Every headless WordPress conversation starts the same way — someone draws an architecture diagram with arrows pointing from a REST API to a shiny Next.js frontend, and it looks clean. Too clean. This is a post about what happens when you close the whiteboard and open the actual codebase. The Stack Decision: GraphQL vs. REST vs. Direct MySQL This is usually the first fork in the road. For this build, the client already had a well-indexed WooCommerce site. The product catalog, slugs, and taxonomy structure were already doing heavy SEO work. So the constraint was simple: nothing about the data layer changes, only how we consume it. WPGraphQL was a real option — but it meant adding a plugin dependency to a WordPress install we were actively trying to slim down. The WP REST API was already there, no installation required, and exposed exactly what we needed: products, categories, pages, and media — all queryable by slug. The decision: WP REST API, consumed server-side via Next.js fetch in Server Components. // Fetching a product by slug — preserving the existing URL structure const res = await fetch ( ` ${ process . env . WP_API_BASE } /wp/v2/product?slug= ${ params . slug } &_embed` , { next : { revalidate : 3600 } } ); const [ product ] = await res . json (); No new dependencies on the WordPress side. The legacy install runs as a lean shell — no active theme, minimal plugins, just the REST API and the data. The Site Kit Problem: Bridging Familiar Workflows This is where most migrations quietly fail the client. The previous team lived inside WordPress admin. Google Site Kit gave them traffic stats, Search Console data, and Analytics — all surfaced in a UI they knew. Ripping that away and telling them "just use Google Analytics directly" is a workflow regression, not an upgrade. The pivot here was building a lightweight admin dashboard as part of the Next.js project — not a full replacement for Site Kit, but a mirror of the metrics they actually checked daily: Page views

10 Prompt Patterns I Use Every Single Day Last Tuesday I spent 40 minutes arguing with Claude about a database schema before I realized I had never told it what I already tried. I described the problem, it gave me the same three suggestions I had already ruled out, I pushed back, it apologized and gave me variations of the same three suggestions. The entire session was garbage because I started from zero instead of from where I actually was. I closed the tab, rewrote my first message, and had a working solution in six minutes. That gap — between how most people prompt and how it actually works when you treat the model like a collaborator who needs real context — is what this post is about. Pattern 1 & 2: Lead With What You Already Tried, and State the Constraint That Binds You These two patterns are almost always used together, so I won't pretend they're separate. When you describe a problem without the history of your attempts, you are forcing the model to rediscover your dead ends. Every developer knows this frustration: you explain a bug, get back a solution you tried on Monday, explain you tried that, get a variation, explain that too — it's a recursive waste. The fix is brutal honesty upfront: "I need X. I already tried A and B. A failed because [specific reason]. B is off the table because [constraint]. Don't suggest either." The constraint layer is the other half. Models are optimists by default. They will give you the architecturally clean, perfectly testable solution that requires three new dependencies and a refactor of your auth layer. Unless you tell them you are shipping in two days, can't add dependencies, and the code needs to be readable by someone who last touched Python in 2019. Constraints aren't limitations on the answer — they are the answer. Front-load them or you will spend the session rejecting suggestions that are technically correct but situationally useless. Pattern 3 & 4: Output First, Reasoning After — and Diff Only, Not Rewrites Two sid

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Those ousted included ADA journal editor-in-chief Steven Kahn and former ADA president Desmond Schatz

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Even with Lockdown Mode, ChatGPT could be still vulnerable to prompt injections, but the goal is to reduce the likelihood that sensitive data gets shared in the process.

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Details for a potential deal haven't been finalized yet.