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It started with a single, innocent question: does anyone actually read this?

I’d been running this Hugo blog for a while, writing posts about the homelab, the cluster, the occasionally catastrophic self-inflicted incidents. At some point the thought surfaced that it would be nice to know whether the words were reaching anyone beyond me and the search indexer. So I installed Plausible — privacy-respecting analytics, no cookies, one config line in Hugo — and moved on.

The Orange Pi 6 Plus sits in the rack, humming along with the quiet confidence of a board that knows it is being underutilised. Inside the CIX P1 SoC is a dedicated Neural Processing Unit (NPU)—a piece of silicon specifically etched to crunch tensors and accelerate inference. The hardware is real, the chip is powered, and the transistors are awake. But in the world of the Linux kernel, it is a ghost. There is no driver to wake it up, no sysfs entry to query it, and no user-space API to feed it data. It is a high-performance engine idling in a vacuum; the chip is awake, but nobody’s home.

700ms. That was the number that haunted my Kubernetes cluster, slowly burning it to the ground. Every alert the cluster generated, every log line it processed for AI-driven feedback, triggered an embedding operation. Each of these embeddings, we thought, took 700ms, saturating a CPU core, which in turn triggered more alerts, creating a truly spectacular, self-immolating feedback loop. The load average climbed to 153. It was a cluster fire, quite literally. Then, in the chaotic aftermath of patching the inferno and moving that “expensive” embedding workload to a humble 15-watt ARM board, something remarkable emerged: the warm latency was a mere 2ms. Even a cold start, including model loading, clocked in at around 100ms. The sobering discovery? The 700ms was never about the embedding operation itself. It was the embedding struggling under full CPU saturation, choked for headroom. On a quiet, dedicated machine with a warm model, the exact same task takes 2 milliseconds.

April 2026 — on the sentinel that decided to burn the house down

“Why can’t I see the new photos?”

That’s how the outage started. Not with a PagerDuty alert or a Grafana dashboard turning red, but with a casual question from my wife. I was already deep in the weeds debugging a glitch in Nextcloud Talk, but as I tried to refresh my own dashboard, the latency didn’t just spike—it vanished. Immich was gone. Mail was gone. The search index was a black hole.

April 2026 — on replacing an inbox full of irrelevant opportunities with a system that actually thinks

If you’ve worked in IT for more than a few years in Europe, you know the pattern. A recruiter reaches out. The message contains your name (sometimes), a job description (loosely relevant), and an offer (usually well below your rate). They’re matching on keywords. “Kubernetes” in your profile, “Kubernetes” in the job description — match. The fact that the role is junior, six timezones away, pays 40% less than your current work, and requires a technology you haven’t touched in three years is irrelevant. The keyword matched.

March 2026 — on why a €400 espresso machine can pull shots a €4000 machine can’t

The Gaggia Classic is a semi-automatic espresso machine with a straightforward design: a pump, a boiler, a solenoid valve, and a group head. It’s been in production in roughly the same form since 1991. It’s not a cheap machine, but it’s not expensive either — around €400 new, often found secondhand for less. It is, by design, simple.