Database Design Patterns: Repository, Unit of Work, and Data Mapper
A comprehensive guide to database design patterns including Repository pattern, Unit of Work, Data Mapper, and Active Record for building maintainable data access layers.
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Distributed Transactions Patterns: SAGA, Two-Phase Commit, and Eventual Consistency
A comprehensive guide to distributed transaction patterns including SAGA pattern, two-phase commit, eventual consistency, and compensation mechanisms for microservices.
Domain-Driven Design: Building Domain-Centric Applications
A comprehensive guide to Domain-Driven Design including entities, value objects, aggregates, repositories, and domain events for complex business logic.
Efficient Long-Context LLM: Strategies for Million-Token Contexts
Learn efficient long-context techniques: sliding window attention, hierarchical methods, sparse attention, KV cache optimization, and dynamic sparse attention for on-device …
Event Sourcing: Building Systems Around State Changes
A comprehensive guide to event sourcing including event stores, aggregate reconstruction, snapshots, and CQRS patterns for audit-ready systems.
Event-Driven Architecture: Building Responsive Systems with Events
A comprehensive guide to event-driven architecture including event sourcing, CQRS, message patterns, and practical implementation strategies for modern distributed systems.
Function Calling and Tool Use: Building Agentic LLM Systems
Learn ReAct patterns, function calling protocols, tool orchestration, and building AI agents that can reason, act, and observe in loops.
Gated Linear Attention: Efficient Transformers with Data-Dependent Gating
GLA combines linear attention efficiency with learned gating for expressivity. Learn how it achieves RNN-like inference with transformer-like training.
GraphRAG: Knowledge Graph Enhanced Retrieval-Augmented Generation
GraphRAG achieves 85%+ accuracy vs 70% for vector-only RAG. Learn knowledge graph construction, hybrid retrieval, entity extraction, and multi-hop reasoning for enterprise AI.
Knowledge Distillation: LLM Compression and Efficient Transfer
Distill large LLMs into compact students. Learn teacher-student frameworks, distillation techniques, temporal adaptation, low-rank feature distillation, and deployment strategies.