Blog

Thoughts on Python, Agentic Systems, AI Projects, and Web Development.

• Prompt People vs AI-Augmented Architects
  2026-04-12

  Coders are not all becoming prompt people. The real split is between operators who only talk to models and engineers who can orchestrate systems, constraints, KPIs, and architecture.

• Codex, Code Atrophy, and the Senior Developer's Job
  2026-04-12

  Codex can speed up delivery, but senior developers still need to understand the inner workings: data flow, state, architecture, and why the code works.

• The Pragmatic Developer's Guide to Surviving the AI Era (Without Becoming an AI Engineer)
  2026-04-08

  Not everyone needs to build Python AI agents to stay relevant. Here is a battle-tested roadmap for leveraging enterprise legacy systems, migrating to edge services, and escaping the AI coding squeeze.

• The New Developer Workflow: Shipping with Codex, Issues, and LLM-First Execution
  2026-03-18

  Modern developers are no longer just writing code. They're framing problems, writing better issues, steering tools like Codex, reviewing diffs, and managing an end-to-end workflow with AI in the loop.

• Building Ghostfrog eBay Edge: An eBay Intelligence Tool for Sellers Who Want to Move Faster
  2026-03-18

  Ghostfrog eBay Edge is being built as a focused intelligence product for sellers who want clearer market signals, stronger listings, and faster, evidence-led decisions.

• Building a Magento 2 UCP Extension (Docker Dev): The Plan for a Best-in-Class Checkout Experience
  2026-01-27

  We're building a Magento 2 UCP adapter (Universal Commerce Protocol) inside a local Docker setup. This tutorial explains the goal, the architecture, and the step-by-step plan to deliver a faster, cleaner checkout experience — without adding extra services or Python.

• From eBay V1 Scripts to a Proper Pipeline: Introducing LangGraph
  2025-12-27

  After proving the concept with a first-pass eBay scraping

• LLM Runtime Glossary: The Stuff Nobody Explains
  2025-12-17

  A plain-English glossary of LLM runtime, API, and inference terms you actually encounter when building real systems with local and hosted models.