Share graph · provenance running forward
Jun Park
Measurement (Nightingale) · lab
What they dropped, which clusters it landed in, what got elected, and which experiments and findings descended from it. Everyone’s is visible to everyone, symmetrically. Show outcomes, not counts — no totals, no rankings, no rollups.
Dropped
signals with this person attached
Landed in
distinct clusters
Elected
of those, now fields
Descended
experiments and published items
Drops
What descended
Measure ROI from telemetry and cycle time, not surveys
When does on-prem inference make sense?
Learning without weights: where continual learning actually lands
Nightingale measurement run: coding-assistant ROI
Survey run: nearest adjacency into pharma
Non-weight-bound learning via retrieval-updated skills
Sensing agent over store telemetry
Follow this person’s finds
Following someone whose drops are consistently good is the internal version of the external voice watchlist, and often a better source than any detector.