Real-world engineering insights from 8+ years of building scalable systems, AI agents, ecommerce platforms, and backend architecture — written in my own words.
A practical introduction to MCP servers, tools, resources, prompts, security boundaries, and a TypeScript example for connecting AI applications to real systems.
A practical engineering guide to tool permissions, audit trails, evaluation, human approval, data boundaries, and lifecycle controls for AI agents.
Practical lessons from AI workflows: tool boundaries, state, retries, idempotency, observability, and keeping business rules outside the prompt.
A practical approach to turning business requirements into boundaries, APIs, orchestration, trade-offs, and a delivery plan.
How I think about using AI agents, Next.js, SEO, and consistent technical content to build a useful engineering presence.
A practical introduction to agent workflows, tools, retrieval, prompts, and the engineering considerations behind useful AI agents.
Production-focused TypeScript practices around strict mode, safer types, refactoring, and reducing runtime surprises.
A practical look at traffic spikes, caching, queues, database pressure, horizontal scaling, and graceful failure.
Redis patterns beyond simple caching, including queues, pub/sub, distributed locks, rate limiting, and session storage.
A hands-on introduction to building intelligent workflows with LangChain and understanding where agents fit in production systems.
Practical lessons for diagnosing slow queries, indexing, query plans, connection pressure, and database performance.