272
total events
115
actions taken
2
surfaces visited
15m 8s
observed span

## ◉01 / ACTION — RESPONSE ENVELOPE

For every action you took — a like, a comment, a message, a share — Reflex averages the activity density in the 30 requests before and 30 requests after the moment you tapped. The shape of the resulting curve is the algorithm's reply, averaged into visibility. A spike on the right means the system reacted. A flat line means it didn't — or at least not in a way your own requests could see.
◈ OBSERVATION

Around your 115 actions, the density of passive view events in the 10 requests immediately after the action held roughly flat (-2% vs the 10 requests before).

Translation: after you tap, the app served you about the same amount of content to scroll past.

## ◉02 / SURFACE TRANSITION MATRIX

The probability that going to any surface next given your current surface. Rows are your current location, columns are where you go next. A strong diagonal means you tend to stay where you are — deep scrolling. Bright off-diagonal cells reveal your habitual paths: the routes your attention takes without you ever consciously choosing.
Not enough distinct surfaces

## ◉03 / SESSION RHYTHM

Activity density over time, bucketed by minute, stacked by category. The pink dots mark burst moments — buckets where your activity exceeded the session's baseline mean by more than 2 standard deviations. These are the moments you were really in it. Autocorrelation underneath reveals whether your session breathes at a regular cadence.
◈ OBSERVATION

Detected 0 burst moments where activity spiked above baseline.

No clear periodic rhythm — your session is arrhythmic, driven by external triggers rather than internal cycles.

## ◉04 / HOOK MOMENTS

For each action you took, Reflex looks at the 5 events immediately before and measures which categories appear more often than their session-wide baseline. Lift values above 1.0× mean that category disproportionately precedes your taps — it's what gets you to act. This is the hook, empirically measured on you, by you.
◈ OBSERVATION

Events with category act are 1.32× more likely to appear in the 5 requests immediately before you take an action, compared to baseline.

In other words: when you see act content, you're measurably more likely to respond.

## ◉05 / DIURNAL PORTRAIT

Your activity wrapped around a 24-hour clock, with each hour's petals colored by activity category. Length of a petal = total events in that hour. This is your temporal signature: the hours you belong to yourself, and the hours you belong to the feed.
◈ OBSERVATION

Peak activity at 14:00 UTC — 94% of all observed events fall in that single hour.

This wheel is a clock. It tells you when you are most available to the feed.

## ◉06 / PREDICTION ENGINE

Reflex splits your session 80/20, trains a Markov model on the earlier chunk, and uses it to predict each next event in the later chunk. The accuracy bars on the left show how well that model performs against a naive baseline of always guessing your most-visited surface. The crystal ball on the right applies the fully-trained model to your very last event and asks: if you kept going right now, what would you probably do?
◈ OBSERVATION

A simple Markov model trained on the first 80% of your session predicts the next surface with 100.0% accuracy on the held-out 20% — 1.00× better than guessing the most common surface.

From where you left off, the most likely next move is HOME FEED (99% chance).

The mirror reflects. The mirror predicts.