Posts

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.

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.

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.

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.

A mailflow rule meant to catch spoofed mail started blocking legitimate transactional email from a third-party shipping provider. The regex logic was sound in isolation. The infrastructure gap it exposed was not.

A WordPress site on our infrastructure was compromised via an abandoned theme. This is a walkthrough of how I found the malicious code, what it was actually doing, and the harder question I still can’t fully answer: whether any real damage was done before we found it.

How we stopped chasing high-volume retail leads, rebuilt our HubSpot tracking, and focused on the 120 trade accounts that actually drive our revenue.

This morning, as soon as I stepped into the office, my team member informed me that one of our important websites had been hacked, and malicious JavaScript code had been injected into ALL .js files. It was so bad that Google flagged the site

You cannot add the GPU to the virtual machine if the GPU is bound to the host kernel driver, so you must unbind the GPU device from the host before you can add it to the virtual machine.

Spinning up initial lab environment with basic Terraform & Ansible adhoc commands. This spins up two freeIPA server and 1 DNS server, along with a virtual network to tie things together

After upgrading our server from Centos7 to Almalinux 8, one particular page would sometimes present an error when trying to access it

Every action in marketing or business carries its own risks. If we ignore risk management, the consequences can be significant when risks materialize, potentially leading to the failure of a campaign or even jeopardizing the very existence of a company.

While maintaining repeatability and consistency

I was learning more about ZFS while applying what I learned to my OWN data. Of course, as Murphy’s Law states… so here are the lesson from a fool.

Installing NVIDIA GPU driver on AlmaLinux is a pain in the ass as there are so many sources with different approaches. These steps worked for me

During installation of Alma Linux, I noticed that it created a logical volume (LV) by default for /home spanning across 2 disks. One of it is failing.

After installing zfs-utils in Alma Linux, I realized I need to sign zfs module because of secure boot. Here’s how

I decided to take the hard way to understand each line of the XML file and the proper way to spin up KVMs instead of with GUI

Ever walked into a store and marveled at the plethora of choices available? The idea is that more options make decisions easier, right? Well, not quite.

Which linux directory to back up # /etc /home /root /var /usr/local/bin and sbin /srv Note: If there are any media mount points in the system such as ‘/mnt’ and ‘/media’, they should be excluded to avoid infinite loop Also helpful to list down the names of packages installed on your system dpkg --get-selections > package_list.txt Backup command options # tar, rsync

One thing I’ve noticed while going through the Azure Fundamental is the importance of non-technical aspects in adopting and migrating to cloud

Cloud skills are transferable. Don’t have a mindset of learning one. So just pick one and get a beginner cert.

In many ways, us marketers have “trained” our customers to be less loyal

Do these things and you will be fully exposed to every aspect of Linux Enterprise systems administration. Do them well and you will have the technical expertise required to seek “Senior” roles.

From consumer behavior research, real-world statistics, and my experience, it’s evident that consumers have a substantial attention span.

Retailers should focus on creating LTOs that are relevant to their target audience and that have a justification for the time limit

First of all wth is /26 or /24 mask? Then I realised I didn’t understand anything (or at least not deep enough) about subnetting