Feature
Signal Insights
Signal Insights v2 is Diorama's component-based insights experience for exploring behavior and health signals. It adapts detail sections by signal type so each page shows the most relevant analyses.
Why it matters
Different metrics need different views. Sleep stages, workout load, and habit completion do not become clearer through the same chart, so each detail page uses components suited to its data.
How Diorama uses this
Browse categorized signals from the Insights tab. Open detail views that dynamically show components like trends, averages, variability, distribution, and baseline deviation. Use streak and adherence-oriented sections for habit-focused signals.
What you can do
Insights v2 lets you inspect each signal with component blocks chosen for that signal's archetype and supported analyses. This means high-volume metrics can emphasize trend and variability, while habit-like metrics can emphasize consistency and completion behavior.
Signal detail pages combine visual sections and computed stats so you can move from "what happened" to "what pattern might matter" without leaving the app.
The same architecture also supports incremental expansion, so new insight components can be added without rebuilding the whole experience.
Notes and limitations
Component visibility depends on data quality and sample size, so not every signal page will look equally dense.
A few advanced metadata pathways are already modeled but not fully surfaced in production UX yet.
Limitations
Some components are hidden on sparse datasets, so depth can vary by signal. Event-focused signals still have fewer specialized surfaces than some continuous metrics. A few metadata-driven capabilities, such as deeper ai-context narration and event-frequency specialization, are still incomplete.
Diorama
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