Open Source, Faster Jev is HERE
A walkthrough of "system one" classification models — fast, structured-decision models that output choices and probabilities instead of text. The host shows Jev (a closed, API-hosted model from TypeSafe AI, now open to everyone with no waitlist) alongside Leia, an open-weight local alternative released days later that runs on Apple Silicon. He demos Leia driving a browser agent to search Google Flights, plays Snake at ~30 decisions/second locally, and walks through Jev's playground where you define states, questions and criteria in JSON.
- System one models return structured decisions (yes/no, a choice, a score with probabilities) rather than generated text, making them suited to classification rather than conversation.
- The host runs Snake locally on Leia at roughly 30 decisions per second, sub-33ms per decision, with the snake reaching a length over 140 without failing.
- Early community demos of Jev include playing Super Mario Bros in real time, ranking ~700 sales leads for intent and outreach messaging, and booking a flight via browser-use in about 7 seconds for roughly $0.0039.
- Leia is open source with open weights and runs locally on M1/M2/M3/M4 Macs; the host describes it as faster than Jev but currently less accurate, being a first version.
- He took Gregor from Browserbase's open-source "Jev ultra fast" demo and had Claude Code swap Jev for Leia — it worked in essentially one session, giving a free, API-free version of the browser agent.
- The system one / system two framing comes from Kahneman's Thinking, Fast and Slow (2011): fast instinctive judgments versus slow deliberative reasoning, which is what current LLMs do — and where they still fail at things like counting the R's in "strawberry".
- In Jev's playground, you supply a state (e.g. food = hamburger) plus questions with explicit criteria; the sandwich example returns 94% true for hamburger and 94% false for avocado toast.
- On a comparable email-triage task, Leia returned department, urgency, churn risk and language in 39ms versus Jev's 93ms — about three times faster.
A concrete cost-and-latency result that shows why fast decision models change what browser agents can do.
The guy behind browser use used browser use plus Jev to have this super ultra fast crazy cheap. So this entire booking took 7 seconds and it only cost like 0.0039 to book a flight.
Side-by-side numbers — 39ms local and open-weight versus 93ms hosted — are the core claim of the video.
It's telling you it's billing, confidence 94%, the language is English... and the amazing part about it, it did all of that in 39 milliseconds. If you compare it to Jev's timing, it's like three times faster.
They say they took 3 years in stealth mode developing this engine and the decision model before they released it, and people just cloned it literally in like 5 days or a week.
You give it the issue with the different classes and it gives you a decision in sub milliseconds.
Even LLMs are failing when you say count the number of R's in the word strawberry.