Jev dominates October's decision-model chatter: 32 of 60 tracked signals, and 39 are hands-on tests, benchmarks or builds

Asked (summary):

Within the world's leading AI-active enterprises, big startups and scale-ups — mainly Europe, the US, India and Israel — which interesting engineers, technologists and researchers publicly expressed interest in decision models (such as TypeSafe's Jev or Perplexity Decider) on LinkedIn, social media, technical blogs, GitHub or Hugging Face in the last month?

A broad live-web scan of October 1–30, 2026 surfaced 60 ranked public signals across 50 independent source hosts; 29 rows name an employer and 27 distinct organizations appear, including Amazon, IBM, Google, Red Hat, Thoughtworks, Vercel, LangChain, Adobe Research and Clalit Health Services. This is a discovery shortlist, not an enumeration of the "top 5,000 people" or all 25,000 enterprises — no such authoritative universe exists publicly.

Colour = signal depth from the row text: hands-on (tested, benchmarked, built or uses in a workflow) publication or endorsement · larger, labelled dots = named-organization targets · x = target fit, y = signal strength (ranking scores, 0–1)

The single strongest enterprise lead is Marc Brooker, Amazon Distinguished Engineer, who said decision models look like "a perfect decider for a workflow step" — priankr.substack.com

Production-grade evidence exists: nbh.co's head of AI engineering ran Jev on 184 real HubSpot enquiries, sorting them in 9 seconds for under a cent — nbh.co

IBM shows up twice (Volkmar Uhlig, CTO Data Platforms, and researcher Kaoutar El Maghraoui), the only organization with more than one named signal — youtube.com

No single source dominates: the largest of the 50 hosts contributes just 4 of 60 rows (6.7%), so the pattern is corroborated across independent outlets — digg.com

All 60 signals, ranked for outreach

#	Person	Organization	Role	Model	Public signal	Date	Fit	Strength	Source

Method: 60 public decision-model interest signals observed Oct 1–30, 2026 across LinkedIn-indexed posts, X-indexed reports, technical blogs, GitHub, Hugging Face, arXiv, Reddit, YouTube and press; 50 independent hosts, 27 named organizations (audit counts taken from the precomputed summary). Fit and strength are 0–1 ranking scores from the scan. "Hands-on" is a keyword match (tested, benchmarked, built, uses, evaluated) on the row's own signal text, not a verified classification. Names, roles, employers and locations are leads requiring human verification before outreach; missing organization or location is left blank, never inferred.

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