurotransmitters)
• Local rendering with Manim
• Voice narration (edge-tts, gtts, pyttsx3)
• Ollama integration for custom AI-generated visualizations
• Web UI (Streamlit)

Future (v0.4+):

• Learner profiles with style/pace tracking
• Visualizations that adjust to YOUR learning patterns
• Local LLM integration for custom explanations
• Interactive mode with Q&A

Location: learnviz/

# Example usage
cd learnviz
python learnviz.py "Explain binary search" --render

2. ONI Academy — Structured Curriculum

Purpose: Provide structured learning paths for neurosecurity concepts, from basics to advanced.

What it offers:

• Learning modules (14-layer model, coherence metric, neural firewall, etc.)
• Interactive web tools (no installation required)
• Python API for programmatic access
• CLI for quick exploration

Location: oni-academy/

# Install
pip install oni-academy

# List modules
oni-academy list

# Launch UI
oni-academy ui

Web Tools (no install):

• Coherence Metric Playground
• ONI Layer Explorer
• NSAM Checkpoint Simulator

3. Neuroscience-BCI — Research Foundation

Purpose: Personal research repository for understanding the biological substrate that BCIs interface with.

What it contains:

• Brain region documentation (motor cortex, limbic system, etc.)
• BCI research notes (how electrodes work, signal decoding)
• Visualization projects (Blender 3D, Manim 2D)
• Key questions the research aims to answer

Location: neuroscience-bci/

Why it matters: You can't secure what you don't understand. This research feeds directly into ONI Academy content and LearnViz visualizations.

How They Align

┌──────────────────────────────────────────────────────────────────┐
│                     KNOWLEDGE FLOW                               │
├──────────────────────────────────────────────────────────────────┤
│                                                                  │
│   NEUROSCIENCE-BCI                                               │
│   ┌────────────────────────────┐                                 │
│   │ • Raw research notes       │                                 │
│   │ • Brain region anatomy     │                                 │
│   │ • "Figure out what I       │                                 │
│   │    don't know"             │                                 │
│   └────────────┬───────────────┘                                 │
│                │ (research matures into curriculum)              │
│                ▼                                                 │
│   ONI ACADEMY                                                    │
│   ┌────────────────────────────┐                                 │
│   │ • Structured modules       │                                 │
│   │ • Learning paths           │                                 │
│   │ • Interactive web tools    │                                 │
│   │ • pip-installable package  │                                 │
│   └────────────┬───────────────┘                                 │
│                │ (curriculum generates visualizations)           │
│                ▼                                                 │
│   LEARNVIZ                                                       │
│   ┌────────────────────────────┐                                 │
│   │ • Adaptive visualizations  │                                 │
│   │ • Learner profiles         │                                 │
│   │ • Custom pacing/style      │                                 │
│   │ • Local-first, private     │                                 │
│   └────────────────────────────┘                                 │
│                                                                  │
│   ════════════════════════════════════════════════════           │
│   ULTIMATE OUTPUT: Personalized learning experience              │
│   that adapts to each individual's cognitive patterns            │
│   ════════════════════════════════════════════════════           │
│                                                                  │
└──────────────────────────────────────────────────────────────────┘

The Feedback Loop

Design Principles

1. Local-First

All computation and learner data stays on your machine:

• No cloud dependency
• Works offline
• You own your learning data
• Privacy by default

2. Adaptive by Default

Every component eventually adapts to the learner:

• Pace: How fast you comprehend
• Style: Visual vs verbal vs kinesthetic
• Depth: Beginner-friendly or expert-level
• History: Skip what you already know

3. Open & Extensible

• Add your own research to neuroscience-bci
• Contribute modules to oni-academy
• Create templates for learnviz
• All Apache 2.0 licensed

4. Research → Curriculum → Visualization

Knowledge flows from raw research to structured curriculum to adaptive visualizations. Each stage builds on the previous.

Roadmap

Phase 1: Foundation ✅

• ONI Academy pip package with learning modules
• Interactive web tools (browser-based)
• Neuroscience research repository structure
• LearnViz v0.1 (concept → video pipeline)

Phase 2: Content Expansion

• More LearnViz templates (graphs, recursion, physics)
• Expanded ONI Academy modules
• Brain region deep dives (motor cortex → full)
• Cross-linking between components

Phase 3: LLM Integration

• Local LLM for custom LearnViz scenes
• AI tutoring in ONI Academy
• Research synthesis assistance
• Natural language → visualization

Phase 4: Adaptive Learning ⭐ Core Goal

• Learner profile implementation
• Pace/style tracking
• Knowledge graph per user
• Auto-adjusting difficulty
• Personalized learning paths

Phase 5: Full Integration

• LearnViz ↔ Academy deep integration
• Research notes auto-generate curriculum
• Community template sharing (opt-in)
• Cross-platform sync (still local-first)

File Structure

autodidact/
├── README.md                    # This file — ecosystem overview
│
├── learnviz/                    # Adaptive visualization engine
│   ├── README.md                # Vision, implementation, roadmap
│   ├── learnviz.py              # CLI orchestrator
│   ├── analyzer.py              # Concept classification
│   ├── generators/              # Code generators (Manim, Remotion, D3)
│   └── output/                  # Generated scripts
│
├── oni-academy/                 # Structured learning platform
│   ├── README.md                # Package documentation
│   ├── ONI_ACADEMY.md           # Full curriculum guide
│   ├── oni_academy/             # Python package source
│   └── tests/                   # Unit tests
│
├── neuroscience-bci/            # Research foundation
│   ├── README.md                # Research roadmap
│   ├── brain-regions/           # Anatomical documentation
│   │   ├── cerebral-cortex/     # Motor, sensory, visual, etc.
│   │   ├── limbic-system/       # Amygdala, hippocampus
│   │   └── ...                  # Other regions
│   └── visualizing-the-mind/    # BCI zoom animations
│       ├── 3D-mindmapper/       # Blender renders
│       └── 2D-mindmapper/       # Manim animations
│
└── neuroscience-homework-todo/  # Active research tasks

Getting Started

For Learning

# Install ONI Academy
pip install oni-academy

# Explore modules
oni-academy list
oni-academy ui

Or just use the web tools — no installation required.

For Creating Visualizations

cd autodidact/learnviz

# Install Manim
pip install manim

# Generate a visualization
python learnviz.py "Binary search algorithm" --render

For Contributing Research

1. Add notes to neuroscience-bci/brain-regions/[region]/
2. Use Notes-[Topic].md for informal notes
3. Use Research-[Topic].md for structured research
4. Tag questions with [Q] and unknowns with [?]

Philosophy

"The best teacher adapts to the student, not the other way around."

Traditional education assumes everyone learns the same way. Autodidact rejects this assumption. By tracking how you learn — your pace, your preferred modalities, what you already know — we can generate educational experiences tailored specifically to you.

This isn't about replacing teachers. It's about augmenting self-directed learning with tools that understand your unique cognitive patterns.

The ultimate goal: Anyone, anywhere, can learn neurosecurity concepts at their own pace, in their own style, without barriers.

Related

• ONI Framework — The security framework these tools teach
• TARA Platform — Security monitoring & simulation
• GitHub Pages — Live interactive tools

Part of the ONI Framework

"Autodidact: Learn how you learn, then learn faster."