Technical articles, tutorials, and thoughts on software engineering
Full-Stack Development
The most effective AI-powered products are rarely built by an AI specialist alone — they need the full-stack discipline to actually ship as real software.
AI Automation
Most "AI automation consultants" are reselling Zapier workflows with a markup. Here's how I'd vet one as a buyer: the audit they should run, what pricing models really cost you, and the questions that expose thin expertise.
A hands-on guide to hiring an AI engineer: how to define the role, vet real LLM and agent experience, run a take-home with a scoring rubric, spot red flags, and choose between full-time, contract and fractional.
A practical, non-academic rundown of the system design concepts that actually come up in day-to-day full-stack engineering work.
There's no single number, but there is a reliable framework for estimating it based on scope, integrations, and team composition.
NestJS's structured, modular architecture is a good fit for backend services that need to integrate LLM calls, queues, and external APIs cleanly.
React's ecosystem has enough depth and enough footguns that genuine specialization produces measurably better outcomes than general full-stack familiarity.
A concrete, prioritized checklist for diagnosing and fixing the most common Next.js performance issues.
Next.js bundles routing, rendering strategy, and API endpoints into one framework — here's what that actually saves you in practice.
Model choice isn't about which is 'smartest' in the abstract — it's about which fits your specific task, cost constraints, and deployment requirements.
Adding an LLM feature is easy to prototype and surprisingly expensive to run reliably at scale. Here's what actually shows up on the bill.
Before building an AI automation, do the math on what it actually saves versus what it costs to build and run — here's a practical framework.
