x = lines added (log) · y = lines deleted (log) · one dot = one commit. The diagonal is pure churn; below it the codebase grows, above it the codebase prunes. Healthy projects live on both sides — deletion is progress too, and most dashboards hide it.

One shared time axis, two stacked strips: AI output tokens per day above the spine, lines changed per day below. The pair reads as a duet — the call (tokens) and the response (code), same days, separate scales, each labeled.

x = cache-read tokens per session (log) · y = output tokens (log) · one dot = one session. The never-charted metric: how much context a session held vs what it produced. The question a governance buyer should ask: is deep context leverage, or is it sprawl?

x = hour of day · y = calendar day · one bar = one session's actual span (start → end) · color = model. Duration becomes visible — marathon sessions read as long bars; the model mix reads as color down the page.

One shared log scale, mirrored at a waterline: output tokens above, cache-read context below. The transparency metaphor made literal — 266× more context is re-read than output is produced. What you see of AI work is the tip; visibility tooling exists because the mass is under the water.

x = day · y = net lines (adds − deletes), diverging around zero. Growth inhales above the line; pruning exhales below. The organism metaphor from the arcade thread, now carrying a real diverging metric — releases are marked as breath marks.

x = day · y = output tokens, stacked by model. The model portfolio as a score: who's playing, when, how loud. This is the "evaluated, discussed, efficiently used" claim as a picture — model choices are visible decisions, not defaults.

x = day · y = tokens per line changed (log) — plot 2 of the last page was the relationship; this is its price history. A falling rate means a token buys more shipped code. Ticker framing on purpose: this is the number a CFO will eventually ask for.