Blog

WordPress Development Insights from the Trenches

I’m Ahmad Wael, a WordPress developer with 15+ years of experience building complex WooCommerce stores, custom plugins, and AI-powered solutions for clients worldwide. This blog shares real-world lessons from actual projects—not theoretical tutorials.

You’ll find in-depth guides on WordPress AI integration, WooCommerce optimization, plugin architecture, PHP best practices, and modern development workflows. Every article comes from solving actual client problems, with code examples you can use immediately.

Whether you’re integrating AI agents into WordPress, managing technical debt in legacy codebases, or building scalable WooCommerce solutions, these insights will save you hours of debugging and research.

Development

Publishing a VS Code Extension: The Senior Dev’s Guide

Publishing a VS Code extension is surprisingly complex, involving Azure DevOps PAT tokens and manifest strictness. In this pragmatic guide, Ahmad Wael shares war stories from the registry trenches, including workarounds for CLI login errors, manual VSIX packaging, and ensuring your theme’s README images actually load in the Marketplace and Open VSX.

AI, Development

Rotary Position Embedding Explained: Going Beyond the Math

Rotary Position Embedding (RoPE) is the architecture behind modern LLM context windows. By using geometric rotation instead of absolute index addition, RoPE allows models to understand relative token distance more effectively. This guide breaks down the intuition, the math, and the Python implementation for senior developers looking to optimize their transformer-based backends.

AI, AI in WordPress

WordPress AI Guidelines: What Developers Need to Know

The new WordPress AI Guidelines have officially landed in the handbook. Ahmad Wael breaks down the five core principles—Responsibility, Disclosure, Licensing, Asset Coverage, and Quality—that every developer needs to know before contributing code or assets to the WordPress ecosystem. Learn why GPL compatibility is non-negotiable and how to avoid the “AI slop” trap.

AI, Development

Fix the 17x Error: Multi-Agent Systems Scaling Guide

Learn how to avoid the “Bag of Agents” trap and scale Multi-Agent Systems effectively. Based on DeepMind’s research, discover why coordination structure matters more than agent quantity and how to suppress 17x error amplification using functional planes and a centralized orchestrator for robust, performant agentic AI.

AI, Development

Distributed Reinforcement Learning: Scaling Real Systems

Distributed reinforcement learning is more than just parallelizing code; it is about solving the synchronization bottleneck. Learn why naive parallelism fails in real-world policy optimization and how to implement Actor-Learner architectures with V-trace for high-performance, asynchronous training that doesn’t melt your server infrastructure. Stop waiting for rollouts and start scaling.