👁️ Vergance

Look at it. Say what you want. Claude knows what "this" means.

Phases 0–4 done · Phase 5 underway

the user looked at cta-primary for 620 ms
while saying "make this bigger"

What it is

Gaze + voice as a multimodal input layer. The camera watches your eyes and mouth. You look at a UI element, say a command, and Vergance emits a small, semantic event that an agent like Claude can act on.

The point is deixis — the pointing part of language. "Make this bigger" is the most natural way to ask for a change and the least useful thing to type, because the word this carries no information without a gesture. Gaze supplies the gesture. Voice alone can't.

Raw camera frames never leave the device — only semantic events do.

Two surfaces, one capture layer

🎯 Live pointer Gaze + voice → a resolved intent event, in real time. The flagship interaction. Today those events land in the app; handing them to an agent, as a Claude Code skill (/vergance) that streams gaze-resolved intent into your session, is Phase 6.
🔥 Post-hoc heatmap Record a session and analyse where attention actually went — the UX-research use case, from the same capture pipeline. Phase 8: the session_summary event type exists, the aggregation and the viz don't.

The honest accuracy bar

Eye tracking attracts overclaiming, so this is stated up front:

Webcam (v1) — region-level. A 2×2 quadrant split is reliable. 3×3 works with a still head and good light. Enough to drive the interaction, not enough to distinguish adjacent buttons.
iPhone TrueDepth (v2) — materially better. Real gaze vectors plus depth.

What's in the core

Sensor-agnostic core Every sensor collapses to the same GazeSample, so backends are interchangeable by design. One exists today — the macOS webcam; the iPhone is Phase 7.
Calibration Nine-point red-dot routine, quadratic least-squares with ridge regularization, reporting RMS error in pixels so the agent knows how far to trust a spatial claim.
One Euro filter Adaptive smoothing — low latency on saccades, heavy smoothing on fixations — instead of a fixed EMA.
Fixation detection A dwell / dispersion detector that turns a raw gaze stream into discrete fixations.
Element resolution A fixation resolves to a named element on Vergance's own canvas today; browser-DOM and Accessibility-API surfaces are staged after it.
Portable event schema Debounced, element-resolved Codable events (session_start, fixation, utterance, session_summary) rather than raw 60 Hz samples. These are the types the Phase 6 hand-off will carry; today they stay in-process.
Drift handling A large head-pose delta from the calibration baseline prompts a recalibration.

Explicit non-goals

Silent lipreading. Visual speech recognition is unreliable. Vergance uses audio for words and lips for timing — mouth openness as voice-activity detection and emphasis.
Pixel-precise gaze. Region- and element-level is the target.
Cloud video. On-device only.

Status

Phases 0–4 done and Phase 5(a) validated on-device — 48 green tests and a working macOS app; the agent hand-off (Phase 6), the iPhone sensor (Phase 7) and the heatmap (Phase 8) are still ahead. ROADMAP.md is the source of truth — it holds the full spec, architecture, event schema, and the phased plan. Swift 5.9, macOS 14+ / iOS 17+, Apache 2.0.

FAQ

Can I use it today?

Not as a finished product. The sensor-agnostic core, calibration, filtering, fixation detection, voice fusion and element resolution on the app's own canvas are in place; the agent hand-off, the iPhone sensor and the heatmap are on the roadmap. It's Apache 2.0, so you can read it, build on it, and contribute now.

Does any video leave my machine?

No. Frames are processed on-device and discarded. Only the small semantic events are emitted — that's the whole design, not a setting you have to find.

Why gaze instead of a mouse?

You're already looking at the thing you're talking about. A mouse makes you say it twice — once with your eyes, once with your hand — and neither survives into the prompt you eventually type.

Does it need an iPhone?

No. A webcam works today at region-level accuracy; the iPhone's TrueDepth camera is the v2 path to real gaze vectors and depth. Because everything collapses to one GazeSample, adding the phone later doesn't mean rewriting anything.