Every system operates on assumptions. Some are explicit—enforced by types, schemas, and validation. But the dangerous ones are implicit: "events are ordered," "usernames are lowercase," "this array is sorted." These invisible boundaries work perfectly until someone crosses them. Then a service that ran flawlessly for months suddenly explodes on "valid" data. The bug isn't in your logic—it's in the gap between what your system claims to accept and what it actually handles. Good architecture closes this gap by making boundaries impossible to cross incorrectly, not just expensive to cross incorrectly.

No platitudes, no theory. Just the exact system, templates, and tools I use to turn daily development work into a searchable knowledge base and high-quality blog posts.

Go beyond basic reliability. This is a complete operational manual for your Rails outbox, covering the four critical metrics, production-grade processor design with Sentry, forensic runbooks, and the anti-patterns that lead to silent failures.

Implicit state is a breeding ground for bugs. We'll refactor a typical Rails model to use a production-ready state machine, covering migrations, race conditions, testing, and audit trails to make your code safer and easier to reason about.

The app/services directory starts with good intentions but often decays into a junk drawer. We'll diagnose the symptoms of a service directory in crisis and explore how patterns like Form Objects, Commands, and Query Objects can restore clarity and maintainability to your Rails app.

Move beyond hardcoded strings and build a scalable, version-controlled, and testable prompt management system in Rails to manage your AI prompts like professional software.

A hardcoded prompt typo cost us $2,400 in 72 hours. Here's the engineering discipline, testing strategies, and open-source tool (Promptly) we built to fix it for good.

Your async AI feature works... until it doesn't. This guide covers how to debug unreported job failures, trace Action Cable WebSocket issues, and handle common pitfalls in production.

A deep dive into architectural patterns using ActiveJob and Redis to solve LLM latency, moving from an 8-second wait to a sub-50ms cached response.

Most function calling tutorials show toy examples that break in production. This guide provides the 80% foundation you actually need: a security-hardened, multi-turn agent with complete parameter validation, proper error handling, logging, and timeouts. Copy, customize, and build on it