Ideas, engineering observations, lessons, and expanded technical commentary from real-world systems experience.
Why trial-and-error prompt engineering fails, and how a disciplined three-tiered evaluation workflow transforms probabilistic models into stable, production-grade software.
A practical engineering guide to self-hosting Langfuse locally with Docker Compose and instrumenting multi-step Python LLM workflows with complete trace, session, and cost observability.
Why treating your vector database as a primary database is a critical architectural anti-pattern, and how to build a highly decoupled, idempotent semantic search pipeline using Qdrant.
Why stepping into 6.8°C water and co-programming with generative AI are driven by the same core principle: building deliberate systems that amplify decades of experience rather than remove human effort.
Why infinite context windows are a trap, and how the disciplined practice of externalizing knowledge and structured forgetting makes AI assistants more focused, precise, and reliable.
Why LLMs make terrible workflow controllers, and how to build a deterministic, choreographed pipeline that automates administrative overhead while preserving human architectural control.
Why aggregate customer ratings are a lagging illusion, and how tracking the temporal density of negative feedback serves as an early-warning radar for systemic operational collapse.
How to completely rebuild complex systems while keeping them operational, proving behavioural parity at every level before dismantling the past.
Why code complete is not context complete, and how to verify that your AI coding sessions produce durable, long-term engineering memory.
Why preserving design context, constraints, and rejected alternatives is critical when building software with autonomous AI coding agents.