How It Works

The Study Forge has three core components: **Modules**, **Notes**, and **Connections**.

1. Modules

A **module** is a deep dive into a specific domain. Think of it as a learning arc with multiple entry points.

Module: Neural Architecture

├─ Attention mechanisms

├─ Transformer architecture

├─ Emergent behavior

├─ Training dynamics

└─ Scaling laws

Each module is **non-linear**. You can:

**Notes** are extracted patterns from source material. Not summaries—**distillations**.

Format:

Example: Consensus in Distributed Systems

Pattern: You cannot have consistency, availability, and partition tolerance simultaneously (CAP theorem)

Context: Distributed databases, microservices, blockchain

Connections: → Byzantine Generals Problem, → Eventual Consistency, → CRDT

Question: Can quantum entanglement bypass CAP limitations?

**Connections** are where understanding emerges. Link notes across modules, sources, and domains.

Example connections:

Consensus algorithms ↔ Neural network training (both solve coordination problems)
Wabi-sabi ↔ Technical debt (functional imperfection)
Observer effect ↔ Heisenberg uncertainty (measurement changes system)

These connections aren't planned—they **emerge from parallel processing**.

1. Capture

While reading/watching/building, extract patterns. Don't summarize—**distill**.

2. Connect

Link to related notes. Follow threads. Let knowledge graph grow organically.

3. Refine

Revisit notes. Update understanding. Delete what no longer serves (seiri).

4. Emerge

Understanding emerges from connections. You don't force insights—you create conditions for them.

The Study Forge is tool-agnostic. Use whatever supports:

The system matters more than the tool.

This workflow mirrors how you already think:

Stop fighting your architecture. Build for it.