---
title: Metabolic Forensics
emoji: 🩸
colorFrom: red
colorTo: indigo
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false
license: mit
short_description: N-of-1 biosignal evidence engine. Forensics, not coaching.
tags:
  - build-small-hackathon
  - backyard-ai
---

# 🩸 Metabolic Forensics

**An N-of-1 biosignal *evidence engine* — forensics, not coaching.**

Point it at your own wearable history (CGM glucose, HRV, recovery, sleep RHR,
steps, morning alertness) and ask forensic questions:

- *"What reliably precedes my glucose spikes?"*
- *"What's different about my bad-recovery mornings?"*

The **system** does the hard part deterministically — temporal alignment,
event-window search, personal baselines, and **counterexample retrieval**
(does the pattern actually hold, or are there days it breaks?). A small local
model only **narrates** the evidence it's handed. It cannot invent findings.

Every answer follows the same honest shape:

> **observed association → counterexamples → one next experiment to run**

Never a diagnosis. This is associations in *your* data, not medical advice.

## Why this isn't "just prompting Gemma"

The model is a commodity ≤32B model. The moat is the evidence pipeline:
event detection, window alignment, baseline/variance estimation, and
counterexample mining. The LLM is handed structured evidence and narrates it —
it is structurally prevented from hallucinating a pattern that the data doesn't
support.

## Status

🚧 Skeleton running on **synthetic demo data**. The real engine + local model
(llama.cpp, off-grid) plug in at the marked points in `app.py`.

Built for the [Build Small Hackathon](https://huggingface.co/build-small-hackathon).