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How Domain-Driven Design principles can shape a cleaner, more meaningful front-end architecture.

After hundreds of hours of AI-assisted development, I expected to write less code. Instead, I changed how I plan, review, and reason about it.

There is no handbook for wiring an external DAM into Optimizely CMS 12. This case study walks through the research, dead ends, and breakthroughs — decompiling the CMP package, extending ContentReferenceEditor, proxy content items, and a custom Assets pane tab — using Wedia as the concrete example.

A front-end developer learns NestJS, MongoDB, and Kafka by building a full-stack user analytics platform in a TypeScript monorepo.

Lighthouse added an Agentic Browsing category that measures how well your site works for user-facing AI agents, from llms.txt and WebMCP to accessibility and layout stability.

Google I/O Connect Berlin and the GDE Europe Summit showed how coding agents can drive Chrome directly through chrome-devtools-mcp, and how Modern Web Guidance keeps agents from reaching for stale patterns in the first place.

Optimizely CMS has a little-known attribute that reloads the editor when a property changes — perfect for dependent dropdowns and checkboxes. Here is how I found it, proved it worked, and shipped it on a Product Detail Page.

Fourteen talks, two days, one city. Color spaces, a browser built from scratch, CSS that runs x86 machine code, a select that took 33 years, and a masonry layout that is finally real. This was CSS Day 2026.

Code was never the real bottleneck. The true constraints are requirements, legacy systems, and operational complexity, which means developers must now shift from writing code to directing AI, validating its outputs, and making architectural decisions.

Agentic coding is making code generation cheaper, but it is raising the value of judgment, systems thinking, and accountability. This article explores why engineering is not being commoditized and what agentic engineering looks like in practice.

Developers often default to the most powerful AI models without thinking about the real cost behind each call. This article explores why understanding model tiers and pricing matters for smarter, more efficient engineering decisions.