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Jev AI: The Decision Model That Makes Agents 20–200x Faster (and 40–400x Cheaper)
Jev (J-E-V), a new “system one” decision model released by Diogo Almeida at TypeSafe AI, claimed to be 20–200x faster and 40–400x cheaper than normal models, and shown reducing an agent session from nearly 1M tokens to 86K when used with Claude Code. Jev doesn’t write; it makes fast decisions—choice, score, and yes/no (“null”)—and returns probabilities so agents can automate above a confidence threshold and escalate below it, with many questions answered in parallel. Early public builds include categorizing 1,018 papers for $0.08, sorting 500 emails for $0.035, scoring 700 leads for $0.09, a browser agent finding flights in 7 seconds under $0.005, and rebuilding internal links across 586 pages in 45.1 seconds for $0.21. It highlights integrations for model routing and tool-use safety checks, discusses context trimming benefits and risks, and promotes the AI Profit Boardroom for deploying these workflows.
00:00 Jev Breakthrough Intro
00:58 What Jev Actually Is
01:26 System One Decisions
02:29 Three Question Types
03:33 Confidence And Batching
04:23 Early Builds And Results
05:59 Browser Agent Case Study
07:03 SEO Internal Linking Win
08:14 Using It Without Code
09:02 Routing And Safety Guardrails
10:55 Context Trimming Debate
12:14 Limits Pricing And Prompting
14:18 What Changed And Next Steps
15:25 Reality Check And Conclusion
Want to make money and save time with AI? Join here: https://www.skool.com/ai-profit-lab-7462/about
Get a FREE AI Automation Strategy Session: https://juliangoldieaiautomation.com/
Get a FREE AI SEO Strategy Session: https://go.juliangoldie.com/strategy-session?utm=julian
Jev AI: The Decision Model That Makes Agents 20–200x Faster (and 40–400x Cheaper)
Jev (J-E-V), a new “system one” decision model released by Diogo Almeida at TypeSafe AI, claimed to be 20–200x faster and 40–400x cheaper than normal models, and shown reducing an agent session from nearly 1M tokens to 86K when used with Claude Code. Jev doesn’t write; it makes fast decisions—choice, score, and yes/no (“null”)—and returns probabilities so agents can automate above a confidence threshold and escalate below it, with many questions answered in parallel. Early public builds include categorizing 1,018 papers for $0.08, sorting 500 emails for $0.035, scoring 700 leads for $0.09, a browser agent finding flights in 7 seconds under $0.005, and rebuilding internal links across 586 pages in 45.1 seconds for $0.21. It highlights integrations for model routing and tool-use safety checks, discusses context trimming benefits and risks, and promotes the AI Profit Boardroom for deploying these workflows.
00:00 Jev Breakthrough Intro
00:58 What Jev Actually Is
01:26 System One Decisions
02:29 Three Question Types
03:33 Confidence And Batching
04:23 Early Builds And Results
05:59 Browser Agent Case Study
07:03 SEO Internal Linking Win
08:14 Using It Without Code
09:02 Routing And Safety Guardrails
10:55 Context Trimming Debate
12:14 Limits Pricing And Prompting
14:18 What Changed And Next Steps
15:25 Reality Check And Conclusion
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