My cluster — a single-node K3s instance running about 60 ArgoCD-managed applications — had been slowly dying for months. Pods crash-looped with “context deadline exceeded” errors. Leader elections failed. The metrics server stopped working entirely. I assumed it was resource pressure and tuned probe timeouts. The real problem was underneath all of it: K3s’s SQLite datastore had silently grown to 40 GB.
I have three Amazon Fire tablets mounted around the house running Fully Kiosk Browser as Home Assistant dashboards. They’re cheap ($35-50 each during sales), they have decent screens, and Fully Kiosk turns them into locked-down kiosks that show a dashboard and nothing else.
My music setup had accreted into a mess. Across two Kubernetes clusters I was running Navidrome, Lyrion Music Server (LMS), beets, and my own AI radio station (SUB/WAVE) - plus a Raspberry Pi running piCorePlayer wired into a Dayton DAX66 matrix amp for whole-home audio. Four things that all “played music,” none of which talked to each other, and no single place to control any of it from Home Assistant.
I started the day asking why one of my Frigate cameras was using more CPU than the others. I finished it having moved every LoadBalancer in my Kubernetes cluster onto BGP-routed addresses, because along the way I found a service that had been silently unreachable for two years.
A notification that says “person detected on doorbell” is useful. A notification that says “a delivery driver in a brown UPS uniform placed a medium-sized box on the porch and is walking back to the truck” is significantly more useful — especially when you’re in a meeting and trying to decide whether to get up.
My K3s cluster runs on six Intel NUCs. They’re great for the 50+ containers that make up my homelab — Plex, Home Assistant, Frigate, Paperless, the usual suspects. But they’re terrible at machine learning. Four Skylake cores and 32 GB of RAM per node doesn’t get you far when Immich wants to classify 80,000 photos or Frigate wants a vision model to describe who’s at your door.
Home automations are code that controls physical things. When they break, the consequences aren’t a 500 error — they’re a garage door that won’t close in a snowstorm, or window shades that open at 3 AM, or a tablet that charges to 100% and stays plugged in for months until the battery swells.
My Bird Buddy smart feeder takes a photo every time a bird visits. Over a few months, that adds up to thousands of images — all sitting in Bird Buddy’s cloud app with no good way to search, organize, or back them up. I wanted them in Immich, my self-hosted photo library, organized by species and properly timestamped.
My grandfather had about 10,000 35mm slides. I rented a SlideSnap X1 and spent a weekend feeding them through — 33 boxes worth, organized into folders by box. The scanner itself was great, but when you’re pushing through thousands of slides in a weekend, some inevitably go in upside down or backwards. No metadata, no EXIF orientation flags — just thousands of JPEGs, some right-side up, some not, sitting on a NAS.
CMS recently published provider-level Medicaid spending data from T-MSIS — every fee-for-service, managed care, and CHIP claim from 2018 through 2024, aggregated by billing provider, procedure code, and month. 227 million rows. $1.09 trillion in payments. I wanted to see what falls out when you run some basic fraud heuristics against it.