Planned
Production System Foundations
Most incidents trace to missing fundamentals, not missing scale patterns — boring production competence is the highest-leverage engineering.
Plannedoutline forthcoming
Planned
Knowledge maps, not tutorials
Atlases of production engineering — prose for humans, ontology for machines.
The atlas library
Each atlas is a deep, code-first book — but they share one knowledge graph, so a concept you learn in one shows up, connected, in the others.
Most incidents trace to missing fundamentals, not missing scale patterns — boring production competence is the highest-leverage engineering.
Architectural patterns proven at consumer scale — prose for humans, ontology for machines.
Client engineering is distributed systems on a fleet of devices you don't control, shipped through a deploy pipeline you don't own.
Teams adopt events for decoupling and inherit distributed-systems problems they didn't sign up for; this atlas teaches when events earn their cost.
A data platform is the system that turns raw operational exhaust into trusted business memory.
Observability is not three kinds of telemetry; it's the ability to ask new questions of a system without shipping new code.
Security is a systems property, not a gate — learnable by the same engineers who learned caching and retries.
Quality is not testing at the end; it's a portfolio of validation investments, each with a cost and an ROI.
Models are the easy part — ML systems fail as systems: data, serving, feedback loops, drift.
Relevance is a product problem wearing infrastructure clothes.
The algorithm is a minority of the outcome — inventory, cold start, feedback loops, and measurement dominate.
A/B testing is easy; trustworthy experimentation at org scale is a systems and culture problem.
GPUs are a scarce, expensive resource you redesign around — the accelerator as a supply chain, not a server; the hardware substrate beneath the AI-era stack.
Production LLM systems are 10% model calls and 90% systems around a component that is confidently wrong.
Agents are distributed systems where one node is stochastic; autonomy is a budget you spend, not a feature you enable.
AI doesn't remove engineering judgment; it relocates it — from writing code to specifying, verifying, and owning systems you didn't type.
Platforms are products with customers who are free to leave; golden paths beat mandates.
Enterprise software is different physics — buyer ≠ user, tenant heterogeneity, and decade-long lifecycles reshape every architectural decision.
Org design is system design — Conway's law runs in both directions, and most "technical" problems are org structures leaking into architecture.