"Architecture is the art of making decisions. Great architects document those decisions and share the reasoning."
We publish in-depth technical essays on architecture, systems design, and engineering practices. Our writing reflects lessons learned from building production AI systems.
Topics range from distributed system patterns to AI-specific architecture challenges, always grounded in real-world experience.
Common architectural patterns for production AI systems: model orchestration, context management, observability, and deployment strategies.
Designing data systems that support AI workloads: vector databases, embedding strategies, data pipelines, and quality management.
Building systems that evolve gracefully: refactoring strategies, migration patterns, and managing technical debt in production.
Optimizing AI systems for latency, throughput, and cost: caching strategies, batching, model optimization, and infrastructure tuning.
Documenting significant architectural decisions: the what, why, and trade-offs behind major design choices in our systems.
Essays provide concrete guidance, not abstract theory. Readers should leave with actionable insights.
Every architectural decision has trade-offs. We discuss both benefits and costs transparently.
Content comes from real production experience, not speculation. Learn from our successes and failures.