Share graph · provenance running forward
Claire Dubois
Sector owner · Banking · firm
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
Prompt caching: use for stable prefixes over 2k tokens; expect 30–45%, not 60%
Every LLM judge ships with a human agreement score or does not ship
Distil to a small model only after the frontier baseline is measured on the same eval
Agents act under delegated, scoped, expiring authority — never a service account
Measure ROI from telemetry and cycle time, not surveys
Give citizen developers a paved road and a retention policy, not a review board
What does inference actually cost right now?
Retrieval or fine-tuning for this?
What eval tooling do we use?
When does on-prem inference make sense?
When do we need to move to post-quantum crypto?
Sovereign inference and the end of US default
Learning without weights: where continual learning actually lands
Token cost ledger across six client patterns
Judge calibration against human panel
Release canary suite v2
Edge SLM for in-store classification
Nightingale measurement run: coding-assistant ROI
Delegated-authority broker for agent tool calls
Confidential inference on a TEE for banking PII
Temporal decision memory for a claims agent
Non-weight-bound learning via retrieval-updated skills
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.