AI Systems Engineer focused on designing production-grade AI application architectures.
My work explores how LLM-based systems can be structured into reliable, auditable software architectures.
- AI Workflow Architecture
- Contract-First System Design
- LLM Memory Systems
- State-Aware System Modeling
Primitive-level architecture specification for building production-grade LLM applications.
Core primitives:
- Session lifecycle
- Input canonicalization
- Deterministic compute
- Prompt assembly
- Streaming execution
- Billing & entitlement
Repository: https://github.com/crypto-Lin/AI-Workflow-Primitives
A local-first memory architecture for long-running human–AI collaboration.
Pipeline: dialog → facts → topic memory → global memory
Features:
- semantic fact extraction
- topic-level memory compression
- global memory synthesis
- append-only audit log
- human-in-the-loop governance
Repository: https://github.com/crypto-Lin/AI-Memory-System
AI Workflow Primitives
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Local AI Memory System
I focus on translating abstract system models into production-ready AI systems.
Key principles:
- Primitive-driven architecture
- Contract-first interfaces
- Explicit system state modeling
- AI governance & auditability
- LLM workflow orchestration
- memory systems for AI agents
- production AI architecture patterns