Writing
Long-form, practical writing from real engineering work — architecture, AI, business systems and the stack behind them.
You do not need a rewrite to add AI to a Laravel or Next.js application. A step-by-step approach: find the right feature, isolate the model behind a service, validate outputs, handle cost and failure, and ship behind a flag.
Tenancy, billing, background work, environments and the boundaries that let a SaaS product grow without a rewrite. A practical architecture for a first version that is built to last, with Next.js, Laravel and PostgreSQL as the reference stack.
When a generic CRM stops fitting, a custom one is often smaller than people expect. The data model, pipeline design, integrations and reporting that make a custom CRM worth building — and the signs that you should not.
Underneath the hype, an AI agent is a loop: a model chooses a tool, the tool runs, the result goes back in. Here is that loop in detail, the engineering that makes it safe, and how to decide whether you need an agent at all.
Retrieval-augmented generation explained from the engineering side: ingestion, chunking, embeddings, hybrid search, reranking, permissions and citations — and the decisions that determine whether a knowledge base actually answers questions correctly.
A practical architecture for AI applications that do real work inside a business: where the model sits, how it reaches your data and tools, and what has to be true before it goes to production.
A short conversation is usually enough to work out whether the idea needs a prototype, a product or a different approach entirely.