Hi, I'm Tim

We inherited a high-volume WooCommerce store running on a dedicated cloud instance. The client’s operations team had a simple but maddening complaint: editing a product took a few seconds, but clicking Update turned into a 30-second coffee break. For a team running 100s of inventory adjustments a day, that was a structural bottleneck slowing down their entire supply chain. We did what any experienced WordPress operator would do. We installed Query Monitor, loaded a product edit page, clicked Update, and waited to read the performance bar.

I was sitting with a lukewarm cup of coffee on a Tuesday morning, running what I figured would be a boring, 10-minute tag audit on our main e-commerce site. Then Google Tag Assistant flagged something that made no sense. Alongside our own Google Ads tag, a second tag with the ID AW-987654321 was firing on every single page view. That second ID belonged to our sister brand. They are a smaller, lower-priority setup under our corporate umbrella, selling a very similar catalog of products. I hopped over to their domain, loaded their home page, and watched the exact reverse happen: our primary brand’s tag, AW-123456789, was firing over there. Visitors looking at our high-margin main line were being quietly tagged for our cheaper secondary line, and vice versa. I had that familiar cold-sweat moment where you assume your browser cache is lying to you; I cleared everything, opened 3 incognito windows, and watched the exact same thing happen.

The number that started it # Plesk was showing 7294.2 MB/month of traffic for one of our internal WordPress sites — which would have made it one of the busiest domains on the server, except Google Analytics and Cloudflare both said the site was nearly dead. A handful of real visits, nothing more.

AI coding agents are runtimes with root-level terminal access. If you rely on file-level ignore lists to protect your secrets, you are one bad stack trace away from leaking live production credentials to third-party model providers.

After years of watching mentees chase platforms and tactics, the real work is always the same: strip the problem back to the human logic underneath. These are the principles I lead with, why they resist them, and where I have watched the gap cost people.

After finishing a newsletter with AI assistance, a question surfaced: what if AI doesn’t improve people but simply scales what they already are — examined or not? That turns out to be the unsettling part.

A high-capability model nearly shipped wrong information to real customers — not because it failed, but because it reasoned past a clear policy. The fix was a smaller, more predictable model. Here is what that taught me about model selection.

Anthropic’s interpretability research on internal activation patterns helps explain a pattern most AI coding tool users have hit: the agent passes all the tests, but the code is quietly broken. Here’s what that means for how you run these sessions.

AI isn’t eliminating junior engineering roles — it’s removing the accidental training system most companies relied on. Here’s the business model for rebuilding it deliberately, with measurable economics and a 90-day pilot structure.

AI isn’t replacing developers in one sweep — it’s dismantling the entry-level ladder that creates them. One year in with an AI coding assistant, here’s what I actually observed about who’s being affected, how, and why the consequences land years from now.

Why most AI projects fail: organizations are not data silos waiting to be cleaned, but complex political ecosystems. The real bottleneck is governance, not models.

Why superior product engineering loses to consistent perception. A look at how reputation and operational consistency now drive market share more than feature sets alone.

We often think “Human-in-the-Loop” (HITL) means having a human click “Yes” at the end. In this case, the HITL was the weakest link.

Decoupling our B2B customer portal from WordPress saved us from a massive database migration, but it introduced a complex web of logic drift and cache lag. Here is how we managed the coordination overhead.

Most WordPress staging setups test the wrong layer. This is what I learned building a production-fidelity workflow on a real Plesk server — including a Cloudflare incident that looked exactly like a WordPress problem until it didn’t.

Three sites, three slightly different copies of the same plugin, no clear canonical version. This is how we untangled it using Git subtree and wp-config.php constants — and what the process revealed about how plugin drift actually happens.

Most CMS staging failures are workflow failures, not technical ones. This covers the design decisions, the bash automation, and the wp_options audit that turned out to be the real work.

For a decade, SPAs were the default answer regardless of the problem. Most teams paid for that overcorrection slowly. Here is how the trade-offs actually look, what the shift back to SSR is really about, and how to make the architecture call at the start of a project.

What works for a solo developer or a team of 10 falls apart at enterprise scale. Here is how the bottleneck shifts from personal tooling to messy data pipelines and organizational coordination as you grow.

A WordPress 500 traced to PHP memory looked like a simple limit bump — until the real cause turned out to be a page builder serializing large CSS blobs into the object cache on every request.

An intermittent WordPress 500 that cleared on refresh turned out to be a single 4 MB Redis key growing without a TTL. Here is what the big-keys scan showed, why the mechanism is easy to miss, and the three config changes that stopped it.

A WordPress site was throwing intermittent 500 errors every ten days. The cache flushed clean each time, which masked the real problem: a single Redis key growing to 4MB and occasionally tipping PHP memory over the edge.

The WooCommerce performance failures that actually hurt at scale don’t show up in standard audits. They live in plugins doing unbounded per-request work that looks harmless at five requests per second and falls apart at twenty.

A WordPress site was reinfecting itself after every cleanup. The culprit was a self-healing backdoor in mu-plugins that reconstructed itself from an encoded payload stored in the database. Here is how I found it, killed it, and what I missed the first time.