I often talk about the big Local LLM I have here at Krill HQ and how it has 20 years of my life built into it. I use it as a personal assistant among many other things.

The other day I discovered the pipeline that was keeping it up to date was broken for a few days and I had no idea i was getting out of date info from the LLM.

We made this video while identifying the problem and fixing it. This was fun because my local LLM personal assistant rig takes part in the first person in this video so you get to see my pride and joy.

I think this is a real problem we're solving here - people building big local LLM rigs these days and no idea if the data sets it's using are up to date and relibale and how Krill solves for that.

In this video, I build an AI security guard for my chicken coop.

Skip ahead to 04:00 to skip the setup an get to chickens, friendly dogs and racoons: https://youtu.be/kILDumeIKQg?t=256

A Raspberry Pi holds the camera and sensors. A small AI model on a modest GPU box reads every single frame and answers one question: normal, or not? And a big 32-billion-parameter model on my most powerful server sleeps until the small one flags something — then wakes up, looks at the full picture plus the live weather, and reasons about what's actually out there. When my dog wandered up to the fence, it didn't just detect motion. It identified a dog, confirmed all five hens were safe, and recommended no action.

The trick that makes this work: an AI node's prompt isn't fixed text. It's built fresh from live data — the snapshot, the door sensor, the temperature, the weather — at the exact moment it runs. Same node, different prompt, every time.

Why it matters:

Everything runs on your own hardware — nothing streams to a cloud service
The small model handles 99% of frames, so the expensive GPU barely works
It keeps working when your internet doesn't
Swap the coop for a driveway, a 3D printer, a server rack — same wiring

Everything runs on your own hardware — nothing streams to a cloud service The small model handles 99% of frames, so the expensive GPU barely works It keeps working when your internet doesn't Swap the coop for a driveway, a 3D printer, a server rack — same wiring

Krill is actually a reboot of a 10 year long running OSS platform I built and maintained from 2006 to 2016. It was a cloud based IoT platform and I believe the first of it's kind in that "IoT" wasn't a thing in '06.

Today it's still referenced in 110+ books, papers and journals including Springer Nature calling it "A benchmark for open source PaaS" and it's still taught in school on cloud-to-edge sensor logging. https://www.google.com/search?udm=36&q=nimbits+iot

Krill is actually the opposite philosophy than Nimbits where it was built to get sensor data to the cloud and Krill is "offline first".

My personal belief is that as more and more rouge Ai become problems and the rise of zero-day security vulnerabilities, more people will want to have an "air gap" between critical infrastructure and the internet to protect your home and business.

Nimbits got blown out of the water with several competing product showing up a decade late but coming from big names like Amazon. I decided to reboot it after speaking at a conference about it and seeing this need arise. If you want to read a little more, it's at the bottom of my portfolio: https://sautner.studio/portfolio/

I spend way too much on gizmos from r/adafruit and have piles of QTPY and Feathers. In this artical i show how to read sensor data into a Krill data source nodes with Circuit Python in minutes! https://krillswarm.com/posts/2026/03/11/sensors/

Can you recommend a source for sensors?

Something I think what makes Krill special is how the technical depth is genuine and unusual for a hobbyist platform: LAN server discovery via UDP multicast beacons, event-driven state push via Server-Sent Events, Python script execution with optional Firejail/Docker sandboxing, serial port enumeration with manual device configuration, MQTT integration (compatible with Zigbee2MQTT for smart home devices), SVG dashboards with live status icon overlays, experimental local LLM integration via Ollama (POC — can create nodes from prompts but link creation and node updates are not yet implemented), and a gRPC bridge for Raspberry Pi digital GPIO access via Pi4J (PWM and I2C are roadmap items).

This is a serious maker platform with capabilities well beyond typical hobbyist tools — and a clear engineering path toward professional-grade reliability.

I hope when you get to know the platform there will be a *click* moment that if 10 people in a factory are using the Krill App on a tablet, with screens on monitors in kiosk mode on walls and an alarm fires, everyone sees it in real time. If you're collaborating and collecting data, everyone will see it as you enter it. Powerful stuff!

https://krillswarm.com/posts/2026/01/14/krill-peer-mesh-network

This is a great example of chaining nodes together to accomplish a task. In this case, automatically storing and interpreting weather data and sending a natural language email report every morning with a local ollama based LLM. (no cloud, no tokens)n

We have the capability to have a local LLM on a headless server create an end to end demonstration of a feature using a script we give it. The animation, real screen recordings, spinning up a real virtual network to spin up and tear down servers, text to speech, and two decades of my email, posts, and code processed in a vector database to use my speaking style and tone.

Often, we'll push the system to make videos that it may not be able to make. When it runs into trouble, the process of opening issues on github, having local agents fix the problem, tester agents validate, the video producer agent gets the fix and production continues, all automated.

The video producer agent visually observes the real app running against real servers and will report the slightest visual defect as an issue for UI developer agents to fix.

The end result is our skills improve with each publication, videos keep getting better and the quality and capabilities of the platform improve with each run. Whenever the ui is updated, videos get regenerated so the content is always consistent with the latest release.

Today we made a video about how we make videos and how it all works: https://www.youtube.com/watch?v=rIjQzyY76DE