Practical writing on AI coding & modern dev workflows

A production-minded guide to containerizing a Next.js 15 app with standalone output and running it on AWS ECS Fargate — Dockerfile, task definition, and the gotchas that bite in production.

A practical comparison of Claude Code and Cursor based on real day-to-day use — where each one shines, where it gets in the way, and how to decide which belongs in your workflow.

A hands-on guide to writing a Model Context Protocol server in TypeScript — exposing tools an AI assistant can actually call, and wiring it into Claude and other MCP clients.

Used deliberately, AI models are surprisingly good at finding the bugs a tired human reviewer skips — off-by-ones, missing awaits, unhandled edge cases. Here is the workflow that makes it reliable.

After shipping three production applications on the OpenAI API, the lessons that stick are not about prompts or models — they are about rate limits, cost control, and the things that break under real traffic.

A grounded look at where large language models earn their keep in a working developer's day — the tasks worth delegating, the ones worth keeping, and the workflow that ties them together.

ChatGPT Projects changed how I use the tool daily — persistent instructions, shared context across conversations, and a much cleaner way to separate different kinds of work.

LangChain promises to make building LLM apps faster. Sometimes it does. Here is an honest look at when the framework earns its complexity and when going direct is the better call.

Beyond autocomplete — how to use ChatGPT as a genuine engineering tool for debugging, design review, and grinding through the tedious parts of a project without shipping subtle bugs.