Share graph · provenance running forward
Priya Raman
Research engineer · inference · 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
Route through a gateway you control; do not standardise on a vendor's
Open-weight models for classification and extraction; frontier for agentic loops
Which model for structured extraction?
When does on-prem inference make sense?
Sovereign inference and the end of US default
Learning without weights: where continual learning actually lands
Cost-aware routing across three model gardens
Open-weight parity on our task evals
Confidential inference on a TEE for banking PII
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.