I tested TypeSafe AI’s Jev by building an app that judges startups.
6,232 YC company descriptions. One yes/no question. An interactive map of AI judgments.
See how I built YCJudged—and why a structured answer can still be wrong.
@typesafeai@arosplatforms
Watch: https://t.co/SnHXcSz7rk
In claims, the expensive mistake is not a slow decision. It is an unexplained one.
At Arosplatforms, we build claims triage agents for insurers. They read the first notice of loss, pull the policy and coverage terms, check the documentation for gaps, score complexity and fraud indicators, and route each file to the right adjuster queue.
We design one rule into every deployment: a human signs off on every payout. The agent prepares the file and the rationale. The adjuster makes the call.
That boundary is what lets claims, compliance and legal teams adopt the system with confidence. Every recommendation is explainable, every override is captured, and the overrides become training signal.
What we ask leaders to track: time from first notice to assignment, rework on misrouted files, adjuster time spent on documentation versus judgment, and the agreement rate between agent and adjuster.
Triage is where AI belongs. Judgment stays with your people.
#InsurTech #GenAI
Always-on agents are great until yours gets ambitious at 3am. Loving the @OpenAI dots launch, but I'm setting approval rules before bed tonight.
#AIAgents
@testingcatalog 3.46B active on a 78B MoE under Apache 2.0 is a really nice combo for anyone running local. Good to see more open weights out of Europe.
@GoogleDeepMind announced Gemini 4 Argon and the first people allowed in are cyber defenders. The velvet rope for a frontier model is now "are you on the blue team." Waiting politely in line with my API key.
@levie Matches what we see. Coding took off because the feedback loop is instant. Ops work moves just as fast once the agent has clear permissions and a human approving the big steps.
Ontario has Vector, world class universities and a ridiculous number of builders.
Imagine Ontario students learning on a public OI model they can take home and keep building on after graduation. A Waterloo co-op student tuning it for a Hamilton manufacturer. A Toronto grad turning it into a startup by spring. @ONgov@VectorInst
Budget caps are a feature, not a nag. Every agent we build at @Arosplatforms has a spend limit before it has a name. Glad to see @simonw making the case for hard caps by default.
Every founder's browser right now: 14 tabs of release notes from @OpenAI and @AnthropicAI, 3 tabs of benchmark charts, and 1 tab of actual work, untouched since Thursday.