How a Music Festival Becomes One Big Network

BLE mesh, WiFi backbone, and a router that runs on a phone.

It’s the headliner. 80,000 people are in one field. Half of them just pulled out a phone to take the same photo. The cell network — designed for everyday density, not festival density — has fallen over for hours. Bars are gone. Texts are queued up to be sent next Tuesday. Calls fail on connect. Your group, the people you came with, are somewhere in this crowd, and you have no way to reach them.

Neuro-Logical: Ontology-Grounded Differential Diagnosis with LLM Integration

Jonathan / Loxation / March 2026

If you’ve spent time in clinical informatics, you’ve watched two waves of clinical decision support arrive with great promise and underdeliver for the same underlying reason.

The first wave — rule-based CDS — encoded expert knowledge as if-then logic over coded data. It worked when the data was clean, the rules were narrow, and the clinical context was constrained. It broke on free text, couldn’t scale to broad differential diagnosis, and required manual maintenance that no team could sustain across a SNOMED-scale terminology.

Implementing Neuro-Symbolic Reasoning on the Edge

How we built a fuzzy description logic reasoner in Rust, paired it with a local LLM, and shipped a neuro-symbolic stack that runs entirely on-device.

Neural networks are remarkably good at pattern recognition. They can classify images, transcribe speech, and generate plausible text. But ask a neural network why it arrived at a conclusion, and you get silence. Ask it to guarantee it won’t contradict a known medical fact, and you get a shrug. For applications where correctness, explainability, and domain knowledge matter — healthcare, child safety, trust scoring — pattern matching alone isn’t enough.