← Back to Study ForgeModules
Deep dives into specific domains. Non-linear learning arcs with multiple entry points.
Neural Architecture
ActiveUnderstanding how transformers, attention mechanisms, and emergent behavior work. Not just using AI—understanding the substrate.
Topics:
- • Attention mechanisms
- • Transformer architecture
- • Training dynamics
- • Emergent capabilities
- • Scaling laws
- • Prompt engineering
- • Fine-tuning strategies
- • Inference optimization
Key Resources:
- → "Attention Is All You Need" (Vaswani et al.)
- → "Scaling Laws for Neural Language Models" (Kaplan et al.)
- → Anthropic interpretability research
Distributed Systems
ActiveHow to build systems that scale, fail gracefully, and maintain consistency across network partitions.
Topics:
- • CAP theorem
- • Consensus algorithms
- • Eventual consistency
- • CRDTs
- • Vector clocks
- • Gossip protocols
- • Sharding strategies
- • Failure modes
Key Resources:
- → "Designing Data-Intensive Applications" (Kleppmann)
- → Raft consensus algorithm paper
- → Jepsen distributed systems tests
Quantum Cognition
PlannedExploring whether quantum mechanics explains consciousness, or if it's an elegant analogy that goes nowhere.
Starting Q2 2025