# Drawing Telemetry

Source: https://www.potatoannotator.com/docs/measurement/drawing-telemetry

**Pre-labelling makes annotators faster, and it makes rubber-stamping frictionless.** A suggestion appears, the annotator clicks accept, and the resulting dataset is a record of a model agreeing with itself. Nothing in the annotation distinguishes that from careful review. The geometry is identical, and every quality measure looks *better*, inter-annotator agreement included.

The only place the difference shows up is in the timing.

> A human cannot inspect a mask boundary and decide in 300 ms. Once, maybe, if the object was obvious. As a **median across many items**, it is not fast expertise.

As one-click auto-labelling becomes the norm, this is the signal that separates review from rubber-stamping. It is the most under-appreciated capability in the 2.8 release.

## The event stream

An event carries a timestamp, an action, a geometry kind, and one integer.

| Action | Meaning | The integer |
|---|---|---|
| `shape_add` | A shape was committed | Vertices: 4 for a box, `len(points)` for a polygon, 0 for a mask |
| `shape_edit` | An existing shape was moved or reshaped | — |
| `shape_remove` | A shape was deleted | — |
| `stroke` | One brush or eraser stroke finished | Stroke length in **image** pixels |
| `fill` | Flood fill applied | Pixels filled |
| `zoom` | Zoom level changed | Level × 100 |
| `pan` | One pan drag finished | Distance in screen pixels |
| `tool`, `undo`, `redo` | | — |
| `ai_suggest` | A suggestion was rendered to the annotator | — |
| `ai_accept` / `ai_reject` | | — |

**No coordinates are recorded, ever.** A stream reconstructs how an annotation was made; it cannot reconstruct the annotation. That is a structural property of the event record rather than a policy. There is no field a coordinate could go in, and a test asserts it.

Stroke length is in image pixels rather than screen pixels, so the same stroke measures the same whether the annotator was zoomed in or out.

## Configuration

```yaml
annotation_telemetry:
  enabled: true
  fidelity: events           # off | summary | events
  store_events: true
  include_schemas: []        # empty means every geometry schema
  exclude_schemas: []
  idle_ms: 120000            # a gap above this is idle, not active
  flush_interval_ms: 10000
```

Runnable example: [`examples/advanced/annotation-telemetry/`](https://github.com/davidjurgens/potato/tree/main/examples/advanced/annotation-telemetry).

## The signal that changes decisions

**AI-accept latency.** A run of sub-500 ms accepts across many items is rubber-stamping, and no other figure separates that from careful agreement.

Read it as a distribution, not as a threshold. One fast accept is an easy object; a median of 300 ms across two hundred items is a different finding, and it is one you cannot reach from the annotations themselves.

## Limits of the signal

This reports **process signals**. It does not detect fraud, and it does not classify an annotator. A flag is a prompt to look, not a finding, and the base rate matters: in a corpus where most items really are easy, fast is often correct.

The text-side twin is [keystroke logging](/docs/features/keystroke-logging), which reports how a free-text answer was produced. Together they are the honest answer to "how do you know your crowdworkers are not pasting from an LLM" — and the honest answer is a set of process signals with per-project calibration, not a classifier.

Annotators see a recording notice. That is the difference between a research instrument and surveillance, and the disclosure defaults to on.

## Related

- [Keystroke logging](/docs/features/keystroke-logging)
- [Keystroke logging ethics](/docs/features/keystroke-logging-ethics)
- [Guide: detecting rubber-stamped pre-labels](/docs/guides/detecting-rubber-stamped-prelabels)
- [Source documentation](https://github.com/davidjurgens/potato/blob/main/docs/advanced/annotation_telemetry.md)