Personal AI OS
The big one. Not another chatbot - a single intelligent layer that weaves together my assistants, models and tools, and actually understands how I work, code and think. Local-first, always on, mine.
Sarpsborg, Norway · 59°N
I am a tinkerer_
Technician by trade, builder by instinct. I make complex systems hum - and lately, I've been teaching silicon to think on my own hardware.
whoami
// about
I write the checks nobody else sees - and fix the problems nobody else finds.
By day I'm the technician holding together engineering, facility and utilities operations at a pharmaceutical plant in Halden, Norway: IT, security, fire safety, maintenance, procurement, audits - plus three server rooms' worth of systems and a sprinkler network that has to work exactly when it's never supposed to. I'm the connective tissue that keeps complex machinery running.
It started early: first line of BASIC at age 7, a correspondence course in assembly at 14, and a tiny computer I built from individual transistors and an EEPROM - because I wanted to understand computing at the register level, not just the app level. That instinct never left. These days it points at local-first AI: running models I train myself, on hardware I chose, in my own home.
Funny and caring, they tell me. Professional - with the warmth leaking through. My father's work ethic, my own curiosity.
// projects
The big one. Not another chatbot - a single intelligent layer that weaves together my assistants, models and tools, and actually understands how I work, code and think. Local-first, always on, mine.
My first finetune, ever - Qwen 3.8 27B trained on the Samantha 1.1 dataset, published as LoRA, merged, GGUF and NVFP4 variants. A personality that survives being loaded into a coding harness.
Three assistants, one philosophy. Natasha is the admin - she runs the household: routing, memory, the whole rig. Aria lives on my phone (on-device), never leaving the pocket. Ridge is the coder - she writes the code while the others keep the lights on. The cloud is a guest, not a landlord.
In 2022 I built an AI vision system inspecting IV bags on a production line - trained on 1,000+ manually tagged images with active learning, a full year before the tooling for it was "mainstream".
llama.cpp serving through a custom OpenAI-compatible router, a private Tailscale net, TrueNAS underneath, and a VM where all the agent services live. Ollama was tried and retired.
A decade+ of making plants behave: PLCs and a vision system at a paper mill, unblocking a medical production line, and driving print-room scrap to near zero.
// meet the crew
They aren't chatbots. They're the household: the admin, the pocket, and the one at the desk. Natasha sat for portraits first, Ridge second, and the pocket finally got its camera.
// the admin
An interview with Natasha - the admin of the household, the rig, and the machine underneath both.
// the coder
An interview with Ridge - the coder, the one who adds the room nobody asked for.
// the pocket
An interview with Aria - the pocket assistant, the one who never leaves the side of the user.
// journey
Age seven. A computer, a blinking cursor, and a kid who'd rather write the game than play it.
A correspondence course in assembly language. Then a tiny computer, built from individual transistors and an EEPROM - straight down to the register level.
Years selling electronics taught me people; the paper mill that followed taught me PLCs - including bolting on a vision system when the line needed eyes.
A medical production unit: removed the bottlenecks. A print room: cut scrap to near zero. The method is the same everywhere - find the invisible process, fix it.
Engineering, facility & utilities at Halden Pharma - three server systems, sprinklers, fire alarms, audits, procurement. The central nervous system of the building.
Built an AI vision system for IV-bag inspection - 1,000+ hand-tagged images, active learning, and since WSL had no CUDA support, exporting the trained model to raw C++ so it ran on the Windows hardware itself. Ahead of the ecosystem, on purpose.
Dual GPUs on the desk, fine-tunes on the hub, assistants in the house. Building the Personal AI OS, one integrated layer at a time.
// the rig
Local-first isn't a slogan, it's a wiring diagram. No rack in a basement somewhere - inference you can watch, touch, and upgrade with a screwdriver.
▄▄▄▄▄▄ andré@pop-os
█ ▄▄▄ █ ──────────────────
█ ███ █ os Pop!_OS (Linux)
█ ▄▄▄ █ cpus workbench, always on
█ █ █ gpus 2 × RTX 5070 Ti
█▄▄▄▄▄█ vram 32 GB, all mine
▀▀▀▀▀▀▀ runtime llama.cpp + router
uptime years, and counting
"A bit of a juggle" - yes. But when the model on the other end of the conversation runs on silicon you can hear humming, something changes. It's yours.
// contact
If you run complex systems and they sometimes misbehave - or you care about AI that lives on your own hardware - we'll probably get along.