13 posts
The full arc from prototype to production: choosing the right model, designing prompts that hold up under real traffic, managing context windows, handling failures, and deploying without burning through your budget.
The core signals to collect, how to instrument from day one, and the practical telemetry pipeline that takes you from raw data to lower MTTR and higher reliability, written from the pager, not the ivory tower.
How to find clean domain boundaries, apply the strangler fig pattern, split a shared database, and set up tests, observability, and rollback so every step of the migration stays reversible.
The eight deployment mistakes that keep showing up in postmortems, from untested migration rollbacks to missing observability, what each one actually costs, and the specific changes that prevent them.
Four production microservices patterns, the API gateway, service mesh, circuit breaker with bulkhead, and saga, with the failure mode each one addresses, the real trade-offs, and the tools that map to each.
A repeatable 7-step framework for reasoning through any system design problem, whether you have seen it before or not: scope it, estimate capacity, design, go deep on the right things, and stress-test before you finish.
Five microservices disadvantages backed by real numbers: operational cost explosion, distributed tracing pain, data consistency failure modes, organizational overhead, and the latency tax, each with a mitigation.
A repeatable 45-minute framework for system design interviews: how to clarify scope, run capacity math on a whiteboard, and reason about trade-offs in a way that signals senior-level thinking.
A staged framework for scaling from a single server to a globally distributed architecture, with concrete decision checkpoints, capacity planning math, and observability triggers at every milestone.
A structured breakdown of the topics architecture interviewers actually test, with representative questions, scoring rubrics, and a realistic two-week prep plan.
Most system design resources teach you enough to draw boxes on a whiteboard. This guide covers what actually breaks under load and how engineers make real trade-offs in production.
A ground-up walkthrough of the LLM stack: tokenization, transformer internals, training pipelines, and inference optimization, from an engineering perspective.
The signal is live. Here's what to expect from imlucas.dev: AI deep-dives, system design breakdowns, and lessons from the trenches.