Writing
Deep-dives on software architecture and the way source code is structured — written to be understood by beginners, yet useful to teams shipping at scale. Diagrams, real examples, no hand-waving.
A reflective essay on why reading code often feels harder than writing it, and how patient tracing, tests, naming, history, and small notes help engineers understand a codebase without rushing to rewrite it.
A practical comparison of prompting, RAG, and fine-tuning for AI products: what each approach changes, when it helps, where it fails, and how teams can choose the smallest reliable intervention.
A calm look at engineering onboarding that helps people become useful without feeling lost: context, small early wins, clear ownership, buddy support, documentation, feedback loops, and patient team habits.
A practical explanation of the Saga pattern for distributed transactions: why one database transaction stops working across services, how orchestration and choreography differ, and why compensation, observability, and idempotency matter.
A practical look at retrospectives that produce visible follow-through: small experiments, clear action owners, trust, decision records, and measurable behavior change instead of another list of forgotten complaints.
A practical reflection on mentoring junior developers through context, calibrated support, confidence, code review, recoverable mistakes, independence, and psychological safety without turning mentorship into control.
A practical article on product ethics for AI teams: consent, data boundaries, bias and fairness, human review, explanations, risk controls, and incentives that shape whether AI features remain useful and responsible.
A calm guide to async communication for remote teams: writing enough context, recording decisions, setting response expectations, improving handoffs, building trust, and protecting people from always-on pressure.
A practical look at API gateway responsibilities: routing, authentication boundaries, rate limiting, request aggregation, backend-for-frontend trade-offs, failure modes, and the observability needed to keep the edge of a system understandable.
