---
schema: 1
type: term
title: '**quantified self**'
description: >-
  **Quantified self** is the practice of tracking personal data about your own
  body and behavior — such as sleep, stress, biomarkers, moods, and daily
  performance — and using that data to understand and adjust your own habits. In
  this channel's material, the term appears in the context of feeding such
  tracked data into AI tools, where it can be analyzed for patterns and used to
  predict or improve outcomes (for example, which variables affect next-day
  meditation depth or symptom flare-ups).
canonical: >-
  https://liquidgold.cc/channel/everyinc/explore/term/quantified-self-4dea2d97ad33
markdown: >-
  https://liquidgold.cc/channel/everyinc/explore/term/quantified-self-4dea2d97ad33.md
llms: null
parent: 'https://liquidgold.cc/channel/everyinc/llms.txt'
channel: everyinc
updated: '2026-10-23T22:12:35.346Z'
language: null
children: []
---


# \*\*quantified self\*\*

Canonical: https://liquidgold.cc/channel/everyinc/explore/term/quantified-self-4dea2d97ad33

Parent: [Channel index](https://liquidgold.cc/channel/everyinc/llms.txt)

## Definition

**Quantified self** is the practice of tracking personal data about your own body and behavior — such as sleep, stress, biomarkers, moods, and daily performance — and using that data to understand and adjust your own habits. In this channel's material, the term appears in the context of feeding such tracked data into AI tools, where it can be analyzed for patterns and used to predict or improve outcomes (for example, which variables affect next-day meditation depth or symptom flare-ups).

## What the term covers hereIn this channel's material, **quantified self** means the practice of tracking data about your own body and behavior — sleep, stress, biomarkers, moods, meeting performance — and putting that data to work. The framing that keeps recurring: AI makes this data *finally useful*. Data that used to sit around as nerd souvenirs can now be thrown into a model and interrogated — *how does X affect Y, what do you notice?*## How it's used in practice- **Jonny Miller** tracks the variables that shape his odds of reaching deep meditative states (jhanas) the next day: sleep over 8.5–9 hours, a low-stress prior day, meaningful practice access the prior week.- **Dan** uses quantified-self data to debrief meetings, and built an OCD-tracking app that logs daily Y-BOCS scores plus video through an emotion-embedding API, hoping to predict symptom flare-ups a day in advance.- In the **Codex Vitae** setup, quantified-self signals — biomarkers, prior experiment results, burnout warning signs — sit inside the context document an AI coach reads, so its advice reflects your actual patterns.- Reid Hoffman's conversations on preparing for AGI connect quantified-self tools to the broader question of data rights and what individuals control about their own data as AI spreads.The channel's argument is that **quantified self plus AI equals prediction**: once a model holds your tracked variables, it can connect them — e.g., predicting which conditions lead to better next-day outcomes — and a *centaur* coaching setup (client, AI coach, human coach) can act on that picture weeklong, not just in a single session.- **Quantified self** = systematically tracking personal data (sleep, stress, biomarkers, mood) and using it.- AI turns that data from a souvenir into a tool: ask the model *how does X affect Y?*- Real uses in the material: predicting next-day jhana access, debriefing meetings, OCD flare-up prediction.- Tracked variables feed the **Codex Vitae** context document that makes an AI coach genuinely personal.